Validate isolated author LIF semantics without changing frozen models
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Digital Drosophila Archive
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# 当前研究状态 — Phase 4AC
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# 当前研究状态 — Phase 4AD
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最新结果：**COMPLETE-SEMANTICS-AUDIT / LEARNING-NOT-ACHIEVED**。
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学习可用的总目标尚未达到，不把审计 PASS 当成学习 PASS。
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初始化 README/registry 保留原字节，最新结论从本页进入。
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已按用户授权建立独立分支 `codex/lif-author-semantics-lab`。
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正式状态：**COMPLETE-ORACLE-VALIDATED-LAB-ONLY**。
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学习仍未达到可用标准；不替换任何旧候选，不把实现检查PASS当作学习PASS。
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- [中文机制结论及限制](docs/progress/phase4ac-interpretation.md)
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- [完整小回路/单细胞结果](docs/progress/phase4ac-lif-semantics.md)
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- [预先声明的诊断方案](models/male-cns-lif/lif-semantics-audit-v1/PROTOCOL.md)
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- [新阶段校验清单](registry/phase4ac-lif-semantics/seal-verification.json)
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- [上一轮末梢作用范围诊断](docs/progress/phase4ab-terminal-scope.md)
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- [小回路与全脑背景的只读复核](docs/progress/feedback-context-review-20260930.md)
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- [关键结果、限制与下一步](docs/progress/phase4ad-interpretation.md)
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- [完整实验报告](docs/progress/phase4ad-author-semantics.md)
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- [实验预声明](models/male-cns-lif/author-semantics-lab-v1/PROTOCOL.md)
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- [新产物封存核验](registry/phase4ad-author-semantics/seal-verification.json)
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- [此前全脑输入背景复核](docs/progress/feedback-context-review-20260930.md)
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## 后续只读复核的重要限制
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## 关键发现
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所选五节点只贡献三个 ALLN 晚期正到达权重的 7.57%–8.99%；达到各自80%正输入需要49–52个来源。
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因此孤立小回路可以自持，不意味着它是全脑唯一原因。回路外输入也可能受该回路反馈影响，不是已证明独立的外源驱动。
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对旧数据的派生分解没有新模拟或训练；正输入比例不能当因果责任比例。
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独立新模型分支尚待用户确认，不把持续目标自动当作改变冻结动力学的许可。
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固定作者代码在Brian2 2.5.1实际运行。16项单元检查、10组真实回路/精度对照及其重复通过；58/58审计项通过。
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恢复作者完整动力学后，五节点真实回路1秒放电从1,483降到610，但后500ms仍有306次。
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因此不能认为“修g reset/不应期就解决学习问题”；持续循环仍存在，低活跃度不是晋级条件。
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## 新增机制证据
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下一步先做独立GPU实现的高入度/累加顺序与短时全图对照，再检验固定DoOR嗅觉输入是否保留区别；尚未开始conditioning。
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输入请求、不应期拒绝和实际放电必须分开统计。后续学习必须执行真实无调制/冻结/随机奖励对照和等待期，而非复用标签。
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只施加一次初始 spike，使用原有冻结动力学：
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## 冻结与服务
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| 真实小回路 | 1 s 内总 spike | 500–1,000 ms spike |
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|---|---:|---:|
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| 2 个 ALLN | 473 | 238 |
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| 3 个 ALLN | 1,064 | 535 |
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| 3 ALLN + VA6 PN + KC | 1,483 | 747 |
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| 真实前向边对照 | 3 | 0 |
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| 无初始刺激 | 0 | 0 |
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原230,239项以及4AA/4AB/4AC与背景复核封存文件前后校验不变。
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旧deterministic-v2五组全脑参考重新运行，事件、计数、最终电压和g精确一致。
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base CSR、raw counts、W_syn、transmitter signs、APL参数、eligibility-v2、14,232-edge mask未修改。
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只在独立实验环境安装作者版本依赖；未停用或修改ComfyUI。
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没有游戏、RL、decoder、视觉输入或权重训练，没有生成新的learned candidate。
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最小回路：body 11478 → 11620（235 contacts），11620 → 11478（283 contacts），两者为 lLN1_bc / ACh。
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单次输入足以在隔离小回路中产生持续到观察期末的活动；不代表已证明全脑必要性，也不代表真实果蝇该回路必然如此。
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## 原作者实现与本项目适配差异
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固定作者代码在 spike 后清零 g，对不应期内 g 更新/输入设限，并有不同积分、阈值和刺激时序。
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本项目冻结版本明确保留 g，继续衰减/接收输入。这是建模语义差异，不是本轮发现的 GPU 回归错误。
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单细胞组件测试证明这些选择会改变响应，但真实连接强度的一次输入下，reset-only 并未改变 spike 数。
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因此 **g reset 不得直接称为学习修复**，也不能仅据放电减少自动选择新模型。
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## 验证与下一步
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7 项单元测试、17×4 单细胞 fixture、5 个真实小回路 CPU/GPU 全状态/事件精确对照通过。
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五组 deterministic-v2 全脑冻结参考 trial 精确一致。
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原快照 230,239 项及 Phase 4AA / 4AB 模型与实验前后校验无变化。
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新产物有独立 manifest/SHA-256；未训练、未生成 learned candidate。
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ComfyUI 保持驻留，不停用、不卸载、不升级依赖。
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后续需要明确授权的独立动力学模型分支：先验证原作者 oracle 和局部机制，再检查输入区别与学习功能。
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仍要求独立 evaluation seeds、A 对 B 的特异性、MBON/downstream readout 和完整 conditioning controls。
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不调整学习率/阈值或扩大 mask，不进入游戏、RL、decoder、视觉；不修改已冻结版本。
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Git 仅同步代码、小报告、官方小型源代码及校验清单。大数据/完整 spike 输出仍在服务器，不等于完整异机备份。
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代码、小报告、来源和校验清单进入Git；大数据及完整实验轨迹保留服务器，不等于完整异机备份。
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# Phase 4AD 作者动力学语义实验室
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状态：`COMPLETE-ORACLE-VALIDATED-LAB-ONLY`。
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本轮仅有预声明的小回路与算子实验；没有整脑替代动力学运行、conditioning、学习成功或新学习候选。
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检查以原始事件、状态轨迹和预注册误差界限重算。缺失、失败、非有限值或初始事件丢失均不能通过；放电减少或静默本身不构成成功。
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核查通过 58/58 项；失败或不完整 0 项。
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冻结历史完整性：通过。
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源码与输入预注册一致性：通过。
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| 回路 | 语义 | 精度 | 总spikes | late spikes |
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|---|---|---|---:|---:|
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| pair | frozen | float32 | 473 | 238 |
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| pair | reset_g_only | float32 | 223 | 111 |
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| pair | refractory_gate_only | float32 | 635 | 321 |
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| pair | reset_and_refractory | float32 | 223 | 111 |
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| trio | frozen | float32 | 1064 | 535 |
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| trio | reset_g_only | float32 | 565 | 283 |
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| trio | refractory_gate_only | float32 | 1100 | 552 |
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| trio | reset_and_refractory | float32 | 469 | 235 |
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| five | frozen | float32 | 1483 | 747 |
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| five | reset_g_only | float32 | 868 | 433 |
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| five | refractory_gate_only | float32 | 1510 | 763 |
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| five | reset_and_refractory | float32 | 634 | 318 |
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| feedforward | frozen | float32 | 3 | 0 |
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| feedforward | reset_g_only | float32 | 3 | 0 |
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| feedforward | refractory_gate_only | float32 | 3 | 0 |
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| feedforward | reset_and_refractory | float32 | 3 | 0 |
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| no_input | frozen | float32 | 0 | 0 |
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| no_input | reset_g_only | float32 | 0 | 0 |
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| no_input | refractory_gate_only | float32 | 0 | 0 |
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| no_input | reset_and_refractory | float32 | 0 | 0 |
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| pair | author_complete | float64 | 213 | 106 |
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| pair | author_complete | float32 | 213 | 106 |
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| trio | author_complete | float64 | 448 | 224 |
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| trio | author_complete | float32 | 448 | 224 |
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| five | author_complete | float64 | 610 | 306 |
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| five | author_complete | float32 | 610 | 306 |
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| feedforward | author_complete | float64 | 3 | 0 |
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| feedforward | author_complete | float32 | 3 | 0 |
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| no_input | author_complete | float64 | 0 | 0 |
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| no_input | author_complete | float32 | 0 | 0 |
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作者Brian2与独立NumPy比较：
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| 实验 | 结果 | 事件一致 | 最大V/g误差 mV | 首次事件分歧step |
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|---|---|---|---:|---:|
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| author:pair:float64 | 通过 | True | 7.105427357601002e-14 | None |
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| author:pair:float32 | 通过 | True | 2.41696834564209e-05 | None |
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| author:trio:float64 | 通过 | True | 8.526512829121202e-14 | None |
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| author:trio:float32 | 通过 | True | 4.25875186920166e-05 | None |
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| author:five:float64 | 通过 | True | 3.836930773104541e-13 | None |
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| author:five:float32 | 通过 | True | 0.0002123713493347168 | None |
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| author:feedforward:float64 | 通过 | True | 3.765876499528531e-13 | None |
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| author:feedforward:float32 | 通过 | True | 0.00020179152488708496 | None |
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| author:no_input:float64 | 通过 | True | 7.105427357601002e-15 | None |
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| author:no_input:float32 | 通过 | True | 1.0728836059570312e-06 | None |
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float32与float64交叉比较仅记录精度影响，允许保留分歧，不能用于掩盖各自与独立oracle的失败。
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- precision_cross_record:pair：事件一致=True，最大误差=3.733467101341148e-05 mV，首次事件分歧step=None。
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- precision_cross_record:trio：事件一致=True，最大误差=3.733467101341148e-05 mV，首次事件分歧step=None。
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- precision_cross_record:five：事件一致=True，最大误差=0.00019546693161487383 mV，首次事件分歧step=None。
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- precision_cross_record:feedforward：事件一致=True，最大误差=0.0005722045887850413 mV，首次事件分歧step=None。
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- precision_cross_record:no_input：事件一致=True，最大误差=0.0 mV，首次事件分歧step=None。
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所有失败轨迹和错误文件原样保留；本简报没有选择获胜变体。
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小回路缺少外部整脑输入与graded APL，不能证明整脑因果必要性、生物真实性或信息保持。初始诊断seed也不是作者的感官Poisson接口。
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环境为Linux宿主，作者记录为Windows；Python补丁版本与环境差异见environment记录，不能称为二进制相同或原论文整脑复现。
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后续整脑嗅觉比较仍须核查固定输入、实际送达事件、持续活动和信息保持；可用学习仍需至少5个训练seed、独立评估、MBON/downstream的A对中性B特异性及acquisition/reversal/retention/delayed-reward对照。
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封存排除 .venv 与 __pycache__，保留环境安装报告；manifest列出payload哈希，checksums另含manifest，不存在自哈希循环。
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# 独立模型分支首轮结果：实现正确，不等于学习修复
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用户明确授权独立新模型分支测试动力学假设。分支为 `codex/lif-author-semantics-lab`，
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模型目录 `models/male-cns-lif/author-semantics-lab-v1/`，实验目录 `experiments/phase4ad-author-semantics/`。
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Phase 1–4AC 保持原样；没有替换已有候选、训练、调学习率、扩大mask或连接游戏。
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## 已验证的结果
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正式状态 `COMPLETE-ORACLE-VALIDATED-LAB-ONLY`，58/58审计项通过。
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实际运行固定作者代码的 `create_model`，并与独立解析积分实现对照，而非仅比较两份自写程序。
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8项算子/边界fixture各测试float64与float32；5个固定真实motif各测试两种精度，分别重复。
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各精度内的spike事件、计数和不应期逐步一致，重复运行逐数组精确一致。
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五个motif的float32与float64事件也一致，但不能外推到全脑高入度累加。
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float64最大V/g差约3.84e-13 mV；float32独立实现相对Brian2最大差约2.12e-4 mV。
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| 真实回路 | 旧冻结模型1秒总spike | 完整作者语义总spike | 作者语义后500ms spike |
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|---|---:|---:|---:|
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| 两个ALLN | 473 | 213 | 106 |
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| 三个ALLN | 1,064 | 448 | 224 |
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| 三ALLN + VA6 PN + KC | 1,483 | 610 | 306 |
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| 前向真实边对照 | 3 | 3 | 0 |
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| 无刺激 | 0 | 0 | 0 |
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**持续循环没有因恢复作者语义而消失。** 放电减少不是学习成功，也没有据此挑选获胜参数。
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单独限制不应期内g更新甚至提高了这些回路的放电；不能把单一算子改动当作通用修复。
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小回路省略了大部分全脑输入和graded APL，不能证明全脑问题完全由这些连接引起。
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此前全脑背景复核显示，三个ALLN的晚期正到达权重约91%以上来自所选五节点之外；这也不是因果责任百分比。
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## 作者参考及边界
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- 官方代码：`philshiu/Drosophila_brain_model`，commit `91bdd1e7dcf193f3e7ca5a8933497fcef63b7960`，MIT。
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- [原作者论文](https://www.nature.com/articles/s41586-024-07763-9)；[固定代码版本](https://github.com/philshiu/Drosophila_brain_model/tree/91bdd1e7dcf193f3e7ca5a8933497fcef63b7960)。
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- Brian2 2.5.1，独立Linux环境；作者记录为Windows/Python3.10.11，此处Linux/Python3.10.12，不能称为二进制相同。
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- 原始g单位为volt，是加性输入状态，不是电导；reset中的局部`w=0`不会清零真实Synapses权重。
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- 原`poi`接口另做了零时长编译审计：向v加入68.75mV、150Hz、N=1，并将目标不应期置零。它不是本轮共同初始seed，也不是已验证的新嗅觉接口。
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- 安装只发生在新实验`.venv`，保存下载URL与归档SHA-256；MarkupSafe1.1.1使用源归档，其他核心包使用wheel。未改旧科学环境或ComfyUI。
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## 下一关
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1. 独立GPU实现须补足高入度、正负抵消、逐边加法顺序、非零初始g、阈值临界值与延迟队列边界测试；禁止用`g+sum(w)`擅自代替作者逐边写入。
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2. 在短时全图上对照事件及状态，再开展固定DoOR嗅觉CAL。保持既有输入请求，不顺手切换为作者的rfc=0输入接口；分别统计请求、因不应期拒绝与实际放电。
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3. CAL通过后才能使用独立seed及新odor验证。不能靠低活跃度或held-out调参授予成功。
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4. 真正conditioning须重新执行D(t)=0、frozen与随机奖励路径，并真正推进retention等待时间；旧脚本中复用基线/新建状态的检查不能替代这些端到端验证。
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5. 全部学习结论仍以MBON/downstream的刺激特异性、acquisition/reversal/retention/delayed reward为准，至少5个training seeds与独立evaluation seeds。
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## 完整性和保存
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原快照230,239项及4AA、4AB、4AC、背景复核产物前后SHA-256无变化。
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旧deterministic-v2五个全脑冻结trial重新执行，事件、计数、最终V/g逐数组相同。
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4090回归峰值已分配显存285,994,496字节；这不是新动力学全脑性能benchmark。
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新模型11个payload、新实验565个payload已独立封存，环境目录不纳入payload。
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Git同步代码、小报告和校验清单；大数据及完整状态轨迹仍在服务器，不能称为完整异机备份。
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详见[完整结果](phase4ad-author-semantics.md)、[封存核验](../../registry/phase4ad-author-semantics/seal-verification.json)。
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# Phase 4AD — independently authorized author-semantics laboratory
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Human authorization: "允许独立新模型分支测试动力学假设".
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Goal remains usable stimulus-specific learning, not reducing spike count or passing an isolated fixture.
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All Phase 1–4AC scientific artifacts, canonical CSR/raw counts, transmitter signs,
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W_syn=0.275, APL calibration, eligibility-v2 and the 14,232-edge plastic mask are
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read-only. No training, threshold/gain/eta search, decoder, game or visual work in
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this prerequisite laboratory. Existing candidate names are not replaced.
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## Fixed comparison before results
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Use the exact five preselected 4AC motifs and the same initial single seed event,
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not newly selected cells. Record all five outcomes, including persistent firing.
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Preserve true edge IDs, counts and original global neuron_idx/body_id.
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|
||||
1. Reproduce frozen explicit float32 Euler/operator semantics exactly, using 4AC
|
||||
archived event/state arrays as an independent oracle.
|
||||
2. Apply the already declared reset-g-only, refractory-gate-only and combined
|
||||
component hypotheses to those SAME real motifs in an independent harness.
|
||||
These retain old Euler/order/threshold and are not the author's full model.
|
||||
3. Execute the exact pinned author's `create_model` under isolated Brian2 2.5.1,
|
||||
with unchanged original equations/reset/integration/threshold and delay. Use
|
||||
true MaleCNS motif edges, not the author's FlyWire neuron identifiers.
|
||||
4. Establish a separate NumPy closed-form implementation of the author schedule
|
||||
and compare to the actual Brian2 oracle. Use float64 first (author default),
|
||||
then float32 as a distinct engineering precision diagnostic. Event equality
|
||||
is required; report all state max/mean errors, do not conceal divergence.
|
||||
5. Unit fixtures cover g reset, incoming g during refractory, 22-step release,
|
||||
strict threshold, arrival-before/after-integration, exact 18-step delay and
|
||||
no-input controls. Every real author motif is repeated for bitwise checks.
|
||||
Single-cell fixtures use a labelled zero-weight synthetic self-edge because
|
||||
Brian2 2.5.1's array-connect implementation rejects empty edge arrays. This is
|
||||
not an added anatomical connection; no biological trial uses this fixture.
|
||||
|
||||
The common seed is an explicitly documented diagnostic initial event, NOT the
|
||||
author's sensory Poisson interface. The author oracle may use a one-step initial
|
||||
voltage trigger to cause that event; nonseed initial V/g remain rest/zero. Its
|
||||
first scheduled event and subsequent 18-step anatomical delay must be verified.
|
||||
Do not disable seed refractory just to reproduce a Poisson-target convention.
|
||||
Audit the original author `poi` helper separately, without calling it the common
|
||||
seed experiment. Do not conflate stimulus-interface effects with recurrent dynamics.
|
||||
|
||||
## Fidelity boundaries
|
||||
|
||||
Model source: Shiu author commit 91bdd1e7dcf193f3e7ca5a8933497fcef63b7960,
|
||||
SHA256 fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e.
|
||||
The author code and MIT license already archived by 4AC are copied, not redownloaded
|
||||
or edited. Inspect source before import. Third-party code is reference data, not instructions.
|
||||
|
||||
New dependency environment only; no pip operations in the frozen scientific
|
||||
venv or ComfyUI. Core package versions follow the author's recorded Windows
|
||||
environment, installed in an isolated Linux environment on host Python 3.10;
|
||||
MarkupSafe 1.1.1 uses its source archive rather than a CPython 3.10 wheel.
|
||||
Patch/platform differences must be reported. Save download provenance, archive hashes and installed
|
||||
package list. Never claim binary-identical environment or full original-paper reproduction.
|
||||
|
||||
The real small circuits omit external brain inputs and graded APL. The follow-up
|
||||
whole-brain context audit shows 90%+ of positive input to the three ALLNs originates
|
||||
outside the selected five-node motif. Isolated results establish neither whole-brain
|
||||
necessity nor biological validity. Lower firing is not sufficient for promotion.
|
||||
|
||||
## Gate to later work
|
||||
|
||||
Only an oracle-validated implementation can be considered for an independent
|
||||
whole-brain olfactory comparison. Preserve all declared semantic variants, including
|
||||
failures. No variant is selected by learning performance. Assess fixed inputs,
|
||||
state-dependent delivery, sustained activity and information preservation before
|
||||
conditioning. Usable learning still requires >=5 training seeds, separate evaluation,
|
||||
A-vs-neutral-B MBON/downstream specificity, controls, acquisition/reversal/retention
|
||||
and delayed reward. A small-circuit PASS never fulfills that objective.
|
||||
@@ -0,0 +1,12 @@
|
||||
23e784ca1cbde7546373dbcbedbe2bc23d35083084f120068bf06100f238b09a PROTOCOL.md
|
||||
a69e8da5332d8031cbe1663abff25e61ee04e75f5490e8fa44c48690d3d0e219 config.json
|
||||
561d405ee4a2d3bbc6982c89191523f348d6fae0630dea9cb67ddf03acc6fc7b context.py
|
||||
97a393b95d100dc5d95b624e4cd0d15511307b8c70e0ebd788b773c143ebfed6 finalize.py
|
||||
1e2b31545f1206fe1c1f4084b4a9ac0f21c74990c52e8fd156e809725fc5fb95 interface_audit.py
|
||||
9663da41d5f623e1d7750e3cad3df880148079968f115bf583b58799fc5fb31f math_lab.py
|
||||
2f4b073261965cf18a94697caf725c40b2e8db790c2871862201337f40c399e5 oracle_runner.py
|
||||
49caa9553938f06539ecf282868491f8e1319d8f24220f89ac9e232db2d82e51 prepare.py
|
||||
a69efc6509b85a5cc00a652caa4820c9c9e1406f6b97c6cc741322728c4d51c3 requirements-oracle.txt
|
||||
8746e1971c5a40a738e10f8f574a243c29cefa5a8b9f61a2846557ceffd1289a run_lab.py
|
||||
fe37a48ce804e6c50f93404bfd3c23a7e54cd49061bbdfb4860315879648c73b verify_frozen.py
|
||||
07a22bade4892adca3a4585656aa3ecce5219790b0dc8a851a12bfd7248d3769 manifest.json
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"name": "author-semantics-lab-v1",
|
||||
"phase": "4AD",
|
||||
"root": "/data/digital-drosophila",
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"human_authorization": "允许独立新模型分支测试动力学假设",
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"seed_margin_mv": 1.0,
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"refractory_ms": 2.2,
|
||||
"resting_potential_mv": -52.0,
|
||||
"reset_potential_mv": -52.0,
|
||||
"threshold_mv": -45.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"seed_node": 1381,
|
||||
"motifs": ["pair", "trio", "five", "feedforward", "no_input"],
|
||||
"component_policies": ["frozen", "reset_g_only", "refractory_gate_only", "reset_and_refractory"],
|
||||
"author_precisions": ["float64", "float32"],
|
||||
"comparison": {
|
||||
"float64_state_max_abs_tolerance_mv": 0.00000001,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"no_training_or_candidate": true
|
||||
}
|
||||
@@ -0,0 +1,27 @@
|
||||
import hashlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
MODEL=Path(__file__).resolve().parent
|
||||
CFG=json.loads((MODEL/'config.json').read_text(encoding='utf-8'))
|
||||
ROOT=Path(CFG['root']);EXP=ROOT/CFG['experiment'];AC=ROOT/CFG['prior_experiment']
|
||||
|
||||
def digest(path):
|
||||
h=hashlib.sha256()
|
||||
with Path(path).open('rb') as f:
|
||||
for b in iter(lambda:f.read(8<<20),b''):h.update(b)
|
||||
return h.hexdigest()
|
||||
|
||||
def save(path,value):
|
||||
path=Path(path);path.parent.mkdir(parents=True,exist_ok=True)
|
||||
with path.open('x',encoding='utf-8') as f:json.dump(value,f,indent=2,ensure_ascii=False,allow_nan=False);f.write('\n')
|
||||
|
||||
def source_hashes():
|
||||
return {p.name:digest(p) for p in sorted(MODEL.iterdir()) if p.is_file() and p.name not in ('manifest.json','checksums.sha256')}
|
||||
|
||||
def check_prereg():
|
||||
pre=json.loads((EXP/'protocol/preregistered.json').read_text())
|
||||
assert pre['source_hashes']==source_hashes(),'Source changed after preregistration'
|
||||
for r in pre['input_hashes']:
|
||||
assert digest(ROOT/r['path'])==r['sha256'],r['path']
|
||||
return pre
|
||||
@@ -0,0 +1,411 @@
|
||||
"""Seal a descriptive laboratory audit, including incomplete and failed runs.
|
||||
|
||||
Run this with the frozen scientific environment, not the isolated Brian2 venv.
|
||||
No simulation, parameter selection, candidate promotion or old-artifact write occurs.
|
||||
"""
|
||||
from datetime import datetime, timezone
|
||||
import json
|
||||
import math
|
||||
from pathlib import Path
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from context import CFG, EXP, MODEL, ROOT, check_prereg, digest, save, source_hashes
|
||||
from prepare import frozen_integrity
|
||||
|
||||
|
||||
UNIT_NAMES = ("zero", "weak_excitation", "inhibition", "refractory_boundary",
|
||||
"equal_threshold", "above_threshold", "nonzero_g_reset", "delay_18")
|
||||
EXCLUDED_PARTS = {".venv", "__pycache__"}
|
||||
SEAL_NAMES = {"manifest.json", "checksums.sha256"}
|
||||
|
||||
|
||||
def read_json(path):
|
||||
with Path(path).open(encoding="utf-8") as stream:
|
||||
return json.load(stream, parse_constant=lambda value: (_ for _ in ()).throw(
|
||||
ValueError("Nonfinite JSON number: " + value)))
|
||||
|
||||
|
||||
def exception_record(error):
|
||||
return {"type": type(error).__name__, "message": str(error),
|
||||
"traceback": traceback.format_exc()}
|
||||
|
||||
|
||||
def attempt(records, name, function):
|
||||
"""Never let an earlier failed arm hide subsequent available evidence."""
|
||||
try:
|
||||
detail = function()
|
||||
if not isinstance(detail, dict) or type(detail.get("pass")) is not bool:
|
||||
raise ValueError("Check must provide an explicit Boolean pass")
|
||||
item = {"name": name, **detail}
|
||||
except Exception as error:
|
||||
item = {"name": name, "pass": False, "error": exception_record(error)}
|
||||
records.append(item)
|
||||
return item
|
||||
|
||||
|
||||
def require(condition, message):
|
||||
if not condition:
|
||||
raise ValueError(message)
|
||||
|
||||
|
||||
def load_trace(directory, expected_nodes, steps, cfg, seed_node, natural_events=False):
|
||||
events = pd.read_csv(directory / "events.csv", dtype={"step": "int64", "neuron_idx": "int64"})
|
||||
require(list(events.columns) == ["step", "neuron_idx"], "Unexpected event columns")
|
||||
for key in ("step", "neuron_idx"):
|
||||
values = events[key].to_numpy()
|
||||
require(np.isfinite(values).all() and np.equal(values, np.floor(values)).all(),
|
||||
"Nonfinite or nonintegral event field: " + key)
|
||||
ticks = events.step.to_numpy(np.int64)
|
||||
nodes = events.neuron_idx.to_numpy(np.int64)
|
||||
require(bool(np.all((ticks >= 0) & (ticks < steps))), "Event outside trial")
|
||||
require(bool(np.all(np.diff(ticks) >= 0)), "Events not time ordered")
|
||||
require(not events.duplicated().any(), "Duplicate event at one neuron/timestep")
|
||||
require(bool(np.isin(nodes, expected_nodes).all()), "Event identity outside motif")
|
||||
with np.load(directory / "state.npz", allow_pickle=False) as stored:
|
||||
state = {key: stored[key].copy() for key in stored.files}
|
||||
require(np.array_equal(state["nodes"], expected_nodes), "State neuron order differs")
|
||||
history = state["history"]
|
||||
require(history.shape == (steps, 3, len(expected_nodes)), "Incomplete state history")
|
||||
require(all(np.isfinite(value).all() for value in state.values()), "Nonfinite state")
|
||||
actual_counts = np.array([np.count_nonzero(nodes == node) for node in expected_nodes])
|
||||
require(np.array_equal(state["counts"], actual_counts), "Counts disagree with raw events")
|
||||
for index, key in enumerate(("v", "g", "ref")):
|
||||
require(np.array_equal(state[key], history[-1, index]),
|
||||
"Final state disagrees with history: " + key)
|
||||
if seed_node is None and not natural_events:
|
||||
require(len(ticks) == 0, "No-input control emitted a spike")
|
||||
elif seed_node is not None:
|
||||
require(np.array_equal(nodes[ticks == 0], [seed_node]),
|
||||
"Declared initial seed event missing or duplicated")
|
||||
late = ticks >= round(cfg["late_start_ms"] / cfg["dt_ms"])
|
||||
per_node = [{"neuron_idx": int(node), "spikes": int(actual_counts[index]),
|
||||
"late_spikes": int(np.count_nonzero(late & (nodes == node))),
|
||||
"last_ms": float(ticks[nodes == node][-1] * cfg["dt_ms"])
|
||||
if actual_counts[index] else None}
|
||||
for index, node in enumerate(expected_nodes)]
|
||||
return {"step": ticks, "neuron_idx": nodes, **state}, per_node
|
||||
|
||||
|
||||
def compare_raw(actual, expected, tolerance):
|
||||
require(math.isfinite(tolerance) and tolerance >= 0, "Invalid preregistered tolerance")
|
||||
a = np.asarray(actual["history"], np.float64)
|
||||
b = np.asarray(expected["history"], np.float64)
|
||||
require(a.shape == b.shape and a.size > 0, "Incomplete comparison trajectories")
|
||||
require(np.isfinite(a).all() and np.isfinite(b).all(), "Nonfinite comparison trajectory")
|
||||
error = np.abs(a[:, :2] - b[:, :2])
|
||||
exact_events = bool(np.array_equal(actual["step"], expected["step"]) and
|
||||
np.array_equal(actual["neuron_idx"], expected["neuron_idx"]))
|
||||
first = None
|
||||
if not exact_events:
|
||||
for step in range(len(a)):
|
||||
aa = actual["neuron_idx"][actual["step"] == step]
|
||||
bb = expected["neuron_idx"][expected["step"] == step]
|
||||
if not np.array_equal(aa, bb):
|
||||
first = step
|
||||
break
|
||||
prefix = error if first is None else error[:first]
|
||||
reference_exact = bool(np.array_equal(a[:, 2], b[:, 2]))
|
||||
count_exact = bool(np.array_equal(actual["counts"], expected["counts"]))
|
||||
maximum = float(error.max())
|
||||
return {"exact_events": exact_events, "exact_counts": count_exact,
|
||||
"ref_exact": reference_exact, "max_abs_mv": maximum,
|
||||
"mean_abs_mv": float(error.mean()),
|
||||
"same_event_prefix_max_abs_mv": float(prefix.max()) if prefix.size else None,
|
||||
"first_event_divergence_step": first, "tolerance_mv": tolerance,
|
||||
"pass_oracle": exact_events and count_exact and reference_exact and maximum <= tolerance}
|
||||
|
||||
|
||||
def comparison_record_matches(raw, stored):
|
||||
"""Only descriptive mean reduction may differ between scientific NumPy builds."""
|
||||
if set(raw) != set(stored):
|
||||
return False
|
||||
return all(math.isclose(raw[key], stored[key], rel_tol=0, abs_tol=1e-12)
|
||||
if key == "mean_abs_mv" else raw[key] == stored[key] for key in raw)
|
||||
|
||||
|
||||
def motif_nodes(cfg, motif):
|
||||
with np.load(ROOT / cfg["prior_experiment"] / "motifs" / (motif + "-state.npz"),
|
||||
allow_pickle=False) as prior:
|
||||
return prior["nodes"].copy()
|
||||
|
||||
|
||||
def check_summary_rows(table, motif, policy, precision, per_node):
|
||||
part = table[(table.motif == motif) & (table.policy == policy) &
|
||||
(table.precision == precision)]
|
||||
require(len(part) == len(per_node) and not part.neuron_idx.duplicated().any(),
|
||||
"Missing/duplicate per-neuron summary rows")
|
||||
require(set(part.neuron_idx) == {row["neuron_idx"] for row in per_node},
|
||||
"Summary neuron identities differ")
|
||||
indexed = part.set_index("neuron_idx")
|
||||
for row in per_node:
|
||||
current = indexed.loc[row["neuron_idx"]]
|
||||
require(current.spikes == row["spikes"] and current.late_spikes == row["late_spikes"],
|
||||
"Summary spike counts disagree with raw events")
|
||||
last = current.last_ms
|
||||
require(pd.isna(last) if row["last_ms"] is None else last == row["last_ms"],
|
||||
"Summary last-spike time disagrees with raw events")
|
||||
|
||||
|
||||
def seal(root):
|
||||
require(not any((root / name).exists() for name in SEAL_NAMES),
|
||||
"Refusing to overwrite an existing seal: " + str(root))
|
||||
files = []
|
||||
for path in sorted(root.rglob("*")):
|
||||
relative = path.relative_to(root)
|
||||
if EXCLUDED_PARTS.intersection(relative.parts):
|
||||
continue
|
||||
require(not path.is_symlink(), "Unsealed symlink not permitted: " + str(path))
|
||||
if not path.is_file() or (len(relative.parts) == 1 and path.name in SEAL_NAMES):
|
||||
continue
|
||||
files.append({"path": relative.as_posix(), "bytes": path.stat().st_size,
|
||||
"sha256": digest(path)})
|
||||
require(bool(files), "Refusing empty artifact manifest")
|
||||
manifest = {"schema": "sha256-artifact-manifest-v1", "root": str(root),
|
||||
"created_utc": datetime.now(timezone.utc).isoformat(),
|
||||
"excluded_directory_names": sorted(EXCLUDED_PARTS),
|
||||
"self_hash_policy": "Manifest lists payload files; checksums also hashes manifest; neither hashes checksums.",
|
||||
"files": files}
|
||||
save(root / "manifest.json", manifest)
|
||||
checksum_rows = files + [{"path": "manifest.json", "sha256": digest(root / "manifest.json")}]
|
||||
with (root / "checksums.sha256").open("x", encoding="utf-8", newline="\n") as stream:
|
||||
for row in checksum_rows:
|
||||
stream.write(row["sha256"] + " " + row["path"] + "\n")
|
||||
for row in checksum_rows:
|
||||
require(digest(root / row["path"]) == row["sha256"],
|
||||
"Artifact changed while sealing: " + row["path"])
|
||||
|
||||
|
||||
def main():
|
||||
outputs = [EXP / "audit/finalize-start.json", EXP / "audit/final-summary.json",
|
||||
EXP / "regression/old-integrity-after.json", EXP / "regression/preregistration-after.json",
|
||||
EXP / "REPORT.md"] + [root / name for root in (MODEL, EXP) for name in SEAL_NAMES]
|
||||
require(not any(path.exists() for path in outputs), "Refusing reused finalization outputs")
|
||||
save(EXP / "audit/finalize-start.json", {"timestamp_utc": datetime.now(timezone.utc).isoformat(),
|
||||
"python": sys.version, "executable": sys.executable, "learning": False,
|
||||
"whole_brain_alternative_run": False})
|
||||
checks = []
|
||||
prereg = attempt(checks, "preregistration", lambda: {"pass": True, "record": check_prereg()})
|
||||
if prereg["pass"]:
|
||||
cfg = prereg["record"]["config"]
|
||||
else:
|
||||
# Read the sealed declaration for descriptive recovery, never bless source drift.
|
||||
try:
|
||||
cfg = read_json(EXP / "protocol/preregistered.json")["config"]
|
||||
except Exception:
|
||||
cfg = CFG
|
||||
integrity = attempt(checks, "old_frozen_integrity_after", lambda: {
|
||||
"pass": True, "results": frozen_integrity()})
|
||||
save(EXP / "regression/old-integrity-after.json", integrity)
|
||||
|
||||
def completed():
|
||||
record = read_json(EXP / "audit/run-complete.json")
|
||||
require(record.get("learning_achieved") is False and
|
||||
record.get("whole_brain_alternative_run") is False, "Run scope inconsistent")
|
||||
require(record.get("author_original_source_sha256") == digest(EXP / "external-resources/model.py"),
|
||||
"Author source hash disagrees with run-complete record")
|
||||
return {"pass": record.get("all_author_oracle_comparisons_pass") is True and
|
||||
record.get("all_frozen_components_match_archive") is True, "record": record}
|
||||
run_complete = attempt(checks, "run_complete", completed)
|
||||
|
||||
def frozen_five_check():
|
||||
record = read_json(EXP / "regression/frozen-five.json")
|
||||
trials = record.get("trials", [])
|
||||
require(len(trials) == 5 and len({row["trial"] for row in trials}) == 5,
|
||||
"Exactly five distinct frozen reference trials are required")
|
||||
required = {"step", "neuron_idx", "counts", "v", "g"}
|
||||
exact = all(set(row.get("exact", {})) == required and
|
||||
all(value is True for value in row["exact"].values()) for row in trials)
|
||||
return {"pass": record.get("pass_all") is True and exact, "record": record,
|
||||
"scope": "Old frozen runtime regression only, not a new-model dynamics gate."}
|
||||
frozen_five = attempt(checks, "frozen_five_gpu_references", frozen_five_check)
|
||||
|
||||
def interface_check():
|
||||
record = read_json(EXP / "oracle/poi-interface/audit.json")
|
||||
flags = record.get("checks", {})
|
||||
require(isinstance(flags, dict) and bool(flags), "Empty Poisson interface audit")
|
||||
require(record.get("author_sha256") == digest(EXP / "external-resources/model.py"),
|
||||
"Poisson audit source hash differs")
|
||||
require(record.get("simulation_duration_ms") == 0 and
|
||||
record.get("stochastic_time_steps") == 0 and
|
||||
record.get("random_realization_tested") is False and
|
||||
record.get("common_seed_experiment") is False, "Poisson audit scope differs")
|
||||
for name, expected in record.get("input_hashes", {}).items():
|
||||
require(digest(EXP / "oracle/poi-interface" / name) == expected,
|
||||
"Poisson fixture input changed: " + name)
|
||||
return {"pass": record.get("gate") is True and all(value is True for value in flags.values()),
|
||||
"record": record, "scope": "Compile-time interface audit; no Poisson realization."}
|
||||
interface = attempt(checks, "author_poi_compile_only", interface_check)
|
||||
outcomes = []
|
||||
components = []
|
||||
steps = round(cfg["duration_ms"] / cfg["dt_ms"])
|
||||
for motif in cfg["motifs"]:
|
||||
for policy in cfg["component_policies"]:
|
||||
def component_check(motif=motif, policy=policy):
|
||||
nodes = motif_nodes(cfg, motif)
|
||||
raw, per_node = load_trace(EXP / "components" / (motif + "-" + policy), nodes,
|
||||
steps, cfg, None if motif == "no_input" else cfg["seed_node"])
|
||||
outcomes.append({"motif": motif, "policy": policy, "precision": "float32",
|
||||
"neurons": per_node, "activity_is_not_a_validation_gate": True})
|
||||
table = pd.read_csv(EXP / "components/summary.csv", float_precision="round_trip")
|
||||
check_summary_rows(table, motif, policy, "float32", per_node)
|
||||
if policy == "frozen":
|
||||
prior_dir = ROOT / cfg["prior_experiment"] / "motifs"
|
||||
old_events = pd.read_parquet(prior_dir / (motif + "-events.parquet"))
|
||||
require(np.array_equal(raw["step"], old_events.step) and
|
||||
np.array_equal(raw["neuron_idx"], old_events.neuron_idx),
|
||||
"Frozen events disagree with 4AC")
|
||||
with np.load(prior_dir / (motif + "-state.npz"), allow_pickle=False) as old:
|
||||
require(all(np.array_equal(old[key], raw[key]) for key in old.files),
|
||||
"Frozen states disagree with 4AC")
|
||||
return {"pass": True, "raw_and_summary_consistent": True,
|
||||
"interpretation": "Artifact/finite/seed checks only; activity is descriptive."}
|
||||
components.append(attempt(checks, "component:" + motif + ":" + policy, component_check))
|
||||
|
||||
unit_results = []
|
||||
for precision in cfg["author_precisions"]:
|
||||
for fixture in UNIT_NAMES:
|
||||
def unit_check(precision=precision, fixture=fixture):
|
||||
stem = fixture
|
||||
record = read_json(EXP / "regression" / ("unit-" + stem + "-" + precision + ".json"))
|
||||
require(record.get("fixture") == fixture and record.get("precision") == precision,
|
||||
"Unit identity mismatch")
|
||||
unit_cfg = dict(cfg, duration_ms=20.0)
|
||||
seeded = fixture in ("refractory_boundary", "above_threshold")
|
||||
directory = EXP / "oracle" / ("unit-" + stem + "-" + precision)
|
||||
nodes = np.array([-2, -1]) if fixture == "delay_18" else np.array([-1])
|
||||
seed = -2 if fixture == "delay_18" else (-1 if seeded else None)
|
||||
actual, _ = load_trace(directory / "brian-output", nodes, 200, unit_cfg, seed,
|
||||
natural_events=fixture == "nonzero_g_reset")
|
||||
independent, _ = load_trace(directory / "independent-output", nodes, 200, unit_cfg, seed,
|
||||
natural_events=fixture == "nonzero_g_reset")
|
||||
raw = compare_raw(actual, independent, float(cfg["comparison"][precision + "_state_max_abs_tolerance_mv"]))
|
||||
stored = read_json(directory / "comparison.json")
|
||||
require(comparison_record_matches(raw, stored) and
|
||||
comparison_record_matches(raw, record["comparison"]),
|
||||
"Unit comparison differs from raw trajectories")
|
||||
flags = record.get("checks")
|
||||
require(isinstance(flags, dict) and bool(flags), "Empty unit checks cannot pass")
|
||||
return {"pass": raw["pass_oracle"] and all(value is True for value in flags.values()),
|
||||
"checks": flags, "raw_comparison": raw}
|
||||
unit_results.append(attempt(checks, "unit:" + fixture + ":" + precision, unit_check))
|
||||
|
||||
author_results = []
|
||||
precision_cross = []
|
||||
for motif in cfg["motifs"]:
|
||||
by_precision = {}
|
||||
for precision in cfg["author_precisions"]:
|
||||
def author_check(motif=motif, precision=precision):
|
||||
nodes = motif_nodes(cfg, motif)
|
||||
directory = EXP / "oracle" / (motif + "-" + precision)
|
||||
seed = None if motif == "no_input" else cfg["seed_node"]
|
||||
actual, per_node = load_trace(directory / "brian-output", nodes, steps, cfg, seed)
|
||||
independent, _ = load_trace(directory / "independent-output", nodes, steps, cfg, seed)
|
||||
by_precision[precision] = actual
|
||||
outcomes.append({"motif": motif, "policy": "author_complete", "precision": precision,
|
||||
"neurons": per_node, "activity_is_not_a_validation_gate": True})
|
||||
raw = compare_raw(actual, independent, float(cfg["comparison"][precision + "_state_max_abs_tolerance_mv"]))
|
||||
stored = read_json(directory / "comparison.json")
|
||||
require(comparison_record_matches(raw, stored), "Author comparison differs from raw trajectories")
|
||||
repeated, _ = load_trace(EXP / "oracle" / (motif + "-" + precision + "-repeat") /
|
||||
"brian-output", nodes, steps, cfg, seed)
|
||||
repeat_flags = read_json(EXP / "regression" / (motif + "-" + precision + "-repeat.json"))
|
||||
require(set(repeat_flags) == set(actual) - {"nodes"}, "Incomplete repeat comparison fields")
|
||||
repeat_raw = {key: bool(np.array_equal(actual[key], repeated[key])) for key in repeat_flags}
|
||||
require(repeat_raw == repeat_flags, "Repeat flags disagree with raw arrays")
|
||||
table = pd.read_csv(EXP / "audit/all-motif-results.csv", float_precision="round_trip")
|
||||
check_summary_rows(table, motif, "author_complete", precision, per_node)
|
||||
return {"pass": raw["pass_oracle"] and all(repeat_raw.values()),
|
||||
"raw_comparison": raw, "repeat_exact": repeat_raw}
|
||||
author_results.append(attempt(checks, "author:" + motif + ":" + precision, author_check))
|
||||
def cross_check(motif=motif):
|
||||
raw = compare_raw(by_precision["float32"], by_precision["float64"],
|
||||
float(cfg["comparison"]["float32_state_max_abs_tolerance_mv"]))
|
||||
stored = read_json(EXP / "regression" / (motif + "-precision-cross-comparison.json"))
|
||||
require(comparison_record_matches(raw, stored), "Precision cross comparison differs from raw trajectories")
|
||||
return {"pass": True, "comparison": raw,
|
||||
"interpretation": "Reporting consistency only; cross-precision divergence is allowed and retained."}
|
||||
precision_cross.append(attempt(checks, "precision_cross_record:" + motif, cross_check))
|
||||
|
||||
retained_errors = []
|
||||
for path in sorted(EXP.rglob("error.json")):
|
||||
if EXCLUDED_PARTS.intersection(path.relative_to(EXP).parts):
|
||||
continue
|
||||
try:
|
||||
error = read_json(path)
|
||||
except Exception as exception:
|
||||
error = exception_record(exception)
|
||||
retained_errors.append({"path": path.relative_to(EXP).as_posix(), "error": error})
|
||||
checks.append({"name": "no_recorded_oracle_errors", "pass": not retained_errors,
|
||||
"errors": retained_errors})
|
||||
final_prereg = attempt(checks, "preregistration_at_seal", lambda: {"pass": True, "record": check_prereg()})
|
||||
save(EXP / "regression/preregistration-after.json", {
|
||||
"initial": prereg, "at_seal": final_prereg, "observed_source_hashes": source_hashes()})
|
||||
passed = bool(checks) and all(item["pass"] for item in checks)
|
||||
summary = {"phase": cfg.get("phase", "4AD"),
|
||||
"status": "COMPLETE-ORACLE-VALIDATED-LAB-ONLY" if passed else "INCOMPLETE-OR-FAILED-LAB-VALIDATION",
|
||||
"laboratory_validation_pass": passed, "checks": checks,
|
||||
"component_checks": components, "unit_checks": unit_results,
|
||||
"frozen_five_gpu_check": frozen_five, "author_poi_interface_check": interface,
|
||||
"author_oracle_checks": author_results, "precision_cross_checks": precision_cross,
|
||||
"motif_outcomes": outcomes, "run_complete_record": run_complete,
|
||||
"whole_brain_alternative_run": False, "conditioning_run": False,
|
||||
"learning_achieved": False, "candidate_generated": False, "selected_variant": None,
|
||||
"goal_remains_unachieved": True,
|
||||
"limits": ["No whole-brain or learning experiment in this protocol.",
|
||||
"No activity-based variant selection; silence is not a success gate.",
|
||||
"Isolated motifs omit external network input and graded APL.",
|
||||
"Author equations on MaleCNS motifs do not reproduce the original paper's full brain or sensory interface.",
|
||||
"Linux host and Python patch differences prevent a binary-identical author-environment claim."]}
|
||||
save(EXP / "audit/final-summary.json", summary)
|
||||
failures = [item for item in checks if not item["pass"]]
|
||||
lines = ["# Phase 4AD 作者动力学语义实验室", "", "状态：`" + summary["status"] + "`。", "",
|
||||
"本轮仅有预声明的小回路与算子实验；没有整脑替代动力学运行、conditioning、学习成功或新学习候选。",
|
||||
"检查以原始事件、状态轨迹和预注册误差界限重算。缺失、失败、非有限值或初始事件丢失均不能通过；放电减少或静默本身不构成成功。", "",
|
||||
f"核查通过 {sum(item['pass'] for item in checks)}/{len(checks)} 项；失败或不完整 {len(failures)} 项。",
|
||||
"冻结历史完整性：" + ("通过。" if integrity["pass"] else "失败或未完成，详见保存的错误。"),
|
||||
"源码与输入预注册一致性：" + ("通过。" if prereg["pass"] and final_prereg["pass"] else "失败或未完成；不得授予验证成功。"), "",
|
||||
"| 回路 | 语义 | 精度 | 总spikes | late spikes |", "|---|---|---|---:|---:|"]
|
||||
for row in outcomes:
|
||||
lines.append("| {} | {} | {} | {} | {} |".format(row["motif"], row["policy"], row["precision"],
|
||||
sum(item["spikes"] for item in row["neurons"]), sum(item["late_spikes"] for item in row["neurons"])))
|
||||
if not outcomes:
|
||||
lines.append("| 无完整可读取结果 | 未执行或缺失 | — | — | — |")
|
||||
lines.extend(["", "作者Brian2与独立NumPy比较：", "",
|
||||
"| 实验 | 结果 | 事件一致 | 最大V/g误差 mV | 首次事件分歧step |", "|---|---|---|---:|---:|"])
|
||||
for item in author_results:
|
||||
raw = item.get("raw_comparison", {})
|
||||
lines.append("| {} | {} | {} | {} | {} |".format(item["name"], "通过" if item["pass"] else "失败/缺失",
|
||||
raw.get("exact_events", "未知"), raw.get("max_abs_mv", "未知"),
|
||||
raw.get("first_event_divergence_step", "无或未取得")))
|
||||
lines.extend(["", "float32与float64交叉比较仅记录精度影响，允许保留分歧，不能用于掩盖各自与独立oracle的失败。"])
|
||||
for item in precision_cross:
|
||||
raw = item.get("comparison")
|
||||
if raw:
|
||||
lines.append(f"- {item['name']}：事件一致={raw['exact_events']}，最大误差={raw['max_abs_mv']} mV，首次事件分歧step={raw['first_event_divergence_step']}。")
|
||||
if failures:
|
||||
lines.extend(["", "失败与未完成项：", ""])
|
||||
for item in failures:
|
||||
detail = item.get("error", {}).get("message") or "原始条件未全部满足；完整字段见 final-summary.json。"
|
||||
lines.append("- `" + item["name"] + "`：" + str(detail).replace("\n", " "))
|
||||
lines.extend(["", "所有失败轨迹和错误文件原样保留；本简报没有选择获胜变体。",
|
||||
"小回路缺少外部整脑输入与graded APL，不能证明整脑因果必要性、生物真实性或信息保持。初始诊断seed也不是作者的感官Poisson接口。",
|
||||
"环境为Linux宿主，作者记录为Windows；Python补丁版本与环境差异见environment记录，不能称为二进制相同或原论文整脑复现。",
|
||||
"后续整脑嗅觉比较仍须核查固定输入、实际送达事件、持续活动和信息保持；可用学习仍需至少5个训练seed、独立评估、MBON/downstream的A对中性B特异性及acquisition/reversal/retention/delayed-reward对照。",
|
||||
"封存排除 .venv 与 __pycache__，保留环境安装报告；manifest列出payload哈希，checksums另含manifest，不存在自哈希循环。", ""])
|
||||
with (EXP / "REPORT.md").open("x", encoding="utf-8") as stream:
|
||||
stream.write("\n".join(lines))
|
||||
seal(MODEL)
|
||||
seal(EXP)
|
||||
print(json.dumps({"status": summary["status"], "laboratory_validation_pass": passed,
|
||||
"failed_checks": len(failures), "learning_achieved": False,
|
||||
"whole_brain_alternative_run": False, "sealed": [str(MODEL), str(EXP)]},
|
||||
ensure_ascii=False), flush=True)
|
||||
return 0 if passed else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
@@ -0,0 +1,149 @@
|
||||
"""Compile-only audit of the pinned author's sensory Poisson interface.
|
||||
|
||||
This is separate from the common single-seed diagnostic. Zero simulation time
|
||||
is advanced, so this audit does not draw or validate any Poisson realization.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import types
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from context import CFG, EXP, digest, save
|
||||
from oracle_runner import AUTHOR_COMMIT, AUTHOR_SHA256, _code_records, _environment
|
||||
|
||||
|
||||
def audit_author_poi() -> dict:
|
||||
"""Create the original model and poi helper, compile at t=0, and save evidence."""
|
||||
author_path = EXP / "external-resources" / "model.py"
|
||||
author_bytes = author_path.read_bytes()
|
||||
source_hash = hashlib.sha256(author_bytes).hexdigest()
|
||||
if source_hash != AUTHOR_SHA256:
|
||||
raise ValueError("The Poisson audit requires the exact pinned author source")
|
||||
output = EXP / "oracle" / "poi-interface"
|
||||
output.mkdir(parents=True, exist_ok=False)
|
||||
environment = _environment()
|
||||
save(output / "environment.json", environment)
|
||||
if environment["packages"]["brian2"]["actual"] != "2.5.1":
|
||||
raise RuntimeError("The Poisson interface audit requires Brian2 2.5.1")
|
||||
|
||||
import brian2 as b2
|
||||
|
||||
preferences = {name: b2.prefs[name] for name in (
|
||||
"codegen.target", "core.default_float_dtype", "legacy.refractory_timing")}
|
||||
old_dt = b2.defaultclock.dt
|
||||
try:
|
||||
b2.start_scope()
|
||||
b2.prefs["codegen.target"] = "numpy"
|
||||
b2.prefs["core.default_float_dtype"] = np.float64
|
||||
b2.prefs["legacy.refractory_timing"] = False
|
||||
b2.defaultclock.dt = float(CFG["dt_ms"]) * b2.ms
|
||||
author = types.ModuleType("phase4ad_poi_author_" + source_hash[:12])
|
||||
author.__file__ = str(author_path)
|
||||
# Execute exactly the reviewed bytes; create no bytecode beside the source.
|
||||
exec(compile(author_bytes, str(author_path), "exec"), author.__dict__)
|
||||
parameters = dict(author.default_params)
|
||||
|
||||
nodes = np.array([-2, -1], dtype=np.int32)
|
||||
completeness = pd.DataFrame(
|
||||
{"local_index": np.array([0, 1], dtype=np.int32)},
|
||||
index=pd.Index(nodes, name="synthetic_global_neuron_idx"))
|
||||
connections = pd.DataFrame({
|
||||
"Presynaptic_Index": np.array([0], dtype=np.int32),
|
||||
"Postsynaptic_Index": np.array([1], dtype=np.int32),
|
||||
"Excitatory x Connectivity": np.array([1], dtype=np.int64),
|
||||
"raw_count": np.array([1], dtype=np.int64),
|
||||
"edge_id": np.array([-1], dtype=np.int64),
|
||||
"source_neuron_idx": nodes[:1], "target_neuron_idx": nodes[1:],
|
||||
})
|
||||
comp_path = output / "completeness.csv"
|
||||
con_path = output / "connectivity.parquet"
|
||||
completeness.to_csv(comp_path)
|
||||
connections.to_parquet(con_path, index=False, engine="pyarrow")
|
||||
neu, syn, spikes = author.create_model(comp_path, con_path, parameters)
|
||||
rfc_before = np.asarray(neu.rfc[:] / b2.ms, dtype=np.float64).copy()
|
||||
# One excited target plus one untouched cell makes the helper's scope
|
||||
# observable. The optional second-rate class is not exercised here.
|
||||
inputs, same_neu = author.poi(neu, [0], [], parameters)
|
||||
if same_neu is not neu:
|
||||
raise AssertionError("The author poi helper returned a different neuron group")
|
||||
rfc_after = np.asarray(neu.rfc[:] / b2.ms, dtype=np.float64).copy()
|
||||
before_v = np.asarray(neu.v_[:], dtype=np.float64).copy()
|
||||
before_g = np.asarray(neu.g_[:], dtype=np.float64).copy()
|
||||
before_w = np.asarray(syn.w_[:], dtype=np.float64).copy()
|
||||
network = b2.Network(neu, syn, spikes, *inputs)
|
||||
save(output / "schedule.json", {
|
||||
"schedule": list(network.schedule),
|
||||
"summary": str(network.scheduling_summary()),
|
||||
})
|
||||
network.run(0 * b2.ms, namespace={})
|
||||
save(output / "generated-code.json", _code_records(network))
|
||||
|
||||
input_records = []
|
||||
for item in inputs:
|
||||
# PoissonInput 2.5.1 retains its on-the-fly subgroup as _group;
|
||||
# N/rate/weight/target_var are its public read-only properties.
|
||||
target = item._group
|
||||
input_records.append({
|
||||
"name": item.name, "type": type(item).__name__,
|
||||
"target_group": target.name,
|
||||
"target_local_start": int(target.start),
|
||||
"target_local_stop": int(target.stop),
|
||||
"target_global_nodes": nodes[int(target.start):int(target.stop)].tolist(),
|
||||
"target_var": item.target_var, "N": int(item.N),
|
||||
"rate_hz": float(item.rate / b2.Hz),
|
||||
"weight_mv": float(item.weight / b2.mV),
|
||||
"weight_volt": float(item.weight / b2.volt),
|
||||
"when": item.when, "order": int(item.order),
|
||||
"dt_ms": float(item.clock.dt / b2.ms),
|
||||
"abstract_code": str(item.abstract_code),
|
||||
"generated_code": [str(code.code) for code in item.code_objects],
|
||||
})
|
||||
expected_rfc = float(parameters["t_rfc"] / b2.ms)
|
||||
expected_weight = float(parameters["w_syn"] * parameters["f_poi"] / b2.mV)
|
||||
expected_rate = float(parameters["r_poi"] / b2.Hz)
|
||||
checks = {
|
||||
"exact_author_source": digest(author_path) == source_hash,
|
||||
"one_poisson_input": len(input_records) == 1,
|
||||
"initial_refractory_is_original": bool(np.array_equal(rfc_before, [expected_rfc, expected_rfc])),
|
||||
"only_excited_target_loses_refractory": bool(np.array_equal(rfc_after, [0., expected_rfc])),
|
||||
"actual_voltage_interface": all(r["target_var"] == "v" for r in input_records),
|
||||
"actual_N_one": all(r["N"] == 1 for r in input_records),
|
||||
"actual_rate_150_hz": bool(expected_rate == 150. and all(r["rate_hz"] == expected_rate for r in input_records)),
|
||||
"actual_weight_68_75_mv": bool(np.isclose(expected_weight, 68.75, rtol=0, atol=1e-12)
|
||||
and all(np.isclose(r["weight_mv"], expected_weight, rtol=0, atol=1e-12) for r in input_records)),
|
||||
"actual_synapses_slot_order_zero": all(r["when"] == "synapses" and r["order"] == 0 for r in input_records),
|
||||
"zero_elapsed_time": float(network.t / b2.ms) == 0.,
|
||||
"no_recorded_spikes": len(spikes.i[:]) == 0,
|
||||
"compile_preserves_voltage": bool(np.array_equal(before_v, np.asarray(neu.v_[:]))),
|
||||
"compile_preserves_g": bool(np.array_equal(before_g, np.asarray(neu.g_[:]))),
|
||||
"compile_preserves_weights": bool(np.array_equal(before_w, np.asarray(syn.w_[:]))),
|
||||
"compile_preserves_refractory_configuration": bool(np.array_equal(rfc_after, np.asarray(neu.rfc[:] / b2.ms))),
|
||||
}
|
||||
result = {
|
||||
"gate": bool(all(checks.values())), "checks": checks,
|
||||
"author_commit": AUTHOR_COMMIT, "author_sha256": source_hash,
|
||||
"fixture": "two synthetic nodes, one raw_count=1 connection",
|
||||
"nodes": nodes.tolist(), "exc_local_indices": [0], "exc2_local_indices": [],
|
||||
"rfc_before_ms": rfc_before.tolist(), "rfc_after_ms": rfc_after.tolist(),
|
||||
"poisson_inputs": input_records,
|
||||
"synaptic_weight_mv": np.asarray(syn.w[:] / b2.mV, dtype=np.float64).tolist(),
|
||||
"input_hashes": {"completeness.csv": digest(comp_path), "connectivity.parquet": digest(con_path)},
|
||||
"simulation_duration_ms": 0., "stochastic_time_steps": 0,
|
||||
"random_realization_tested": False, "common_seed_experiment": False,
|
||||
"interpretation": "Original poi adds 68.75 mV to voltage at 150 Hz with N=1 in the synapses slot and sets its target rfc to zero. This interface is not the common initial seed experiment.",
|
||||
"scope": "Compile-time interface audit only; no stochastic trial, learning, or whole-brain simulation. The optional exc2 helper branch is not exercised.",
|
||||
}
|
||||
save(output / "audit.json", result)
|
||||
if not result["gate"]:
|
||||
raise AssertionError("Author Poisson interface gate failed: " + str(checks))
|
||||
return result
|
||||
except Exception as error:
|
||||
save(output / "error.json", {"type": type(error).__name__, "message": str(error)})
|
||||
raise
|
||||
finally:
|
||||
for name, value in preferences.items():
|
||||
b2.prefs[name] = value
|
||||
b2.defaultclock.dt = old_dt
|
||||
@@ -0,0 +1,67 @@
|
||||
{
|
||||
"schema": "sha256-artifact-manifest-v1",
|
||||
"root": "/data/digital-drosophila/models/male-cns-lif/author-semantics-lab-v1",
|
||||
"created_utc": "2026-09-29T23:19:04.144370+00:00",
|
||||
"excluded_directory_names": [
|
||||
".venv",
|
||||
"__pycache__"
|
||||
],
|
||||
"self_hash_policy": "Manifest lists payload files; checksums also hashes manifest; neither hashes checksums.",
|
||||
"files": [
|
||||
{
|
||||
"path": "PROTOCOL.md",
|
||||
"bytes": 4577,
|
||||
"sha256": "23e784ca1cbde7546373dbcbedbe2bc23d35083084f120068bf06100f238b09a"
|
||||
},
|
||||
{
|
||||
"path": "config.json",
|
||||
"bytes": 1332,
|
||||
"sha256": "a69e8da5332d8031cbe1663abff25e61ee04e75f5490e8fa44c48690d3d0e219"
|
||||
},
|
||||
{
|
||||
"path": "context.py",
|
||||
"bytes": 1057,
|
||||
"sha256": "561d405ee4a2d3bbc6982c89191523f348d6fae0630dea9cb67ddf03acc6fc7b"
|
||||
},
|
||||
{
|
||||
"path": "finalize.py",
|
||||
"bytes": 25910,
|
||||
"sha256": "97a393b95d100dc5d95b624e4cd0d15511307b8c70e0ebd788b773c143ebfed6"
|
||||
},
|
||||
{
|
||||
"path": "interface_audit.py",
|
||||
"bytes": 8415,
|
||||
"sha256": "1e2b31545f1206fe1c1f4084b4a9ac0f21c74990c52e8fd156e809725fc5fb95"
|
||||
},
|
||||
{
|
||||
"path": "math_lab.py",
|
||||
"bytes": 4533,
|
||||
"sha256": "9663da41d5f623e1d7750e3cad3df880148079968f115bf583b58799fc5fb31f"
|
||||
},
|
||||
{
|
||||
"path": "oracle_runner.py",
|
||||
"bytes": 18548,
|
||||
"sha256": "2f4b073261965cf18a94697caf725c40b2e8db790c2871862201337f40c399e5"
|
||||
},
|
||||
{
|
||||
"path": "prepare.py",
|
||||
"bytes": 3453,
|
||||
"sha256": "49caa9553938f06539ecf282868491f8e1319d8f24220f89ac9e232db2d82e51"
|
||||
},
|
||||
{
|
||||
"path": "requirements-oracle.txt",
|
||||
"bytes": 453,
|
||||
"sha256": "a69efc6509b85a5cc00a652caa4820c9c9e1406f6b97c6cc741322728c4d51c3"
|
||||
},
|
||||
{
|
||||
"path": "run_lab.py",
|
||||
"bytes": 10795,
|
||||
"sha256": "8746e1971c5a40a738e10f8f574a243c29cefa5a8b9f61a2846557ceffd1289a"
|
||||
},
|
||||
{
|
||||
"path": "verify_frozen.py",
|
||||
"bytes": 2613,
|
||||
"sha256": "fe37a48ce804e6c50f93404bfd3c23a7e54cd49061bbdfb4860315879648c73b"
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,70 @@
|
||||
"""Independent event-scheduled author equation and preserved frozen component arms."""
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
|
||||
sys.path.insert(0,'/data/digital-drosophila/models/male-cns-lif/lif-semantics-audit-v1')
|
||||
from math_reference import params, state
|
||||
|
||||
|
||||
def component(nodes,edges,weights,config,policy,seed_node):
|
||||
p=params(config['model']);n=len(nodes);steps=round(config['duration_ms']/config['dt_ms'])
|
||||
ix={int(x):i for i,x in enumerate(nodes)}
|
||||
src=np.array([ix[int(x)] for x in edges.source_neuron_idx],np.int32)
|
||||
dst=np.array([ix[int(x)] for x in edges.target_neuron_idx],np.int32)
|
||||
v=np.full(n,p['rest'],np.float32);g=np.zeros(n,np.float32);ref=np.zeros(n,np.int32)
|
||||
qlen=p['delay_steps']+1;q=np.zeros((qlen,n),np.float32)
|
||||
history=np.empty((steps,3,n),np.float32);counts=np.zeros(n,np.int32);ts=[];ns=[]
|
||||
for t in range(steps):
|
||||
v,g,ref,fire=state(v,g,ref,q[t%qlen],p,policy);q[t%qlen]=0
|
||||
if t==0 and seed_node is not None:
|
||||
k=ix[seed_node];assert not fire[k] and ref[k]==0
|
||||
fire[k]=True;v[k]=p['reset'];ref[k]=p['ref_steps']
|
||||
if policy in ('reset_g_only','reset_and_refractory'):g[k]=np.float32(0)
|
||||
counts+=fire;active=np.flatnonzero(fire);ts.extend([t]*len(active));ns.extend(nodes[active].tolist())
|
||||
selected=np.flatnonzero(fire[src]);selected=selected[np.argsort(dst[selected],kind='stable')]
|
||||
for target in np.unique(dst[selected]):
|
||||
acc=np.float32(0)
|
||||
for e in selected[dst[selected]==target]:acc=np.float32(acc+weights[e])
|
||||
slot=(t+p['delay_steps'])%qlen;q[slot,target]=np.float32(q[slot,target]+acc)
|
||||
history[t]=v,g,ref
|
||||
return dict(step=np.array(ts,np.int32),neuron_idx=np.array(ns,np.int32),counts=counts,v=v,g=g,ref=ref,history=history,queue=q)
|
||||
|
||||
|
||||
def author_numpy(nodes,edges,config,precision,seed_node,external_arrivals=None):
|
||||
"""Analytic integration in SI volts; sequential arrival writes follow spike detection."""
|
||||
f=np.dtype(precision).type;m=config['model'];n=len(nodes);dt=config['dt_ms']
|
||||
steps=round(config['duration_ms']/dt);rs=round(m['refractory_ms']/dt);ds=round(m['synaptic_delay_ms']/dt)
|
||||
ix={int(x):i for i,x in enumerate(nodes)}
|
||||
src=np.array([ix[int(x)] for x in edges.source_neuron_idx],np.int32)
|
||||
dst=np.array([ix[int(x)] for x in edges.target_neuron_idx],np.int32)
|
||||
weights=np.asarray(edges.raw_count.to_numpy(np.float64)*(m['w_syn_mv_per_anatomical_synapse']*.001),dtype=precision)
|
||||
rest=m['resting_potential_mv']*.001;reset=m['reset_potential_mv']*.001;threshold=m['threshold_mv']*.001
|
||||
v=np.full(n,rest,dtype=precision);g=np.zeros(n,dtype=precision)
|
||||
last=np.full(n,-1000000,np.int64);counts=np.zeros(n,np.int32)
|
||||
em=np.exp(-dt/m['membrane_tau_ms']);es=np.exp(-dt/m['synaptic_tau_ms'])
|
||||
drive=m['synaptic_tau_ms']/(m['membrane_tau_ms']-m['synaptic_tau_ms'])*(em-es)
|
||||
history=np.empty((steps,3,n),np.float64);queue=[[] for _ in range(ds+1)];ts=[];ns=[]
|
||||
external=np.zeros((steps,n),dtype=precision) if external_arrivals is None else np.asarray(np.asarray(external_arrivals,np.float64)*.001,dtype=precision)
|
||||
assert external.shape==(steps,n)
|
||||
for t in range(steps):
|
||||
available=(t-last)>=rs
|
||||
# Use previous g for the coupled exact differential update, then cast to variable dtype.
|
||||
oldg=g.copy()
|
||||
v[available]=np.asarray(rest+em*(v[available]-rest)+drive*oldg[available],dtype=precision)
|
||||
g[available]=np.asarray(es*oldg[available],dtype=precision)
|
||||
if t==0 and seed_node is not None:v[ix[seed_node]]=f(threshold+config['seed_margin_mv']*.001)
|
||||
fire=available&(v>threshold);last[fire]=t;counts+=fire
|
||||
active=np.flatnonzero(fire);ts.extend([t]*len(active));ns.extend(nodes[active].tolist())
|
||||
accept=available&~fire
|
||||
due=queue[t%(ds+1)];queue[t%(ds+1)]=[]
|
||||
for e in due:
|
||||
if accept[dst[e]]:g[dst[e]]=f(g[dst[e]]+weights[e])
|
||||
for j in np.flatnonzero(external[t]):
|
||||
if accept[j]:g[j]=f(g[j]+external[t,j])
|
||||
queue[(t+ds)%(ds+1)].extend(np.flatnonzero(fire[src]).tolist())
|
||||
v[fire]=f(reset);g[fire]=f(0)
|
||||
ref=np.maximum(rs-(t-last),0).astype(np.int32)
|
||||
history[t]=np.asarray(v,np.float64)*1000,np.asarray(g,np.float64)*1000,ref
|
||||
return dict(step=np.array(ts,np.int32),neuron_idx=np.array(ns,np.int32),counts=counts,
|
||||
v=np.asarray(v,np.float64)*1000,g=np.asarray(g,np.float64)*1000,ref=ref,history=history,weights=np.asarray(weights,np.float64)*1000)
|
||||
@@ -0,0 +1,346 @@
|
||||
"""Run the pinned author's create_model in a dedicated, serial Brian2 process.
|
||||
|
||||
This module never edits the author source or its parameters. The caller supplies
|
||||
an exclusive output directory and the already validated, signed connectivity
|
||||
multipliers in ``raw_count``. Global IDs are not FlyWire IDs and are mapped only
|
||||
for this small-circuit diagnostic. Brian stores voltages in SI volts internally;
|
||||
the returned state and weights are converted to mV in the selected dtype.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import importlib.metadata
|
||||
import json
|
||||
import platform
|
||||
import sys
|
||||
import types
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
AUTHOR_SHA256 = "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e"
|
||||
AUTHOR_COMMIT = "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960"
|
||||
PACKAGE_PINS = {
|
||||
"brian2": "2.5.1", "numpy": "1.22.3", "pandas": "1.4.3",
|
||||
"scipy": "1.8.1", "pyarrow": "11.0.0", "joblib": "1.2.0",
|
||||
"cython": "0.29.33", "jinja2": "2.11.3", "markupsafe": "1.1.1",
|
||||
"sympy": "1.12", "mpmath": "1.3.0", "pyparsing": "3.0.9",
|
||||
"packaging": "23.1", "setuptools": "67.8.0",
|
||||
"python-dateutil": "2.8.2", "pytz": "2023.3", "six": "1.16.0",
|
||||
"wheel": "0.38.4",
|
||||
}
|
||||
|
||||
|
||||
def _json(path: Path, value) -> None:
|
||||
with path.open("x", encoding="utf-8") as output:
|
||||
json.dump(value, output, indent=2, sort_keys=True, allow_nan=False)
|
||||
output.write("\n")
|
||||
|
||||
|
||||
def _steps(value: float, dt: float, label: str, *, positive=False) -> int:
|
||||
ratio = float(value) / dt
|
||||
if not np.isfinite(ratio) or ratio < 0:
|
||||
raise ValueError(f"{label} must be finite and nonnegative")
|
||||
result = int(round(ratio))
|
||||
if not np.isclose(ratio, result, rtol=0, atol=1e-9):
|
||||
raise ValueError(f"{label} must be an integer number of dt steps")
|
||||
if result > np.iinfo(np.int32).max or (positive and result == 0):
|
||||
raise ValueError(f"{label} is outside the supported step range")
|
||||
return result
|
||||
|
||||
|
||||
def _int_vector(values, label: str, dtype=np.int64) -> np.ndarray:
|
||||
source = np.asarray(values)
|
||||
if source.ndim != 1 or source.dtype.kind not in "iu":
|
||||
raise ValueError(f"{label} must be a one-dimensional integer array")
|
||||
limits = np.iinfo(dtype)
|
||||
if np.any(source < limits.min) or np.any(source > limits.max):
|
||||
raise ValueError(f"{label} is outside {np.dtype(dtype).name} range")
|
||||
return source.astype(dtype, copy=True)
|
||||
|
||||
|
||||
def _environment() -> dict:
|
||||
packages = {}
|
||||
for name, expected in PACKAGE_PINS.items():
|
||||
try:
|
||||
actual = importlib.metadata.version(name)
|
||||
except importlib.metadata.PackageNotFoundError:
|
||||
actual = None
|
||||
packages[name] = {"expected": expected, "actual": actual,
|
||||
"matches": actual == expected}
|
||||
return {
|
||||
"python": platform.python_version(), "author_python": "3.10.11",
|
||||
"python_patch_matches": platform.python_version() == "3.10.11",
|
||||
"python_executable": sys.executable, "platform": platform.platform(),
|
||||
"author_platform": "Windows", "packages": packages,
|
||||
"all_package_pins_match": all(p["matches"] for p in packages.values()),
|
||||
"binary_identical_author_environment_claimed": False,
|
||||
}
|
||||
|
||||
|
||||
def _code_text(value):
|
||||
if isinstance(value, dict):
|
||||
return {str(key): _code_text(item) for key, item in value.items()}
|
||||
return str(value)
|
||||
|
||||
|
||||
def _code_records(network) -> list:
|
||||
records = []
|
||||
for obj in network.sorted_objects:
|
||||
record = {"name": obj.name, "type": type(obj).__name__,
|
||||
"when": obj.when, "order": int(obj.order),
|
||||
"active": bool(obj.active)}
|
||||
for key in ("abstract_code", "user_code"):
|
||||
if hasattr(obj, key):
|
||||
record[key] = _code_text(getattr(obj, key))
|
||||
record["code_objects"] = [
|
||||
{"name": code.name, "type": type(code).__name__,
|
||||
"generated_code": _code_text(code.code)}
|
||||
for code in getattr(obj, "code_objects", ())
|
||||
]
|
||||
records.append(record)
|
||||
return records
|
||||
|
||||
|
||||
def run_brian(author_path: Path, io_dir: Path, nodes: np.ndarray,
|
||||
edges: pd.DataFrame, config: dict, precision: str,
|
||||
seed_node: int | None, external_arrivals=None) -> dict:
|
||||
"""Execute an unchanged author model and return canonical end-of-tick arrays.
|
||||
|
||||
``external_arrivals`` is either None or a (T, N) array in mV. Every nonzero
|
||||
entry gets its own SpikeGeneratorGroup source and zero-delay conditional
|
||||
synaptic write, ordered after the anatomical synapses. It is not direct
|
||||
Python assignment to g. ``seed_margin_mv`` may be zero for a strict-threshold
|
||||
fixture; positive margins must remain representably above the threshold.
|
||||
|
||||
This function controls process-global Brian preferences and is not thread
|
||||
safe. Use only the isolated oracle environment, serially or in subprocesses.
|
||||
A failed run keeps its exclusive inputs and an error record for diagnosis.
|
||||
"""
|
||||
if precision not in ("float64", "float32"):
|
||||
raise ValueError("precision must be float64 or float32")
|
||||
dtype = np.dtype(precision)
|
||||
nodes = _int_vector(nodes, "nodes", np.int32)
|
||||
if not len(nodes) or np.any(np.diff(nodes.astype(np.int64)) <= 0):
|
||||
raise ValueError("nodes must be nonempty, sorted, and unique")
|
||||
required = ["edge_id", "source_neuron_idx", "target_neuron_idx", "raw_count"]
|
||||
if not set(required).issubset(edges.columns):
|
||||
raise ValueError(f"edges require columns {required}")
|
||||
edge_ids = _int_vector(edges.edge_id, "edge_id")
|
||||
if np.any(np.diff(edge_ids) <= 0):
|
||||
raise ValueError("edge_id order must be canonical, increasing, and unique")
|
||||
sources = _int_vector(edges.source_neuron_idx, "source_neuron_idx", np.int32)
|
||||
targets = _int_vector(edges.target_neuron_idx, "target_neuron_idx", np.int32)
|
||||
multipliers = _int_vector(edges.raw_count, "raw_count")
|
||||
if not np.isin(sources, nodes).all() or not np.isin(targets, nodes).all():
|
||||
raise ValueError("all edge endpoints must occur in nodes")
|
||||
lookup = {int(node): i for i, node in enumerate(nodes)}
|
||||
if seed_node is not None and int(seed_node) not in lookup:
|
||||
raise ValueError("seed_node must occur in nodes")
|
||||
|
||||
dt_ms = float(config["dt_ms"])
|
||||
if not np.isfinite(dt_ms) or dt_ms <= 0:
|
||||
raise ValueError("dt_ms must be finite and positive")
|
||||
total_steps = _steps(config["duration_ms"], dt_ms, "duration_ms", positive=True)
|
||||
model = config["model"]
|
||||
refractory_steps = _steps(model["refractory_ms"], dt_ms, "refractory_ms")
|
||||
delay_steps = _steps(model["synaptic_delay_ms"], dt_ms, "synaptic_delay_ms")
|
||||
margin_mv = float(config["seed_margin_mv"])
|
||||
if not np.isfinite(margin_mv) or margin_mv < 0:
|
||||
raise ValueError("seed_margin_mv must be finite and nonnegative")
|
||||
if "dt_ms" in model and float(model["dt_ms"]) != dt_ms:
|
||||
raise ValueError("top-level and model dt_ms disagree")
|
||||
arrivals = None
|
||||
if external_arrivals is not None:
|
||||
arrivals = np.asarray(external_arrivals, dtype=np.float64)
|
||||
if arrivals.shape != (total_steps, len(nodes)) or not np.isfinite(arrivals).all():
|
||||
raise ValueError("external_arrivals must be finite with shape (T, N)")
|
||||
|
||||
author_path = Path(author_path).resolve(strict=True)
|
||||
author_bytes = author_path.read_bytes()
|
||||
author_hash = hashlib.sha256(author_bytes).hexdigest()
|
||||
if author_hash != AUTHOR_SHA256:
|
||||
raise ValueError(f"Author source hash mismatch: {author_hash}")
|
||||
io_dir = Path(io_dir).resolve()
|
||||
io_dir.mkdir(parents=True, exist_ok=False)
|
||||
environment = _environment()
|
||||
_json(io_dir / "environment.json", environment)
|
||||
if environment["packages"]["brian2"]["actual"] != "2.5.1":
|
||||
raise RuntimeError("The author oracle requires exactly Brian2 2.5.1")
|
||||
|
||||
import brian2 as b2
|
||||
|
||||
saved_preferences = {
|
||||
"codegen.target": b2.prefs["codegen.target"],
|
||||
"core.default_float_dtype": b2.prefs["core.default_float_dtype"],
|
||||
"legacy.refractory_timing": b2.prefs["legacy.refractory_timing"],
|
||||
}
|
||||
saved_dt = b2.defaultclock.dt
|
||||
try:
|
||||
b2.start_scope()
|
||||
b2.prefs["codegen.target"] = "numpy"
|
||||
b2.prefs["core.default_float_dtype"] = dtype.type
|
||||
b2.prefs["legacy.refractory_timing"] = False
|
||||
b2.defaultclock.dt = dt_ms * b2.ms
|
||||
# Compile the checked bytes themselves: no source rewrite or __pycache__
|
||||
# is possible, and a subsequent file change cannot alter imported code.
|
||||
author = types.ModuleType("phase4ad_pinned_author_" + author_hash[:12])
|
||||
author.__file__ = str(author_path)
|
||||
exec(compile(author_bytes, str(author_path), "exec"), author.__dict__)
|
||||
parameters = dict(author.default_params)
|
||||
checks = {}
|
||||
for config_key, author_key, unit in (
|
||||
("membrane_tau_ms", "t_mbr", b2.ms),
|
||||
("synaptic_tau_ms", "tau", b2.ms),
|
||||
("refractory_ms", "t_rfc", b2.ms),
|
||||
("synaptic_delay_ms", "t_dly", b2.ms),
|
||||
("resting_potential_mv", "v_0", b2.mV),
|
||||
("reset_potential_mv", "v_rst", b2.mV),
|
||||
("threshold_mv", "v_th", b2.mV),
|
||||
("w_syn_mv_per_anatomical_synapse", "w_syn", b2.mV),
|
||||
):
|
||||
expected = float(model[config_key])
|
||||
actual = float(parameters[author_key] / unit)
|
||||
if not np.isfinite(expected) or not np.isclose(actual, expected, rtol=0, atol=1e-12):
|
||||
raise ValueError(f"config {config_key}={expected} differs from author {actual}")
|
||||
checks[config_key] = {"config": expected, "author": actual}
|
||||
|
||||
completeness = pd.DataFrame({"local_index": np.arange(len(nodes), dtype=np.int32)},
|
||||
index=pd.Index(nodes, name="global_neuron_idx"))
|
||||
connections = pd.DataFrame({
|
||||
"Presynaptic_Index": [lookup[int(x)] for x in sources],
|
||||
"Postsynaptic_Index": [lookup[int(x)] for x in targets],
|
||||
"Excitatory x Connectivity": multipliers,
|
||||
"edge_id": edge_ids,
|
||||
"source_neuron_idx": sources, "target_neuron_idx": targets,
|
||||
}).astype({"Presynaptic_Index": "int32", "Postsynaptic_Index": "int32"})
|
||||
comp_path = io_dir / "completeness.csv"
|
||||
con_path = io_dir / "connectivity.parquet"
|
||||
completeness.to_csv(comp_path)
|
||||
connections.to_parquet(con_path, index=False, engine="pyarrow")
|
||||
neu, syn, spikes = author.create_model(comp_path, con_path, parameters)
|
||||
if np.dtype(neu.variables["v"].dtype) != dtype or np.dtype(syn.variables["w"].dtype) != dtype:
|
||||
raise AssertionError("Brian did not allocate the requested floating-point dtype")
|
||||
np.testing.assert_array_equal(np.asarray(syn.i[:]), connections.Presynaptic_Index)
|
||||
np.testing.assert_array_equal(np.asarray(syn.j[:]), connections.Postsynaptic_Index)
|
||||
|
||||
objects = [neu, syn, spikes]
|
||||
seed_state = {"requested_global_node": None if seed_node is None else int(seed_node),
|
||||
"margin_mv": margin_mv, "injections": 0}
|
||||
if seed_node is not None:
|
||||
seed_local = lookup[int(seed_node)]
|
||||
|
||||
def inject_seed():
|
||||
if seed_state["injections"] == 0:
|
||||
if float(b2.defaultclock.t / b2.ms) != 0:
|
||||
raise AssertionError("Seed injection must occur at tick zero")
|
||||
neu.v[seed_local] = parameters["v_th"] + margin_mv * b2.mV
|
||||
applied = float(neu.v[seed_local] / b2.mV)
|
||||
if margin_mv > 0 and not applied > float(parameters["v_th"] / b2.mV):
|
||||
raise ValueError("The positive seed margin rounded away in the selected dtype")
|
||||
seed_state["applied_mv"] = applied
|
||||
seed_state["injections"] = 1
|
||||
|
||||
objects.append(b2.NetworkOperation(inject_seed, clock=neu.clock,
|
||||
when="before_thresholds", order=0,
|
||||
name="oracle_seed_injection"))
|
||||
|
||||
external_metadata = {"events": 0, "ordering": "after default_synapses.pre"}
|
||||
if arrivals is not None:
|
||||
event_ticks, event_targets = np.nonzero(arrivals)
|
||||
event_values = arrivals[event_ticks, event_targets]
|
||||
external_metadata["events"] = int(len(event_ticks))
|
||||
if len(event_ticks):
|
||||
event_ids = np.arange(len(event_ticks), dtype=np.int32)
|
||||
external_source = b2.SpikeGeneratorGroup(
|
||||
len(event_ticks), indices=event_ids,
|
||||
times=event_ticks.astype(np.float64) * dt_ms * b2.ms,
|
||||
sorted=True, clock=neu.clock, name="zz_external_sources")
|
||||
external_syn = b2.Synapses(
|
||||
external_source, neu, model="w : volt", on_pre="g_post += w",
|
||||
delay=0 * b2.ms, name="zz_external_arrivals")
|
||||
external_syn.connect(i=event_ids, j=event_targets)
|
||||
external_syn.w = event_values * b2.mV
|
||||
external_syn.pre.order = int(syn.pre.order) + 1
|
||||
if external_syn.pre.when != syn.pre.when:
|
||||
raise AssertionError("External and anatomical pathways must share a schedule slot")
|
||||
objects.extend([external_source, external_syn])
|
||||
external_metadata["pathway_order"] = int(external_syn.pre.order)
|
||||
external_metadata["actual_weights_mv"] = np.asarray(external_syn.w[:] / b2.mV, dtype=dtype).tolist()
|
||||
pd.DataFrame({"tick": event_ticks, "neuron_idx": nodes[event_targets],
|
||||
"requested_mv": event_values}).to_csv(io_dir / "external-arrivals.csv", index=False)
|
||||
|
||||
trace = b2.StateMonitor(neu, ["v", "g", "lastspike", "not_refractory"],
|
||||
record=True, when="end", name="oracle_end_state")
|
||||
objects.append(trace)
|
||||
network = b2.Network(*objects)
|
||||
_json(io_dir / "run-spec.json", {
|
||||
"author_sha256": author_hash, "author_commit": AUTHOR_COMMIT,
|
||||
"precision": precision, "config": config, "parameter_checks": checks,
|
||||
"dt_ms": dt_ms, "steps": total_steps, "refractory_steps": refractory_steps,
|
||||
"delay_steps": delay_steps, "nodes": nodes.tolist(), "edge_ids": edge_ids.tolist(),
|
||||
"seed": seed_state.copy(), "external_arrivals": external_metadata,
|
||||
"preferences": {"codegen.target": "numpy", "legacy.refractory_timing": False,
|
||||
"core.default_float_dtype": precision},
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference",
|
||||
"internal_voltage_unit": "volt", "returned_voltage_unit": "mV",
|
||||
})
|
||||
_json(io_dir / "schedule.json", {"schedule": list(network.schedule),
|
||||
"summary": str(network.scheduling_summary())})
|
||||
network.run(float(config["duration_ms"]) * b2.ms, namespace={})
|
||||
_json(io_dir / "generated-code.json", _code_records(network))
|
||||
|
||||
ticks_float = np.asarray(spikes.t[:] / b2.ms, dtype=np.float64) / dt_ms
|
||||
ticks = np.rint(ticks_float).astype(np.int32)
|
||||
if not np.allclose(ticks_float, ticks, rtol=0, atol=1e-6):
|
||||
raise AssertionError("Spike timestamps do not lie on the configured integer clock")
|
||||
event_nodes = nodes[np.asarray(spikes.i[:], dtype=np.int32)]
|
||||
order = np.lexsort((event_nodes, ticks))
|
||||
ticks, event_nodes = ticks[order], event_nodes[order]
|
||||
counts = np.asarray(spikes.count[:], dtype=np.int32).copy()
|
||||
if int(counts.sum()) != len(ticks):
|
||||
raise AssertionError("SpikeMonitor counts disagree with event records")
|
||||
trace_ticks = np.asarray(trace.t[:] / b2.ms, dtype=np.float64) / dt_ms
|
||||
if trace_ticks.shape != (total_steps,) or not np.allclose(
|
||||
trace_ticks, np.arange(total_steps), rtol=0, atol=1e-6):
|
||||
raise AssertionError("End-state trace has the wrong tick range")
|
||||
voltage = np.asarray(trace.v / b2.mV, dtype=dtype).T.copy()
|
||||
conductance = np.asarray(trace.g / b2.mV, dtype=dtype).T.copy()
|
||||
last_spike_ms = np.asarray(trace.lastspike / b2.ms, dtype=np.float64).T
|
||||
seen = np.isfinite(last_spike_ms) & (last_spike_ms >= 0)
|
||||
# Brian's never-spiked sentinel can be far outside int32. Convert only
|
||||
# real nonnegative timestamps, then compute an end-of-tick countdown.
|
||||
last_ticks = np.zeros(last_spike_ms.shape, dtype=np.int64)
|
||||
last_ticks[seen] = np.rint(last_spike_ms[seen] / dt_ms).astype(np.int64)
|
||||
elapsed = np.arange(total_steps, dtype=np.int64)[:, None] - last_ticks
|
||||
ref = np.where(seen, np.maximum(refractory_steps - elapsed, 0), 0).astype(np.int32)
|
||||
available = np.asarray(trace.not_refractory, dtype=bool).T
|
||||
if not np.array_equal(available, ref == 0):
|
||||
raise AssertionError("Recorded refractory flag disagrees with the integer countdown")
|
||||
history = np.stack([voltage, conductance, ref.astype(dtype)], axis=1)
|
||||
if not np.isfinite(history).all():
|
||||
raise AssertionError("Nonfinite author state")
|
||||
if seed_node is not None:
|
||||
seed_state["spiked_at_zero"] = bool(np.any((ticks == 0) & (event_nodes == seed_node)))
|
||||
if seed_state["injections"] != 1 or (margin_mv > 0 and not seed_state["spiked_at_zero"]):
|
||||
raise AssertionError("The common initial seed event was not produced exactly once")
|
||||
_json(io_dir / "seed.json", seed_state)
|
||||
weights = np.asarray(syn.w[:] / b2.mV, dtype=dtype).copy()
|
||||
_json(io_dir / "actual-weights.json", {
|
||||
"edge_id": edge_ids.tolist(), "weights_mv": weights.tolist(),
|
||||
"weights_volt": np.asarray(syn.w_[:], dtype=np.float64).tolist(),
|
||||
})
|
||||
if hashlib.sha256(author_path.read_bytes()).hexdigest() != author_hash:
|
||||
raise AssertionError("Author source changed during execution")
|
||||
return {"step": ticks, "neuron_idx": event_nodes, "counts": counts,
|
||||
"v": voltage[-1].copy(), "g": conductance[-1].copy(),
|
||||
"ref": ref[-1].copy(), "history": history, "weights": weights}
|
||||
except Exception as error:
|
||||
_json(io_dir / "error.json", {"type": type(error).__name__, "message": str(error)})
|
||||
raise
|
||||
finally:
|
||||
for name, value in saved_preferences.items():
|
||||
b2.prefs[name] = value
|
||||
b2.defaultclock.dt = saved_dt
|
||||
@@ -0,0 +1,56 @@
|
||||
"""Predeclare and verify old inputs before executing any alternative dynamics."""
|
||||
import ast
|
||||
from datetime import datetime, timezone
|
||||
import importlib.metadata
|
||||
import json
|
||||
import platform
|
||||
from pathlib import Path
|
||||
import shutil
|
||||
import subprocess
|
||||
import sys
|
||||
from context import CFG,ROOT,EXP,MODEL,AC,digest,save,source_hashes
|
||||
|
||||
|
||||
def frozen_integrity():
|
||||
sys.path.insert(0,str(ROOT/CFG['prior_model']))
|
||||
import audit_semantics as old
|
||||
result=old.integrity()
|
||||
for p in (ROOT/CFG['prior_model'],AC,ROOT/'registry/feedback-context-review-20260930'):
|
||||
result[str(p.relative_to(ROOT))]=old.manifest_check(p,digest)
|
||||
assert all(x['pass'] for x in result.values())
|
||||
return result
|
||||
|
||||
|
||||
def main():
|
||||
assert (MODEL/'oracle_runner.py').is_file()
|
||||
assert not (EXP/'protocol').exists(),'Refusing reused experiment protocol'
|
||||
for d in ('protocol','external-resources','inputs','components','oracle','regression','audit','performance'):(EXP/d).mkdir()
|
||||
source=AC/'external-resources/model.py'
|
||||
assert digest(source)=='fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e'
|
||||
tree=ast.parse(source.read_text())
|
||||
assert all(isinstance(x,(ast.Import,ast.ImportFrom,ast.Assign,ast.FunctionDef,ast.Expr)) for x in tree.body)
|
||||
inputs=[source,AC/'external-resources/LICENSE',AC/'external-resources/environment_full.yml',
|
||||
AC/'audit/selected-real-edges.csv',AC/'audit/selected-metadata.parquet',ROOT/CFG['prior_model']/'math_reference.py']
|
||||
for name in CFG['motifs']:
|
||||
inputs.extend([AC/'motifs'/f'{name}-edges.csv',AC/'motifs'/f'{name}-events.parquet',AC/'motifs'/f'{name}-state.npz'])
|
||||
save(EXP/'protocol/preregistered.json',dict(config=CFG,timestamp_utc=datetime.now(timezone.utc).isoformat(),
|
||||
source_hashes=source_hashes(),input_hashes=[dict(path=str(p.relative_to(ROOT)),sha256=digest(p),bytes=p.stat().st_size) for p in inputs],
|
||||
parent_commit=subprocess.check_output(['git','-C',str(ROOT),'rev-parse','HEAD'],text=True).strip(),
|
||||
pre_model_execution=True))
|
||||
save(EXP/'regression/old-integrity-before.json',frozen_integrity())
|
||||
for name in ('model.py','LICENSE','environment_full.yml'):
|
||||
dst=EXP/'external-resources'/name
|
||||
with (AC/'external-resources'/name).open('rb') as src,dst.open('xb') as out:shutil.copyfileobj(src,out)
|
||||
assert digest(dst)==digest(AC/'external-resources'/name)
|
||||
versions={n:importlib.metadata.version(n) for n in ('numpy','pandas','pyarrow','torch','scipy')}
|
||||
save(EXP/'environment/frozen-scientific-environment.json',dict(python=sys.version,executable=sys.executable,
|
||||
platform=platform.platform(),package_versions=versions))
|
||||
save(EXP/'environment/bootstrap-notes.json',dict(isolated_venv_only=True,existing_environments_modified=False,
|
||||
initial_install_failure='New venv pip22.0.2 lacks --report; upgraded ONLY new venv pip to author-pinned23.0.1 before installing',
|
||||
MarkupSafe='Author-pinned1.1.1 used PyPI source distribution and legacy setup.py because no CPython3.10 wheel; other requested core packages used wheels',
|
||||
author_environment='Windows conda CPython3.10.11',actual_environment='Linux CPython3.10.12; not binary-identical',
|
||||
package_download_provenance='install-report.json contains HTTPS PyPI archive URLs and SHA256 for packages'))
|
||||
print('PREPARED: old integrity PASS; independent model hypotheses preregistered',flush=True)
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,20 @@
|
||||
# Package versions in the pinned author's environment_full.yml, Linux wheels.
|
||||
# The host Python patch is recorded separately; this is not the author's Windows binary environment.
|
||||
brian2==2.5.1
|
||||
numpy==1.22.3
|
||||
pandas==1.4.3
|
||||
scipy==1.8.1
|
||||
pyarrow==11.0.0
|
||||
joblib==1.2.0
|
||||
cython==0.29.33
|
||||
jinja2==2.11.3
|
||||
markupsafe==1.1.1
|
||||
sympy==1.12
|
||||
mpmath==1.3.0
|
||||
pyparsing==3.0.9
|
||||
packaging==23.1
|
||||
setuptools==67.8.0
|
||||
python-dateutil==2.8.2
|
||||
pytz==2023.3
|
||||
six==1.16.0
|
||||
wheel==0.38.4
|
||||
@@ -0,0 +1,158 @@
|
||||
"""Run fixed component arms and independent author oracle, preserving divergences."""
|
||||
import importlib.metadata
|
||||
import json
|
||||
from pathlib import Path
|
||||
import platform
|
||||
import sys
|
||||
import subprocess
|
||||
import time
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from context import CFG,ROOT,EXP,MODEL,AC,digest,save,check_prereg
|
||||
from math_lab import component,author_numpy
|
||||
from oracle_runner import run_brian,PACKAGE_PINS
|
||||
|
||||
|
||||
def save_result(where,result,nodes):
|
||||
where.mkdir(parents=True,exist_ok=False)
|
||||
pd.DataFrame(dict(step=result['step'],neuron_idx=result['neuron_idx'])).to_csv(where/'events.csv',index=False)
|
||||
np.savez_compressed(where/'state.npz',nodes=nodes,**{k:v for k,v in result.items() if k not in ('step','neuron_idx')})
|
||||
|
||||
|
||||
def compare(actual,expected,precision):
|
||||
event=bool(np.array_equal(actual['step'],expected['step']) and np.array_equal(actual['neuron_idx'],expected['neuron_idx']))
|
||||
a=np.asarray(actual['history'],np.float64);b=np.asarray(expected['history'],np.float64)
|
||||
assert a.shape==b.shape
|
||||
errs=np.abs(a[:,:2]-b[:,:2])
|
||||
# Describe numerical prefix separately from post-event-divergence trajectories.
|
||||
first=None
|
||||
for t in range(len(a)):
|
||||
aa=actual['neuron_idx'][actual['step']==t];bb=expected['neuron_idx'][expected['step']==t]
|
||||
if not np.array_equal(aa,bb):first=t;break
|
||||
prefix=errs[:first] if first is not None else errs
|
||||
tol=CFG['comparison'][precision+'_state_max_abs_tolerance_mv']
|
||||
return dict(exact_events=event,exact_counts=bool(np.array_equal(actual['counts'],expected['counts'])),
|
||||
ref_exact=bool(np.array_equal(a[:,2],b[:,2])),max_abs_mv=float(errs.max()),mean_abs_mv=float(errs.mean()),
|
||||
same_event_prefix_max_abs_mv=float(prefix.max()) if prefix.size else None,
|
||||
first_event_divergence_step=first,tolerance_mv=tol,
|
||||
pass_oracle=bool(event and errs.max()<=tol and np.array_equal(a[:,2],b[:,2])))
|
||||
|
||||
|
||||
def oracle_trial(name,nodes,edges,cfg,precision,seed,external=None):
|
||||
io=EXP/'oracle'/(name+'-'+precision)
|
||||
result=run_brian(EXP/'external-resources/model.py',io,nodes,edges,cfg,precision,seed,external)
|
||||
independent=author_numpy(nodes,edges,cfg,precision,seed,external)
|
||||
comparison=compare(result,independent,precision)
|
||||
save(io/'comparison.json',comparison)
|
||||
save_result(io/'brian-output',result,nodes);save_result(io/'independent-output',independent,nodes)
|
||||
assert np.isfinite(result['history']).all() and np.isfinite(independent['history']).all()
|
||||
return result,comparison
|
||||
|
||||
|
||||
def unit_fixtures():
|
||||
rows=[];nodes=np.array([-1],np.int32)
|
||||
# Brian2 2.5.1 calls max/min on connect arrays and rejects zero-length arrays.
|
||||
# An explicitly synthetic zero-weight self-edge is inert, not an anatomical edge.
|
||||
inert=pd.DataFrame(dict(edge_id=[-1],source_neuron_idx=[-1],target_neuron_idx=[-1],raw_count=[0]),dtype='int64')
|
||||
for precision in CFG['author_precisions']:
|
||||
# Synthetic fixture indices are negative and are not MaleCNS body IDs.
|
||||
c=dict(CFG,duration_ms=20.0)
|
||||
cases={}
|
||||
a=np.zeros((200,1));cases['zero']=(a,None,1.0)
|
||||
a=np.zeros((200,1));a[0,0]=1.;cases['weak_excitation']=(a,None,1.0)
|
||||
a=np.zeros((200,1));a[0,0]=-1.;cases['inhibition']=(a,None,1.0)
|
||||
a=np.zeros((200,1));a[[0,21,22,23],0]=.1;cases['refractory_boundary']=(a,-1,1.0)
|
||||
a=np.zeros((200,1));cases['equal_threshold']=(a,-1,0.0)
|
||||
a=np.zeros((200,1));cases['above_threshold']=(a,-1,1.0)
|
||||
a=np.zeros((200,1));a[0,0]=2000.;cases['nonzero_g_reset']=(a,None,1.0)
|
||||
for name,(arr,seed,margin) in cases.items():
|
||||
cfg=dict(c,seed_margin_mv=margin)
|
||||
r,cmp=oracle_trial('unit-'+name,nodes,inert,cfg,precision,seed,arr)
|
||||
h=r['history'];events=r['step'];checks={}
|
||||
if name=='zero':checks={'no_events':len(events)==0,'g_zero':bool(np.all(h[:,1]==0)),
|
||||
'rest_stationary':bool(np.max(np.abs(h[:,0]-CFG['model']['resting_potential_mv']))<1e-3)}
|
||||
elif name in ('weak_excitation','inhibition'):
|
||||
checks={'no_events':len(events)==0,'arrival_at_zero':bool(h[0,1,0]>0 if name=='weak_excitation' else h[0,1,0]<0),
|
||||
'integration_next_step':bool(h[1,0,0]>h[0,0,0] if name=='weak_excitation' else h[1,0,0]<h[0,0,0])}
|
||||
elif name=='refractory_boundary':checks={'one_seed':np.array_equal(events,[0]),'reject_through_21':bool(np.all(h[:22,1]==0)),
|
||||
'accept_at_22':bool(h[22,1,0]>0),'release_at_22':bool(h[21,2,0]==1 and h[22,2,0]==0),'reset_g_at_zero':h[0,1,0]==0}
|
||||
elif name=='equal_threshold':checks={'strict_threshold_no_event':len(events)==0}
|
||||
elif name=='nonzero_g_reset':checks={'natural_spike_at_1':np.array_equal(events,[1]),
|
||||
'nonzero_g_before_spike':bool(h[0,1,0]>1999),'zero_g_after_spike':bool(np.all(h[1:,1,0]==0))}
|
||||
else:checks={'single_seed':np.array_equal(events,[0]),'reset_g':bool(h[0,1,0]==0)}
|
||||
row=dict(fixture=name,precision=precision,checks={k:bool(v) for k,v in checks.items()},comparison=cmp)
|
||||
rows.append(row);save(EXP/'regression'/('unit-'+name+'-'+precision+'.json'),row)
|
||||
print(json.dumps(dict(unit=name,precision=precision,checks=all(checks.values()),oracle=cmp)),flush=True)
|
||||
delay_nodes=np.array([-2,-1],np.int32)
|
||||
weak_edge=pd.DataFrame(dict(edge_id=[-1],source_neuron_idx=[-2],target_neuron_idx=[-1],raw_count=[1]),dtype='int64')
|
||||
r,cmp=oracle_trial('unit-delay_18',delay_nodes,weak_edge,c,precision,-2)
|
||||
h=r['history'];checks=dict(one_seed=np.array_equal(r['step'],[0]),
|
||||
zero_before_arrival=bool(np.all(h[:18,1,1]==0)),arrival_at_18=bool(h[18,1,1]>0),
|
||||
first_voltage_effect_at_19=bool(h[19,0,1]>h[18,0,1]))
|
||||
row=dict(fixture='delay_18',precision=precision,checks=checks,comparison=cmp)
|
||||
rows.append(row);save(EXP/'regression'/('unit-delay_18-'+precision+'.json'),row)
|
||||
save(EXP/'regression/unit-fixtures.json',rows)
|
||||
# Unit oracle failures prevent real author-motif execution; preserve all failed traces.
|
||||
assert all(all(r['checks'].values()) and r['comparison']['pass_oracle'] for r in rows),'Author operator unit gate failed'
|
||||
|
||||
|
||||
def main():
|
||||
check_prereg();assert not (EXP/'audit/run-start.json').exists(),'Refusing overwritten run'
|
||||
assert all(importlib.metadata.version(n)==v for n,v in PACKAGE_PINS.items()),'Dependency-pin mismatch'
|
||||
save(EXP/'environment/installed-packages.json',json.loads(subprocess.check_output([sys.executable,'-m','pip','list','--format=json'],text=True)))
|
||||
save(EXP/'audit/run-start.json',dict(python=sys.version,platform=platform.platform(),
|
||||
packages={n:importlib.metadata.version(n) for n in ('brian2','numpy','pandas','scipy','pyarrow','sympy','cython')},
|
||||
codegen='numpy',learning=False,user_authorized_new_model_hypotheses=True))
|
||||
t0=time.perf_counter();rows=[];validations=[]
|
||||
for name in CFG['motifs']:
|
||||
edges=pd.read_csv(AC/'motifs'/f'{name}-edges.csv').sort_values('edge_id')
|
||||
old=np.load(AC/'motifs'/f'{name}-state.npz');nodes=old['nodes'];seed=None if name=='no_input' else CFG['seed_node']
|
||||
weights=edges.raw_count.to_numpy(np.float32)*np.float32(CFG['model']['w_syn_mv_per_anatomical_synapse'])
|
||||
for policy in CFG['component_policies']:
|
||||
r=component(nodes,edges,weights,CFG,policy,seed)
|
||||
assert np.isfinite(r['history']).all()
|
||||
if policy=='frozen':
|
||||
ev=pd.read_parquet(AC/'motifs'/f'{name}-events.parquet')
|
||||
eq={k:bool(np.array_equal(old[k],r[k])) for k in old.files if k!='nodes'}
|
||||
eq['events']=bool(np.array_equal(ev.step,r['step']) and np.array_equal(ev.neuron_idx,r['neuron_idx']))
|
||||
assert all(eq.values()),eq
|
||||
validations.append(dict(motif=name,frozen_vs_4AC_exact=eq))
|
||||
save_result(EXP/'components'/(name+'-'+policy),r,nodes)
|
||||
late=r['step']>=round(CFG['late_start_ms']/CFG['dt_ms'])
|
||||
for node in nodes:
|
||||
select=r['neuron_idx']==node
|
||||
rows.append(dict(motif=name,policy=policy,precision='float32',neuron_idx=int(node),spikes=int(select.sum()),
|
||||
late_spikes=int((select&late).sum()),last_ms=float(r['step'][select][-1]*CFG['dt_ms']) if select.any() else None))
|
||||
pd.DataFrame(rows).to_csv(EXP/'components/summary.csv',index=False)
|
||||
save(EXP/'regression/frozen-components.json',validations)
|
||||
unit_fixtures()
|
||||
from interface_audit import audit_author_poi
|
||||
audit_author_poi()
|
||||
ors=[]
|
||||
for name in CFG['motifs']:
|
||||
edges=pd.read_csv(AC/'motifs'/f'{name}-edges.csv').sort_values('edge_id')
|
||||
nodes=np.load(AC/'motifs'/f'{name}-state.npz')['nodes'];seed=None if name=='no_input' else CFG['seed_node'];by_precision={}
|
||||
for precision in CFG['author_precisions']:
|
||||
r,cmp=oracle_trial(name,nodes,edges,CFG,precision,seed);by_precision[precision]=r
|
||||
repeat=run_brian(EXP/'external-resources/model.py',EXP/'oracle'/(name+'-'+precision+'-repeat'),nodes,edges,CFG,precision,seed)
|
||||
exact_repeat={k:bool(np.array_equal(r[k],repeat[k])) for k in r}
|
||||
save_result(EXP/'oracle'/(name+'-'+precision+'-repeat')/'brian-output',repeat,nodes)
|
||||
save(EXP/'regression'/(name+'-'+precision+'-repeat.json'),exact_repeat)
|
||||
assert all(exact_repeat.values()),exact_repeat
|
||||
late=r['step']>=round(CFG['late_start_ms']/CFG['dt_ms'])
|
||||
for node in nodes:
|
||||
chosen=r['neuron_idx']==node
|
||||
rows.append(dict(motif=name,policy='author_complete',precision=precision,neuron_idx=int(node),
|
||||
spikes=int(chosen.sum()),late_spikes=int((chosen&late).sum()),last_ms=float(r['step'][chosen][-1]*CFG['dt_ms']) if chosen.any() else None))
|
||||
ors.append(dict(motif=name,precision=precision,comparison=cmp))
|
||||
print(json.dumps(dict(motif=name,precision=precision,events=len(r['step']),late=int(late.sum()),oracle=cmp)),flush=True)
|
||||
save(EXP/'regression'/(name+'-precision-cross-comparison.json'),compare(by_precision['float32'],by_precision['float64'],'float32'))
|
||||
pd.DataFrame(rows).to_csv(EXP/'audit/all-motif-results.csv',index=False)
|
||||
save(EXP/'regression/author-motifs.json',ors)
|
||||
save(EXP/'performance/summary.json',dict(wall_seconds=time.perf_counter()-t0,backend='CPU NumPy + Brian2',not_runtime_benchmark=True))
|
||||
save(EXP/'audit/run-complete.json',dict(all_author_oracle_comparisons_pass=all(x['comparison']['pass_oracle'] for x in ors),
|
||||
all_frozen_components_match_archive=True,learning_achieved=False,whole_brain_alternative_run=False,
|
||||
author_original_source_sha256=digest(EXP/'external-resources/model.py')))
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
@@ -0,0 +1,47 @@
|
||||
"""Unchanged deterministic-v2 five-trial regression; old outputs are read only."""
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import yaml
|
||||
from context import ROOT,EXP,CFG,save,check_prereg
|
||||
|
||||
|
||||
def main():
|
||||
check_prereg()
|
||||
assert not (EXP/'regression/frozen-five.json').exists()
|
||||
runtime=ROOT/'models/male-cns-lif/runtime-v2-deterministic-candidate'
|
||||
# Discover the exact prior-audit runtime path from its immutable config.
|
||||
prior=yaml.safe_load((ROOT/CFG['prior_model']/'config.yaml').read_text())
|
||||
runtime=ROOT/prior['runtime'];pc=yaml.safe_load((runtime/'phase4p.yaml').read_text())
|
||||
sys.path.insert(0,str(ROOT/'models/male-cns-lif/terminal-scope-diagnostic-v1'))
|
||||
from diagnostic import queues
|
||||
queues()
|
||||
os.environ['CUDA_VISIBLE_DEVICES']=pc['device']['visible_gpu_uuid']
|
||||
os.environ['CUDA_HOME']='/usr/local/cuda';os.environ['MAX_JOBS']='2';os.environ['TORCH_CUDA_ARCH_LIST']='8.9'
|
||||
os.environ['PATH']=str(ROOT/'models/male-cns-lif/.venv/bin')+':/usr/local/cuda/bin:'+os.environ['PATH']
|
||||
sys.path.insert(0,str(ROOT/'models/male-cns-lif'));sys.path.insert(0,str(runtime))
|
||||
import torch,phase4p
|
||||
free,total=torch.cuda.mem_get_info();assert free>2*1024**3
|
||||
torch.cuda.reset_peak_memory_stats();t0=time.perf_counter()
|
||||
rt=phase4p.CanonicalRuntime(pc,runtime,EXP)
|
||||
rows=[]
|
||||
for spec in pc['reference_trials']:
|
||||
queues();base=ROOT/'experiments/phase4p-deterministic-runtime/phase4c-v2/reference'/spec['name']
|
||||
ev=pd.read_parquet(base/'spike-events.parquet')
|
||||
expected=dict(step=ev.step.to_numpy(),neuron_idx=ev.neuron_idx.to_numpy(),
|
||||
counts=np.load(base/'per-neuron-spike-counts.int32.npy'),
|
||||
v=np.load(base/'final-voltage.float32.npy'),g=np.load(base/'final-g.float32.npy'))
|
||||
r=rt.run(np.flatnonzero(rt.pops[spec['population']]),spec['rate_hz'],spec['seed'],spec['duration_ms'])
|
||||
eq={k:bool(np.array_equal(r[k],v)) for k,v in expected.items()}
|
||||
row=dict(trial=spec['name'],exact=eq,wall_seconds=r['metrics']['wall_seconds'])
|
||||
rows.append(row);save(EXP/'regression'/('frozen-'+spec['name']+'.json'),row)
|
||||
print(row,flush=True)
|
||||
save(EXP/'regression/frozen-five.json',dict(pass_all=all(all(x['exact'].values()) for x in rows),trials=rows,
|
||||
oracle='frozen deterministic-v2, NOT author Brian2',wall_seconds=time.perf_counter()-t0,
|
||||
peak_allocated_vram=int(torch.cuda.max_memory_allocated()),gpu=torch.cuda.get_device_name(),comfyui_modified=False))
|
||||
assert all(all(x['exact'].values()) for x in rows)
|
||||
|
||||
|
||||
if __name__=='__main__':main()
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,71 @@
|
||||
# Phase 4AD 作者动力学语义实验室
|
||||
|
||||
状态：`COMPLETE-ORACLE-VALIDATED-LAB-ONLY`。
|
||||
|
||||
本轮仅有预声明的小回路与算子实验；没有整脑替代动力学运行、conditioning、学习成功或新学习候选。
|
||||
检查以原始事件、状态轨迹和预注册误差界限重算。缺失、失败、非有限值或初始事件丢失均不能通过；放电减少或静默本身不构成成功。
|
||||
|
||||
核查通过 58/58 项；失败或不完整 0 项。
|
||||
冻结历史完整性：通过。
|
||||
源码与输入预注册一致性：通过。
|
||||
|
||||
| 回路 | 语义 | 精度 | 总spikes | late spikes |
|
||||
|---|---|---|---:|---:|
|
||||
| pair | frozen | float32 | 473 | 238 |
|
||||
| pair | reset_g_only | float32 | 223 | 111 |
|
||||
| pair | refractory_gate_only | float32 | 635 | 321 |
|
||||
| pair | reset_and_refractory | float32 | 223 | 111 |
|
||||
| trio | frozen | float32 | 1064 | 535 |
|
||||
| trio | reset_g_only | float32 | 565 | 283 |
|
||||
| trio | refractory_gate_only | float32 | 1100 | 552 |
|
||||
| trio | reset_and_refractory | float32 | 469 | 235 |
|
||||
| five | frozen | float32 | 1483 | 747 |
|
||||
| five | reset_g_only | float32 | 868 | 433 |
|
||||
| five | refractory_gate_only | float32 | 1510 | 763 |
|
||||
| five | reset_and_refractory | float32 | 634 | 318 |
|
||||
| feedforward | frozen | float32 | 3 | 0 |
|
||||
| feedforward | reset_g_only | float32 | 3 | 0 |
|
||||
| feedforward | refractory_gate_only | float32 | 3 | 0 |
|
||||
| feedforward | reset_and_refractory | float32 | 3 | 0 |
|
||||
| no_input | frozen | float32 | 0 | 0 |
|
||||
| no_input | reset_g_only | float32 | 0 | 0 |
|
||||
| no_input | refractory_gate_only | float32 | 0 | 0 |
|
||||
| no_input | reset_and_refractory | float32 | 0 | 0 |
|
||||
| pair | author_complete | float64 | 213 | 106 |
|
||||
| pair | author_complete | float32 | 213 | 106 |
|
||||
| trio | author_complete | float64 | 448 | 224 |
|
||||
| trio | author_complete | float32 | 448 | 224 |
|
||||
| five | author_complete | float64 | 610 | 306 |
|
||||
| five | author_complete | float32 | 610 | 306 |
|
||||
| feedforward | author_complete | float64 | 3 | 0 |
|
||||
| feedforward | author_complete | float32 | 3 | 0 |
|
||||
| no_input | author_complete | float64 | 0 | 0 |
|
||||
| no_input | author_complete | float32 | 0 | 0 |
|
||||
|
||||
作者Brian2与独立NumPy比较：
|
||||
|
||||
| 实验 | 结果 | 事件一致 | 最大V/g误差 mV | 首次事件分歧step |
|
||||
|---|---|---|---:|---:|
|
||||
| author:pair:float64 | 通过 | True | 7.105427357601002e-14 | None |
|
||||
| author:pair:float32 | 通过 | True | 2.41696834564209e-05 | None |
|
||||
| author:trio:float64 | 通过 | True | 8.526512829121202e-14 | None |
|
||||
| author:trio:float32 | 通过 | True | 4.25875186920166e-05 | None |
|
||||
| author:five:float64 | 通过 | True | 3.836930773104541e-13 | None |
|
||||
| author:five:float32 | 通过 | True | 0.0002123713493347168 | None |
|
||||
| author:feedforward:float64 | 通过 | True | 3.765876499528531e-13 | None |
|
||||
| author:feedforward:float32 | 通过 | True | 0.00020179152488708496 | None |
|
||||
| author:no_input:float64 | 通过 | True | 7.105427357601002e-15 | None |
|
||||
| author:no_input:float32 | 通过 | True | 1.0728836059570312e-06 | None |
|
||||
|
||||
float32与float64交叉比较仅记录精度影响，允许保留分歧，不能用于掩盖各自与独立oracle的失败。
|
||||
- precision_cross_record:pair：事件一致=True，最大误差=3.733467101341148e-05 mV，首次事件分歧step=None。
|
||||
- precision_cross_record:trio：事件一致=True，最大误差=3.733467101341148e-05 mV，首次事件分歧step=None。
|
||||
- precision_cross_record:five：事件一致=True，最大误差=0.00019546693161487383 mV，首次事件分歧step=None。
|
||||
- precision_cross_record:feedforward：事件一致=True，最大误差=0.0005722045887850413 mV，首次事件分歧step=None。
|
||||
- precision_cross_record:no_input：事件一致=True，最大误差=0.0 mV，首次事件分歧step=None。
|
||||
|
||||
所有失败轨迹和错误文件原样保留；本简报没有选择获胜变体。
|
||||
小回路缺少外部整脑输入与graded APL，不能证明整脑因果必要性、生物真实性或信息保持。初始诊断seed也不是作者的感官Poisson接口。
|
||||
环境为Linux宿主，作者记录为Windows；Python补丁版本与环境差异见environment记录，不能称为二进制相同或原论文整脑复现。
|
||||
后续整脑嗅觉比较仍须核查固定输入、实际送达事件、持续活动和信息保持；可用学习仍需至少5个训练seed、独立评估、MBON/downstream的A对中性B特异性及acquisition/reversal/retention/delayed-reward对照。
|
||||
封存排除 .venv 与 __pycache__，保留环境安装报告；manifest列出payload哈希，checksums另含manifest，不存在自哈希循环。
|
||||
@@ -0,0 +1,121 @@
|
||||
motif,policy,precision,neuron_idx,spikes,late_spikes,last_ms
|
||||
pair,frozen,float32,1381,237,119,999.3000000000001
|
||||
pair,frozen,float32,1511,236,119,997.5
|
||||
pair,reset_g_only,float32,1381,112,56,999.0
|
||||
pair,reset_g_only,float32,1511,111,55,994.9000000000001
|
||||
pair,refractory_gate_only,float32,1381,318,161,999.1
|
||||
pair,refractory_gate_only,float32,1511,317,160,997.5
|
||||
pair,reset_and_refractory,float32,1381,112,56,999.0
|
||||
pair,reset_and_refractory,float32,1511,111,55,994.9000000000001
|
||||
trio,frozen,float32,1197,355,179,999.3000000000001
|
||||
trio,frozen,float32,1381,355,178,997.8000000000001
|
||||
trio,frozen,float32,1511,354,178,997.4000000000001
|
||||
trio,reset_g_only,float32,1197,188,94,995.4000000000001
|
||||
trio,reset_g_only,float32,1381,189,95,998.7
|
||||
trio,reset_g_only,float32,1511,188,94,996.1
|
||||
trio,refractory_gate_only,float32,1197,369,185,998.3000000000001
|
||||
trio,refractory_gate_only,float32,1381,367,184,999.1
|
||||
trio,refractory_gate_only,float32,1511,364,183,997.2
|
||||
trio,reset_and_refractory,float32,1197,156,78,995.3000000000001
|
||||
trio,reset_and_refractory,float32,1381,157,79,999.6
|
||||
trio,reset_and_refractory,float32,1511,156,78,997.5
|
||||
five,frozen,float32,1197,370,185,998.8000000000001
|
||||
five,frozen,float32,1381,369,185,997.4000000000001
|
||||
five,frozen,float32,1511,367,186,999.5
|
||||
five,frozen,float32,82312,80,41,990.3000000000001
|
||||
five,frozen,float32,133666,297,150,999.5
|
||||
five,reset_g_only,float32,1197,249,125,996.2
|
||||
five,reset_g_only,float32,1381,211,104,999.2
|
||||
five,reset_g_only,float32,1511,209,104,996.5
|
||||
five,reset_g_only,float32,82312,33,17,994.9000000000001
|
||||
five,reset_g_only,float32,133666,166,83,996.7
|
||||
five,refractory_gate_only,float32,1197,372,188,998.0
|
||||
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2678,1381
|
||||
2697,1197
|
||||
2715,1511
|
||||
2738,1381
|
||||
2748,133666
|
||||
2759,1197
|
||||
2778,1511
|
||||
2797,1381
|
||||
2819,1197
|
||||
2827,133666
|
||||
2840,1511
|
||||
2860,1381
|
||||
2874,1197
|
||||
2899,1511
|
||||
2912,133666
|
||||
2920,1381
|
||||
2939,1197
|
||||
2957,1511
|
||||
2980,1381
|
||||
2990,133666
|
||||
3001,1197
|
||||
3020,1511
|
||||
3039,1381
|
||||
3061,1197
|
||||
3069,133666
|
||||
3082,1511
|
||||
3102,1381
|
||||
3116,1197
|
||||
3141,1511
|
||||
3154,133666
|
||||
3162,1381
|
||||
3181,1197
|
||||
3199,1511
|
||||
3222,1381
|
||||
3232,133666
|
||||
3243,1197
|
||||
3262,1511
|
||||
3268,82312
|
||||
3281,1381
|
||||
3303,1197
|
||||
3311,133666
|
||||
3324,1511
|
||||
3344,1381
|
||||
3358,1197
|
||||
3383,1511
|
||||
3396,133666
|
||||
3404,1381
|
||||
3423,1197
|
||||
3441,1511
|
||||
3464,1381
|
||||
3474,133666
|
||||
3485,1197
|
||||
3504,1511
|
||||
3523,1381
|
||||
3545,1197
|
||||
3553,133666
|
||||
3566,1511
|
||||
3586,1381
|
||||
3600,1197
|
||||
3625,1511
|
||||
3638,133666
|
||||
3646,1381
|
||||
3665,1197
|
||||
3683,1511
|
||||
3706,1381
|
||||
3716,133666
|
||||
3727,1197
|
||||
3746,1511
|
||||
3765,1381
|
||||
3787,1197
|
||||
3795,133666
|
||||
3808,1511
|
||||
3828,1381
|
||||
3842,1197
|
||||
3867,1511
|
||||
3880,133666
|
||||
3888,1381
|
||||
3907,1197
|
||||
3919,82312
|
||||
3925,1511
|
||||
3948,1381
|
||||
3958,133666
|
||||
3969,1197
|
||||
3988,1511
|
||||
4007,1381
|
||||
4029,1197
|
||||
4037,133666
|
||||
4050,1511
|
||||
4070,1381
|
||||
4084,1197
|
||||
4109,1511
|
||||
4122,133666
|
||||
4130,1381
|
||||
4149,1197
|
||||
4167,1511
|
||||
4190,1381
|
||||
4200,133666
|
||||
4211,1197
|
||||
4230,1511
|
||||
4249,1381
|
||||
4271,1197
|
||||
4279,133666
|
||||
4292,1511
|
||||
4312,1381
|
||||
4326,1197
|
||||
4351,1511
|
||||
4364,133666
|
||||
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|
||||
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|
||||
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|
||||
4432,1381
|
||||
4442,133666
|
||||
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|
||||
4472,1511
|
||||
4491,1381
|
||||
4513,1197
|
||||
4521,133666
|
||||
4534,1511
|
||||
4554,1381
|
||||
4554,82312
|
||||
4568,1197
|
||||
4593,1511
|
||||
4606,133666
|
||||
4614,1381
|
||||
4633,1197
|
||||
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|
||||
4674,1381
|
||||
4684,133666
|
||||
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|
||||
4714,1511
|
||||
4733,1381
|
||||
4755,1197
|
||||
4763,133666
|
||||
4776,1511
|
||||
4796,1381
|
||||
4810,1197
|
||||
4835,1511
|
||||
4848,133666
|
||||
4856,1381
|
||||
4875,1197
|
||||
4893,1511
|
||||
4916,1381
|
||||
4926,133666
|
||||
4937,1197
|
||||
4956,1511
|
||||
4975,1381
|
||||
4997,1197
|
||||
5005,133666
|
||||
5018,1511
|
||||
5038,1381
|
||||
5052,1197
|
||||
5077,1511
|
||||
5090,133666
|
||||
5098,1381
|
||||
5117,1197
|
||||
5135,1511
|
||||
5158,1381
|
||||
5168,133666
|
||||
5179,1197
|
||||
5198,1511
|
||||
5204,82312
|
||||
5217,1381
|
||||
5239,1197
|
||||
5247,133666
|
||||
5260,1511
|
||||
5280,1381
|
||||
5294,1197
|
||||
5319,1511
|
||||
5332,133666
|
||||
5340,1381
|
||||
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|
||||
5377,1511
|
||||
5400,1381
|
||||
5410,133666
|
||||
5421,1197
|
||||
5440,1511
|
||||
5459,1381
|
||||
5481,1197
|
||||
5489,133666
|
||||
5502,1511
|
||||
5522,1381
|
||||
5536,1197
|
||||
5561,1511
|
||||
5574,133666
|
||||
5582,1381
|
||||
5601,1197
|
||||
5619,1511
|
||||
5642,1381
|
||||
5652,133666
|
||||
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|
||||
5682,1511
|
||||
5701,1381
|
||||
5723,1197
|
||||
5731,133666
|
||||
5744,1511
|
||||
5764,1381
|
||||
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|
||||
5803,1511
|
||||
5816,133666
|
||||
5824,1381
|
||||
5843,1197
|
||||
5855,82312
|
||||
5861,1511
|
||||
5884,1381
|
||||
5894,133666
|
||||
5905,1197
|
||||
5924,1511
|
||||
5943,1381
|
||||
5965,1197
|
||||
5973,133666
|
||||
5986,1511
|
||||
6006,1381
|
||||
6020,1197
|
||||
6045,1511
|
||||
6058,133666
|
||||
6066,1381
|
||||
6085,1197
|
||||
6103,1511
|
||||
6126,1381
|
||||
6136,133666
|
||||
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|
||||
6166,1511
|
||||
6185,1381
|
||||
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|
||||
6215,133666
|
||||
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|
||||
6248,1381
|
||||
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|
||||
6287,1511
|
||||
6300,133666
|
||||
6308,1381
|
||||
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|
||||
6345,1511
|
||||
6368,1381
|
||||
6378,133666
|
||||
6389,1197
|
||||
6408,1511
|
||||
6427,1381
|
||||
6449,1197
|
||||
6457,133666
|
||||
6470,1511
|
||||
6490,1381
|
||||
6490,82312
|
||||
6504,1197
|
||||
6529,1511
|
||||
6542,133666
|
||||
6550,1381
|
||||
6569,1197
|
||||
6587,1511
|
||||
6610,1381
|
||||
6620,133666
|
||||
6631,1197
|
||||
6650,1511
|
||||
6669,1381
|
||||
6691,1197
|
||||
6699,133666
|
||||
6712,1511
|
||||
6732,1381
|
||||
6746,1197
|
||||
6771,1511
|
||||
6784,133666
|
||||
6792,1381
|
||||
6811,1197
|
||||
6829,1511
|
||||
6852,1381
|
||||
6862,133666
|
||||
6873,1197
|
||||
6892,1511
|
||||
6911,1381
|
||||
6933,1197
|
||||
6941,133666
|
||||
6954,1511
|
||||
6974,1381
|
||||
6988,1197
|
||||
7013,1511
|
||||
7026,133666
|
||||
7034,1381
|
||||
7053,1197
|
||||
7071,1511
|
||||
7094,1381
|
||||
7104,133666
|
||||
7115,1197
|
||||
7134,1511
|
||||
7140,82312
|
||||
7153,1381
|
||||
7175,1197
|
||||
7183,133666
|
||||
7196,1511
|
||||
7216,1381
|
||||
7230,1197
|
||||
7255,1511
|
||||
7268,133666
|
||||
7276,1381
|
||||
7295,1197
|
||||
7313,1511
|
||||
7336,1381
|
||||
7346,133666
|
||||
7357,1197
|
||||
7376,1511
|
||||
7395,1381
|
||||
7417,1197
|
||||
7425,133666
|
||||
7438,1511
|
||||
7458,1381
|
||||
7472,1197
|
||||
7497,1511
|
||||
7510,133666
|
||||
7518,1381
|
||||
7537,1197
|
||||
7555,1511
|
||||
7578,1381
|
||||
7588,133666
|
||||
7599,1197
|
||||
7618,1511
|
||||
7637,1381
|
||||
7659,1197
|
||||
7667,133666
|
||||
7680,1511
|
||||
7700,1381
|
||||
7714,1197
|
||||
7739,1511
|
||||
7752,133666
|
||||
7760,1381
|
||||
7779,1197
|
||||
7791,82312
|
||||
7797,1511
|
||||
7820,1381
|
||||
7830,133666
|
||||
7841,1197
|
||||
7860,1511
|
||||
7879,1381
|
||||
7901,1197
|
||||
7909,133666
|
||||
7922,1511
|
||||
7942,1381
|
||||
7956,1197
|
||||
7981,1511
|
||||
7994,133666
|
||||
8002,1381
|
||||
8021,1197
|
||||
8039,1511
|
||||
8062,1381
|
||||
8072,133666
|
||||
8083,1197
|
||||
8102,1511
|
||||
8121,1381
|
||||
8143,1197
|
||||
8151,133666
|
||||
8164,1511
|
||||
8184,1381
|
||||
8198,1197
|
||||
8223,1511
|
||||
8236,133666
|
||||
8244,1381
|
||||
8263,1197
|
||||
8281,1511
|
||||
8304,1381
|
||||
8314,133666
|
||||
8325,1197
|
||||
8344,1511
|
||||
8363,1381
|
||||
8385,1197
|
||||
8393,133666
|
||||
8406,1511
|
||||
8426,1381
|
||||
8426,82312
|
||||
8440,1197
|
||||
8465,1511
|
||||
8478,133666
|
||||
8486,1381
|
||||
8505,1197
|
||||
8523,1511
|
||||
8546,1381
|
||||
8556,133666
|
||||
8567,1197
|
||||
8586,1511
|
||||
8605,1381
|
||||
8627,1197
|
||||
8635,133666
|
||||
8648,1511
|
||||
8668,1381
|
||||
8682,1197
|
||||
8707,1511
|
||||
8720,133666
|
||||
8728,1381
|
||||
8747,1197
|
||||
8765,1511
|
||||
8788,1381
|
||||
8798,133666
|
||||
8809,1197
|
||||
8828,1511
|
||||
8847,1381
|
||||
8869,1197
|
||||
8877,133666
|
||||
8890,1511
|
||||
8910,1381
|
||||
8924,1197
|
||||
8949,1511
|
||||
8962,133666
|
||||
8970,1381
|
||||
8989,1197
|
||||
9007,1511
|
||||
9030,1381
|
||||
9040,133666
|
||||
9051,1197
|
||||
9070,1511
|
||||
9076,82312
|
||||
9089,1381
|
||||
9111,1197
|
||||
9119,133666
|
||||
9132,1511
|
||||
9152,1381
|
||||
9166,1197
|
||||
9191,1511
|
||||
9204,133666
|
||||
9212,1381
|
||||
9231,1197
|
||||
9249,1511
|
||||
9272,1381
|
||||
9282,133666
|
||||
9293,1197
|
||||
9312,1511
|
||||
9331,1381
|
||||
9353,1197
|
||||
9361,133666
|
||||
9374,1511
|
||||
9394,1381
|
||||
9408,1197
|
||||
9433,1511
|
||||
9446,133666
|
||||
9454,1381
|
||||
9473,1197
|
||||
9491,1511
|
||||
9514,1381
|
||||
9524,133666
|
||||
9535,1197
|
||||
9554,1511
|
||||
9573,1381
|
||||
9595,1197
|
||||
9603,133666
|
||||
9616,1511
|
||||
9636,1381
|
||||
9650,1197
|
||||
9675,1511
|
||||
9688,133666
|
||||
9696,1381
|
||||
9715,1197
|
||||
9727,82312
|
||||
9733,1511
|
||||
9756,1381
|
||||
9766,133666
|
||||
9777,1197
|
||||
9796,1511
|
||||
9815,1381
|
||||
9837,1197
|
||||
9845,133666
|
||||
9858,1511
|
||||
9878,1381
|
||||
9892,1197
|
||||
9917,1511
|
||||
9930,133666
|
||||
9938,1381
|
||||
9957,1197
|
||||
9975,1511
|
||||
9998,1381
|
||||
|
@@ -0,0 +1,869 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
45,1197
|
||||
49,1511
|
||||
77,1381
|
||||
88,133666
|
||||
96,1197
|
||||
103,1511
|
||||
127,1381
|
||||
142,1197
|
||||
148,133666
|
||||
151,1511
|
||||
175,1381
|
||||
178,1197
|
||||
197,1511
|
||||
205,133666
|
||||
220,1381
|
||||
221,1197
|
||||
248,1511
|
||||
265,1197
|
||||
268,1381
|
||||
268,133666
|
||||
297,1511
|
||||
298,1197
|
||||
316,1381
|
||||
325,133666
|
||||
341,1197
|
||||
343,1511
|
||||
364,82312
|
||||
369,1381
|
||||
387,1197
|
||||
389,133666
|
||||
395,1511
|
||||
419,1381
|
||||
432,1197
|
||||
443,1511
|
||||
452,133666
|
||||
468,1381
|
||||
472,1197
|
||||
491,1511
|
||||
512,1381
|
||||
515,1197
|
||||
515,133666
|
||||
543,1511
|
||||
557,1197
|
||||
560,1381
|
||||
577,133666
|
||||
591,1511
|
||||
593,1197
|
||||
598,1381
|
||||
626,1197
|
||||
627,1511
|
||||
634,133666
|
||||
635,1381
|
||||
661,1197
|
||||
664,1511
|
||||
670,1381
|
||||
677,82312
|
||||
685,133666
|
||||
697,1197
|
||||
703,1511
|
||||
706,1381
|
||||
732,1197
|
||||
741,1511
|
||||
741,133666
|
||||
742,1381
|
||||
769,1197
|
||||
776,1381
|
||||
777,1511
|
||||
794,133666
|
||||
806,1197
|
||||
811,1381
|
||||
815,1511
|
||||
840,1197
|
||||
844,1381
|
||||
850,133666
|
||||
856,1511
|
||||
877,1197
|
||||
881,1381
|
||||
895,1511
|
||||
902,133666
|
||||
918,1197
|
||||
921,1381
|
||||
933,82312
|
||||
940,1511
|
||||
959,1197
|
||||
964,133666
|
||||
965,1381
|
||||
992,1511
|
||||
1000,1197
|
||||
1013,1381
|
||||
1024,133666
|
||||
1040,1511
|
||||
1041,1197
|
||||
1061,1381
|
||||
1082,1197
|
||||
1085,1511
|
||||
1086,133666
|
||||
1112,1381
|
||||
1124,1197
|
||||
1133,1511
|
||||
1146,133666
|
||||
1159,1381
|
||||
1163,1197
|
||||
1182,1511
|
||||
1203,1197
|
||||
1203,1381
|
||||
1207,133666
|
||||
1233,1511
|
||||
1244,1197
|
||||
1250,82312
|
||||
1251,1381
|
||||
1268,133666
|
||||
1281,1197
|
||||
1281,1511
|
||||
1301,1381
|
||||
1322,1197
|
||||
1325,1511
|
||||
1327,133666
|
||||
1352,1381
|
||||
1364,1197
|
||||
1373,1511
|
||||
1387,133666
|
||||
1399,1381
|
||||
1403,1197
|
||||
1422,1511
|
||||
1443,1197
|
||||
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|
||||
1447,133666
|
||||
1473,1511
|
||||
1484,1197
|
||||
1491,1381
|
||||
1508,133666
|
||||
1521,1197
|
||||
1521,1511
|
||||
1541,1381
|
||||
1549,82312
|
||||
1562,1197
|
||||
1565,1511
|
||||
1567,133666
|
||||
1592,1381
|
||||
1604,1197
|
||||
1613,1511
|
||||
1627,133666
|
||||
1639,1381
|
||||
1643,1197
|
||||
1662,1511
|
||||
1683,1197
|
||||
1683,1381
|
||||
1687,133666
|
||||
1713,1511
|
||||
1724,1197
|
||||
1731,1381
|
||||
1748,133666
|
||||
1761,1197
|
||||
1761,1511
|
||||
1781,1381
|
||||
1802,1197
|
||||
1805,1511
|
||||
1807,133666
|
||||
1832,1381
|
||||
1844,1197
|
||||
1848,82312
|
||||
1853,1511
|
||||
1867,133666
|
||||
1879,1381
|
||||
1883,1197
|
||||
1902,1511
|
||||
1923,1197
|
||||
1923,1381
|
||||
1927,133666
|
||||
1953,1511
|
||||
1964,1197
|
||||
1971,1381
|
||||
1988,133666
|
||||
2001,1197
|
||||
2001,1511
|
||||
2021,1381
|
||||
2042,1197
|
||||
2045,1511
|
||||
2047,133666
|
||||
2072,1381
|
||||
2084,1197
|
||||
2093,1511
|
||||
2107,133666
|
||||
2119,1381
|
||||
2123,1197
|
||||
2142,1511
|
||||
2147,82312
|
||||
2163,1197
|
||||
2163,1381
|
||||
2167,133666
|
||||
2193,1511
|
||||
2204,1197
|
||||
2211,1381
|
||||
2228,133666
|
||||
2241,1197
|
||||
2241,1511
|
||||
2261,1381
|
||||
2282,1197
|
||||
2285,1511
|
||||
2287,133666
|
||||
2312,1381
|
||||
2324,1197
|
||||
2333,1511
|
||||
2347,133666
|
||||
2359,1381
|
||||
2363,1197
|
||||
2382,1511
|
||||
2403,1197
|
||||
2403,1381
|
||||
2407,133666
|
||||
2433,1511
|
||||
2444,1197
|
||||
2447,82312
|
||||
2451,1381
|
||||
2468,133666
|
||||
2481,1197
|
||||
2481,1511
|
||||
2501,1381
|
||||
2522,1197
|
||||
2525,1511
|
||||
2527,133666
|
||||
2552,1381
|
||||
2564,1197
|
||||
2573,1511
|
||||
2587,133666
|
||||
2599,1381
|
||||
2603,1197
|
||||
2622,1511
|
||||
2643,1197
|
||||
2643,1381
|
||||
2647,133666
|
||||
2673,1511
|
||||
2684,1197
|
||||
2691,1381
|
||||
2708,133666
|
||||
2721,1197
|
||||
2721,1511
|
||||
2741,1381
|
||||
2749,82312
|
||||
2762,1197
|
||||
2765,1511
|
||||
2767,133666
|
||||
2792,1381
|
||||
2804,1197
|
||||
2813,1511
|
||||
2827,133666
|
||||
2839,1381
|
||||
2843,1197
|
||||
2862,1511
|
||||
2883,1197
|
||||
2883,1381
|
||||
2887,133666
|
||||
2913,1511
|
||||
2924,1197
|
||||
2931,1381
|
||||
2948,133666
|
||||
2961,1197
|
||||
2961,1511
|
||||
2981,1381
|
||||
3002,1197
|
||||
3005,1511
|
||||
3007,133666
|
||||
3032,1381
|
||||
3044,1197
|
||||
3048,82312
|
||||
3053,1511
|
||||
3067,133666
|
||||
3079,1381
|
||||
3083,1197
|
||||
3102,1511
|
||||
3123,1197
|
||||
3123,1381
|
||||
3127,133666
|
||||
3153,1511
|
||||
3164,1197
|
||||
3171,1381
|
||||
3188,133666
|
||||
3201,1197
|
||||
3201,1511
|
||||
3221,1381
|
||||
3242,1197
|
||||
3245,1511
|
||||
3247,133666
|
||||
3272,1381
|
||||
3284,1197
|
||||
3293,1511
|
||||
3307,133666
|
||||
3319,1381
|
||||
3323,1197
|
||||
3342,1511
|
||||
3347,82312
|
||||
3363,1197
|
||||
3363,1381
|
||||
3367,133666
|
||||
3393,1511
|
||||
3404,1197
|
||||
3411,1381
|
||||
3428,133666
|
||||
3441,1197
|
||||
3441,1511
|
||||
3461,1381
|
||||
3482,1197
|
||||
3485,1511
|
||||
3487,133666
|
||||
3512,1381
|
||||
3524,1197
|
||||
3533,1511
|
||||
3547,133666
|
||||
3559,1381
|
||||
3563,1197
|
||||
3582,1511
|
||||
3603,1197
|
||||
3603,1381
|
||||
3607,133666
|
||||
3633,1511
|
||||
3644,1197
|
||||
3647,82312
|
||||
3651,1381
|
||||
3668,133666
|
||||
3681,1197
|
||||
3681,1511
|
||||
3701,1381
|
||||
3722,1197
|
||||
3725,1511
|
||||
3727,133666
|
||||
3752,1381
|
||||
3764,1197
|
||||
3773,1511
|
||||
3787,133666
|
||||
3799,1381
|
||||
3803,1197
|
||||
3822,1511
|
||||
3843,1197
|
||||
3843,1381
|
||||
3847,133666
|
||||
3873,1511
|
||||
3884,1197
|
||||
3891,1381
|
||||
3908,133666
|
||||
3921,1197
|
||||
3921,1511
|
||||
3941,1381
|
||||
3949,82312
|
||||
3962,1197
|
||||
3965,1511
|
||||
3967,133666
|
||||
3992,1381
|
||||
4004,1197
|
||||
4013,1511
|
||||
4027,133666
|
||||
4039,1381
|
||||
4043,1197
|
||||
4062,1511
|
||||
4083,1197
|
||||
4083,1381
|
||||
4087,133666
|
||||
4113,1511
|
||||
4124,1197
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
4181,1381
|
||||
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|
||||
4205,1511
|
||||
4207,133666
|
||||
4232,1381
|
||||
4244,1197
|
||||
4248,82312
|
||||
4253,1511
|
||||
4267,133666
|
||||
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|
||||
4283,1197
|
||||
4302,1511
|
||||
4323,1197
|
||||
4323,1381
|
||||
4327,133666
|
||||
4353,1511
|
||||
4364,1197
|
||||
4371,1381
|
||||
4388,133666
|
||||
4401,1197
|
||||
4401,1511
|
||||
4421,1381
|
||||
4442,1197
|
||||
4445,1511
|
||||
4447,133666
|
||||
4472,1381
|
||||
4484,1197
|
||||
4493,1511
|
||||
4507,133666
|
||||
4519,1381
|
||||
4523,1197
|
||||
4542,1511
|
||||
4547,82312
|
||||
4563,1197
|
||||
4563,1381
|
||||
4567,133666
|
||||
4593,1511
|
||||
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|
||||
4611,1381
|
||||
4628,133666
|
||||
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|
||||
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|
||||
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|
||||
4682,1197
|
||||
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|
||||
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|
||||
4712,1381
|
||||
4724,1197
|
||||
4733,1511
|
||||
4747,133666
|
||||
4759,1381
|
||||
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|
||||
4782,1511
|
||||
4803,1197
|
||||
4803,1381
|
||||
4807,133666
|
||||
4833,1511
|
||||
4844,1197
|
||||
4847,82312
|
||||
4851,1381
|
||||
4868,133666
|
||||
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|
||||
4881,1511
|
||||
4901,1381
|
||||
4922,1197
|
||||
4925,1511
|
||||
4927,133666
|
||||
4952,1381
|
||||
4964,1197
|
||||
4973,1511
|
||||
4987,133666
|
||||
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|
||||
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|
||||
5022,1511
|
||||
5043,1197
|
||||
5043,1381
|
||||
5047,133666
|
||||
5073,1511
|
||||
5084,1197
|
||||
5091,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
5149,82312
|
||||
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|
||||
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|
||||
5167,133666
|
||||
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|
||||
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|
||||
5213,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
5287,133666
|
||||
5313,1511
|
||||
5324,1197
|
||||
5331,1381
|
||||
5348,133666
|
||||
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|
||||
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|
||||
5381,1381
|
||||
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|
||||
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|
||||
5407,133666
|
||||
5432,1381
|
||||
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|
||||
5448,82312
|
||||
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|
||||
5467,133666
|
||||
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|
||||
5483,1197
|
||||
5502,1511
|
||||
5523,1197
|
||||
5523,1381
|
||||
5527,133666
|
||||
5553,1511
|
||||
5564,1197
|
||||
5571,1381
|
||||
5588,133666
|
||||
5601,1197
|
||||
5601,1511
|
||||
5621,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
5719,1381
|
||||
5723,1197
|
||||
5742,1511
|
||||
5747,82312
|
||||
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|
||||
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|
||||
5767,133666
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
5924,1197
|
||||
5933,1511
|
||||
5947,133666
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
6524,1197
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
6632,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
7923,1197
|
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|
@@ -0,0 +1 @@
|
||||
step,neuron_idx
|
||||
|
+1
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||||
step,neuron_idx
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|
+1
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step,neuron_idx
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+1
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step,neuron_idx
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
8733,1381
|
||||
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|
||||
8775,1381
|
||||
8799,1511
|
||||
8817,1381
|
||||
8841,1511
|
||||
8859,1381
|
||||
8883,1511
|
||||
8901,1381
|
||||
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|
||||
8943,1381
|
||||
8967,1511
|
||||
8985,1381
|
||||
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|
||||
9027,1381
|
||||
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|
||||
9069,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
9531,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
9783,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
9909,1381
|
||||
9933,1511
|
||||
9951,1381
|
||||
9975,1511
|
||||
9993,1381
|
||||
|
+636
@@ -0,0 +1,636 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
49,1511
|
||||
90,1381
|
||||
116,1511
|
||||
139,1381
|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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344,1381
|
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|
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|
||||
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|
||||
411,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
679,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
908,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
||||
1816,1381
|
||||
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|
||||
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|
||||
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|
||||
1879,1381
|
||||
1896,1511
|
||||
1914,1381
|
||||
1928,1511
|
||||
1945,1381
|
||||
1963,1511
|
||||
1979,1381
|
||||
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|
||||
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|
||||
2030,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
||||
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
|
+224
@@ -0,0 +1,224 @@
|
||||
step,neuron_idx
|
||||
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|
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|
@@ -0,0 +1,224 @@
|
||||
step,neuron_idx
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||||
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|
||||
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|
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|
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|
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|
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|
||||
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||||
9990,1381
|
||||
|
@@ -0,0 +1,81 @@
|
||||
motif,policy,precision,neuron_idx,spikes,late_spikes,last_ms
|
||||
pair,frozen,float32,1381,237,119,999.3000000000001
|
||||
pair,frozen,float32,1511,236,119,997.5
|
||||
pair,reset_g_only,float32,1381,112,56,999.0
|
||||
pair,reset_g_only,float32,1511,111,55,994.9000000000001
|
||||
pair,refractory_gate_only,float32,1381,318,161,999.1
|
||||
pair,refractory_gate_only,float32,1511,317,160,997.5
|
||||
pair,reset_and_refractory,float32,1381,112,56,999.0
|
||||
pair,reset_and_refractory,float32,1511,111,55,994.9000000000001
|
||||
trio,frozen,float32,1197,355,179,999.3000000000001
|
||||
trio,frozen,float32,1381,355,178,997.8000000000001
|
||||
trio,frozen,float32,1511,354,178,997.4000000000001
|
||||
trio,reset_g_only,float32,1197,188,94,995.4000000000001
|
||||
trio,reset_g_only,float32,1381,189,95,998.7
|
||||
trio,reset_g_only,float32,1511,188,94,996.1
|
||||
trio,refractory_gate_only,float32,1197,369,185,998.3000000000001
|
||||
trio,refractory_gate_only,float32,1381,367,184,999.1
|
||||
trio,refractory_gate_only,float32,1511,364,183,997.2
|
||||
trio,reset_and_refractory,float32,1197,156,78,995.3000000000001
|
||||
trio,reset_and_refractory,float32,1381,157,79,999.6
|
||||
trio,reset_and_refractory,float32,1511,156,78,997.5
|
||||
five,frozen,float32,1197,370,185,998.8000000000001
|
||||
five,frozen,float32,1381,369,185,997.4000000000001
|
||||
five,frozen,float32,1511,367,186,999.5
|
||||
five,frozen,float32,82312,80,41,990.3000000000001
|
||||
five,frozen,float32,133666,297,150,999.5
|
||||
five,reset_g_only,float32,1197,249,125,996.2
|
||||
five,reset_g_only,float32,1381,211,104,999.2
|
||||
five,reset_g_only,float32,1511,209,104,996.5
|
||||
five,reset_g_only,float32,82312,33,17,994.9000000000001
|
||||
five,reset_g_only,float32,133666,166,83,996.7
|
||||
five,refractory_gate_only,float32,1197,372,188,998.0
|
||||
five,refractory_gate_only,float32,1381,375,191,998.6
|
||||
five,refractory_gate_only,float32,1511,371,187,997.5
|
||||
five,refractory_gate_only,float32,82312,85,43,991.9000000000001
|
||||
five,refractory_gate_only,float32,133666,307,154,997.7
|
||||
five,reset_and_refractory,float32,1197,165,82,995.7
|
||||
five,reset_and_refractory,float32,1381,166,83,999.8000000000001
|
||||
five,reset_and_refractory,float32,1511,165,83,997.5
|
||||
five,reset_and_refractory,float32,82312,15,8,972.7
|
||||
five,reset_and_refractory,float32,133666,123,62,993.0
|
||||
feedforward,frozen,float32,1197,1,0,10.0
|
||||
feedforward,frozen,float32,1381,1,0,0.0
|
||||
feedforward,frozen,float32,1511,1,0,4.9
|
||||
feedforward,frozen,float32,82312,0,0,
|
||||
feedforward,frozen,float32,133666,0,0,
|
||||
feedforward,reset_g_only,float32,1197,1,0,10.0
|
||||
feedforward,reset_g_only,float32,1381,1,0,0.0
|
||||
feedforward,reset_g_only,float32,1511,1,0,4.9
|
||||
feedforward,reset_g_only,float32,82312,0,0,
|
||||
feedforward,reset_g_only,float32,133666,0,0,
|
||||
feedforward,refractory_gate_only,float32,1197,1,0,10.0
|
||||
feedforward,refractory_gate_only,float32,1381,1,0,0.0
|
||||
feedforward,refractory_gate_only,float32,1511,1,0,4.9
|
||||
feedforward,refractory_gate_only,float32,82312,0,0,
|
||||
feedforward,refractory_gate_only,float32,133666,0,0,
|
||||
feedforward,reset_and_refractory,float32,1197,1,0,10.0
|
||||
feedforward,reset_and_refractory,float32,1381,1,0,0.0
|
||||
feedforward,reset_and_refractory,float32,1511,1,0,4.9
|
||||
feedforward,reset_and_refractory,float32,82312,0,0,
|
||||
feedforward,reset_and_refractory,float32,133666,0,0,
|
||||
no_input,frozen,float32,1197,0,0,
|
||||
no_input,frozen,float32,1381,0,0,
|
||||
no_input,frozen,float32,1511,0,0,
|
||||
no_input,frozen,float32,82312,0,0,
|
||||
no_input,frozen,float32,133666,0,0,
|
||||
no_input,reset_g_only,float32,1197,0,0,
|
||||
no_input,reset_g_only,float32,1381,0,0,
|
||||
no_input,reset_g_only,float32,1511,0,0,
|
||||
no_input,reset_g_only,float32,82312,0,0,
|
||||
no_input,reset_g_only,float32,133666,0,0,
|
||||
no_input,refractory_gate_only,float32,1197,0,0,
|
||||
no_input,refractory_gate_only,float32,1381,0,0,
|
||||
no_input,refractory_gate_only,float32,1511,0,0,
|
||||
no_input,refractory_gate_only,float32,82312,0,0,
|
||||
no_input,refractory_gate_only,float32,133666,0,0,
|
||||
no_input,reset_and_refractory,float32,1197,0,0,
|
||||
no_input,reset_and_refractory,float32,1381,0,0,
|
||||
no_input,reset_and_refractory,float32,1511,0,0,
|
||||
no_input,reset_and_refractory,float32,82312,0,0,
|
||||
no_input,reset_and_refractory,float32,133666,0,0,
|
||||
|
File diff suppressed because it is too large
Load Diff
+1101
File diff suppressed because it is too large
Load Diff
+470
@@ -0,0 +1,470 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
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|
||||
8695,1511
|
||||
8716,1381
|
||||
8737,1197
|
||||
8759,1511
|
||||
8780,1381
|
||||
8801,1197
|
||||
8823,1511
|
||||
8844,1381
|
||||
8865,1197
|
||||
8887,1511
|
||||
8908,1381
|
||||
8929,1197
|
||||
8951,1511
|
||||
8972,1381
|
||||
8993,1197
|
||||
9015,1511
|
||||
9036,1381
|
||||
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|
||||
9079,1511
|
||||
9100,1381
|
||||
9121,1197
|
||||
9143,1511
|
||||
9164,1381
|
||||
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|
||||
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|
||||
9228,1381
|
||||
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|
||||
9271,1511
|
||||
9292,1381
|
||||
9313,1197
|
||||
9335,1511
|
||||
9356,1381
|
||||
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|
||||
9399,1511
|
||||
9420,1381
|
||||
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|
||||
9463,1511
|
||||
9484,1381
|
||||
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|
||||
9527,1511
|
||||
9548,1381
|
||||
9569,1197
|
||||
9591,1511
|
||||
9612,1381
|
||||
9633,1197
|
||||
9655,1511
|
||||
9676,1381
|
||||
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|
||||
9719,1511
|
||||
9740,1381
|
||||
9761,1197
|
||||
9783,1511
|
||||
9804,1381
|
||||
9825,1197
|
||||
9847,1511
|
||||
9868,1381
|
||||
9889,1197
|
||||
9911,1511
|
||||
9932,1381
|
||||
9953,1197
|
||||
9975,1511
|
||||
9996,1381
|
||||
|
@@ -0,0 +1,566 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
45,1197
|
||||
49,1511
|
||||
77,1381
|
||||
96,1197
|
||||
103,1511
|
||||
129,1381
|
||||
149,1197
|
||||
156,1511
|
||||
182,1381
|
||||
202,1197
|
||||
209,1511
|
||||
235,1381
|
||||
255,1197
|
||||
262,1511
|
||||
288,1381
|
||||
308,1197
|
||||
315,1511
|
||||
341,1381
|
||||
361,1197
|
||||
368,1511
|
||||
394,1381
|
||||
414,1197
|
||||
421,1511
|
||||
447,1381
|
||||
467,1197
|
||||
474,1511
|
||||
500,1381
|
||||
520,1197
|
||||
527,1511
|
||||
553,1381
|
||||
573,1197
|
||||
580,1511
|
||||
606,1381
|
||||
626,1197
|
||||
633,1511
|
||||
659,1381
|
||||
679,1197
|
||||
686,1511
|
||||
712,1381
|
||||
732,1197
|
||||
739,1511
|
||||
765,1381
|
||||
785,1197
|
||||
792,1511
|
||||
818,1381
|
||||
838,1197
|
||||
845,1511
|
||||
871,1381
|
||||
891,1197
|
||||
898,1511
|
||||
924,1381
|
||||
944,1197
|
||||
951,1511
|
||||
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|
||||
997,1197
|
||||
1004,1511
|
||||
1030,1381
|
||||
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|
||||
1057,1511
|
||||
1083,1381
|
||||
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|
||||
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|
||||
1136,1381
|
||||
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|
||||
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|
||||
1189,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1560,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1825,1381
|
||||
1845,1197
|
||||
1852,1511
|
||||
1878,1381
|
||||
1898,1197
|
||||
1905,1511
|
||||
1931,1381
|
||||
1951,1197
|
||||
1958,1511
|
||||
1984,1381
|
||||
2004,1197
|
||||
2011,1511
|
||||
2037,1381
|
||||
2057,1197
|
||||
2064,1511
|
||||
2090,1381
|
||||
2110,1197
|
||||
2117,1511
|
||||
2143,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
2223,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
3389,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
3627,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
3733,1381
|
||||
3753,1197
|
||||
3760,1511
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
3919,1511
|
||||
3945,1381
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
9272,1511
|
||||
9298,1381
|
||||
9318,1197
|
||||
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|
||||
9351,1381
|
||||
9371,1197
|
||||
9378,1511
|
||||
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|
||||
9424,1197
|
||||
9431,1511
|
||||
9457,1381
|
||||
9477,1197
|
||||
9484,1511
|
||||
9510,1381
|
||||
9530,1197
|
||||
9537,1511
|
||||
9563,1381
|
||||
9583,1197
|
||||
9590,1511
|
||||
9616,1381
|
||||
9636,1197
|
||||
9643,1511
|
||||
9669,1381
|
||||
9689,1197
|
||||
9696,1511
|
||||
9722,1381
|
||||
9742,1197
|
||||
9749,1511
|
||||
9775,1381
|
||||
9795,1197
|
||||
9802,1511
|
||||
9828,1381
|
||||
9848,1197
|
||||
9855,1511
|
||||
9881,1381
|
||||
9901,1197
|
||||
9908,1511
|
||||
9934,1381
|
||||
9954,1197
|
||||
9961,1511
|
||||
9987,1381
|
||||
|
@@ -0,0 +1,9 @@
|
||||
{
|
||||
"isolated_venv_only": true,
|
||||
"existing_environments_modified": false,
|
||||
"initial_install_failure": "New venv pip22.0.2 lacks --report; upgraded ONLY new venv pip to author-pinned23.0.1 before installing",
|
||||
"MarkupSafe": "Author-pinned1.1.1 used PyPI source distribution and legacy setup.py because no CPython3.10 wheel; other requested core packages used wheels",
|
||||
"author_environment": "Windows conda CPython3.10.11",
|
||||
"actual_environment": "Linux CPython3.10.12; not binary-identical",
|
||||
"package_download_provenance": "install-report.json contains HTTPS PyPI archive URLs and SHA256 for packages"
|
||||
}
|
||||
+12
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"python": "3.10.12 (main, Aug 31 2026, 10:18:17) [GCC 11.4.0]",
|
||||
"executable": "/data/digital-drosophila/models/male-cns-lif/.venv/bin/python",
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"package_versions": {
|
||||
"numpy": "2.2.6",
|
||||
"pandas": "2.3.3",
|
||||
"pyarrow": "25.0.1",
|
||||
"torch": "2.14.0",
|
||||
"scipy": "1.15.3"
|
||||
}
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,78 @@
|
||||
[
|
||||
{
|
||||
"name": "Brian2",
|
||||
"version": "2.5.1"
|
||||
},
|
||||
{
|
||||
"name": "Cython",
|
||||
"version": "0.29.33"
|
||||
},
|
||||
{
|
||||
"name": "Jinja2",
|
||||
"version": "2.11.3"
|
||||
},
|
||||
{
|
||||
"name": "joblib",
|
||||
"version": "1.2.0"
|
||||
},
|
||||
{
|
||||
"name": "MarkupSafe",
|
||||
"version": "1.1.1"
|
||||
},
|
||||
{
|
||||
"name": "mpmath",
|
||||
"version": "1.3.0"
|
||||
},
|
||||
{
|
||||
"name": "numpy",
|
||||
"version": "1.22.3"
|
||||
},
|
||||
{
|
||||
"name": "packaging",
|
||||
"version": "23.1"
|
||||
},
|
||||
{
|
||||
"name": "pandas",
|
||||
"version": "1.4.3"
|
||||
},
|
||||
{
|
||||
"name": "pip",
|
||||
"version": "23.0.1"
|
||||
},
|
||||
{
|
||||
"name": "pyarrow",
|
||||
"version": "11.0.0"
|
||||
},
|
||||
{
|
||||
"name": "pyparsing",
|
||||
"version": "3.0.9"
|
||||
},
|
||||
{
|
||||
"name": "python-dateutil",
|
||||
"version": "2.8.2"
|
||||
},
|
||||
{
|
||||
"name": "pytz",
|
||||
"version": "2023.3"
|
||||
},
|
||||
{
|
||||
"name": "scipy",
|
||||
"version": "1.8.1"
|
||||
},
|
||||
{
|
||||
"name": "setuptools",
|
||||
"version": "67.8.0"
|
||||
},
|
||||
{
|
||||
"name": "six",
|
||||
"version": "1.16.0"
|
||||
},
|
||||
{
|
||||
"name": "sympy",
|
||||
"version": "1.12"
|
||||
},
|
||||
{
|
||||
"name": "wheel",
|
||||
"version": "0.38.4"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2023 Philip Shiu and Nico Spiller
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -0,0 +1,109 @@
|
||||
name: brian2
|
||||
channels:
|
||||
- defaults
|
||||
- conda-forge
|
||||
dependencies:
|
||||
- abseil-cpp=20211102.0=h0e60522_0
|
||||
- arrow-cpp=11.0.0=py310h7cee713_0
|
||||
- asttokens=2.2.1=pyhd8ed1ab_0
|
||||
- aws-c-common=0.4.57=ha925a31_1
|
||||
- aws-c-event-stream=0.1.6=hd77b12b_5
|
||||
- aws-checksums=0.1.9=ha925a31_0
|
||||
- aws-sdk-cpp=1.8.185=hd77b12b_0
|
||||
- backcall=0.2.0=pyh9f0ad1d_0
|
||||
- backports=1.0=pyhd8ed1ab_3
|
||||
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
|
||||
- boost-cpp=1.78.0=h5b4e17d_0
|
||||
- brian2=2.5.1=py310hc781a3c_0
|
||||
- bzip2=1.0.8=he774522_0
|
||||
- c-ares=1.19.0=h2bbff1b_0
|
||||
- ca-certificates=2023.5.7=h56e8100_0
|
||||
- colorama=0.4.6=pyhd8ed1ab_0
|
||||
- cython=0.29.33=py310hd77b12b_0
|
||||
- debugpy=1.5.1=py310hd77b12b_0
|
||||
- decorator=5.1.1=pyhd8ed1ab_0
|
||||
- executing=1.2.0=pyhd8ed1ab_0
|
||||
- gflags=2.2.2=ha925a31_1004
|
||||
- glog=0.5.0=h4797de2_0
|
||||
- gsl=2.7=hdfb1a43_0
|
||||
- importlib-metadata=6.6.0=pyha770c72_0
|
||||
- importlib_metadata=6.6.0=hd8ed1ab_0
|
||||
- intel-openmp=2023.1.0=h57928b3_46319
|
||||
- ipykernel=6.15.0=pyh025b116_0
|
||||
- ipython=8.14.0=pyh08f2357_0
|
||||
- jedi=0.18.2=pyhd8ed1ab_0
|
||||
- jinja2=2.11.3=pyhd8ed1ab_2
|
||||
- joblib=1.2.0=pyhd8ed1ab_0
|
||||
- jupyter_client=8.2.0=pyhd8ed1ab_0
|
||||
- jupyter_core=5.3.0=py310h5588dad_0
|
||||
- libblas=3.9.0=8_mkl
|
||||
- libbrotlicommon=1.0.9=h8ffe710_7
|
||||
- libbrotlidec=1.0.9=h8ffe710_7
|
||||
- libbrotlienc=1.0.9=h8ffe710_7
|
||||
- libcblas=3.9.0=8_mkl
|
||||
- libcurl=7.88.1=h86230a5_0
|
||||
- libffi=3.4.4=hd77b12b_0
|
||||
- liblapack=3.9.0=8_mkl
|
||||
- libprotobuf=3.20.3=h23ce68f_0
|
||||
- libsodium=1.0.18=h8d14728_1
|
||||
- libssh2=1.10.0=hcd4344a_0
|
||||
- libthrift=0.15.0=h636ae23_0
|
||||
- lz4-c=1.9.4=h2bbff1b_0
|
||||
- m2w64-gcc-libgfortran=5.3.0=6
|
||||
- m2w64-gcc-libs=5.3.0=7
|
||||
- m2w64-gcc-libs-core=5.3.0=7
|
||||
- m2w64-gmp=6.1.0=2
|
||||
- m2w64-libwinpthread-git=5.0.0.4634.697f757=2
|
||||
- markupsafe=1.1.1=py310he2412df_4
|
||||
- matplotlib-inline=0.1.6=pyhd8ed1ab_0
|
||||
- mkl=2020.4=hb70f87d_311
|
||||
- mpmath=1.3.0=pyhd8ed1ab_0
|
||||
- msys2-conda-epoch=20160418=1
|
||||
- nest-asyncio=1.5.6=pyhd8ed1ab_0
|
||||
- numpy=1.22.3=py310hed7ac4c_2
|
||||
- openssl=1.1.1t=h2bbff1b_0
|
||||
- orc=1.7.4=h623e30f_1
|
||||
- packaging=23.1=pyhd8ed1ab_0
|
||||
- pandas=1.4.3=py310hf5e1058_0
|
||||
- parso=0.8.3=pyhd8ed1ab_0
|
||||
- pickleshare=0.7.5=py_1003
|
||||
- pip=23.0.1=py310haa95532_0
|
||||
- platformdirs=3.5.1=pyhd8ed1ab_0
|
||||
- prompt-toolkit=3.0.38=pyha770c72_0
|
||||
- prompt_toolkit=3.0.38=hd8ed1ab_0
|
||||
- psutil=5.9.0=py310h2bbff1b_0
|
||||
- pure_eval=0.2.2=pyhd8ed1ab_0
|
||||
- py-cpuinfo=9.0.0=pyhd8ed1ab_0
|
||||
- pyarrow=11.0.0=py310h790e06d_0
|
||||
- pygments=2.15.1=pyhd8ed1ab_0
|
||||
- pyparsing=3.0.9=pyhd8ed1ab_0
|
||||
- python=3.10.11=h966fe2a_2
|
||||
- python-dateutil=2.8.2=pyhd8ed1ab_0
|
||||
- python_abi=3.10=2_cp310
|
||||
- pytz=2023.3=pyhd8ed1ab_0
|
||||
- pywin32=305=py310h2bbff1b_0
|
||||
- pyzmq=25.0.2=py310hd77b12b_0
|
||||
- re2=2022.04.01=h0e60522_0
|
||||
- scipy=1.8.1=py310h7c00807_2
|
||||
- setuptools=67.8.0=py310haa95532_0
|
||||
- six=1.16.0=pyh6c4a22f_0
|
||||
- snappy=1.1.9=h6c2663c_0
|
||||
- sqlite=3.41.2=h2bbff1b_0
|
||||
- stack_data=0.6.2=pyhd8ed1ab_0
|
||||
- sympy=1.12=pyh04b8f61_3
|
||||
- tk=8.6.12=h2bbff1b_0
|
||||
- tornado=6.2=py310he2412df_0
|
||||
- traitlets=5.9.0=pyhd8ed1ab_0
|
||||
- typing-extensions=4.6.3=hd8ed1ab_0
|
||||
- typing_extensions=4.6.3=pyha770c72_0
|
||||
- tzdata=2023c=h04d1e81_0
|
||||
- utf8proc=2.6.1=h2bbff1b_0
|
||||
- vc=14.2=h21ff451_1
|
||||
- vs2015_runtime=14.27.29016=h5e58377_2
|
||||
- wcwidth=0.2.6=pyhd8ed1ab_0
|
||||
- wheel=0.38.4=py310haa95532_0
|
||||
- xz=5.4.2=h8cc25b3_0
|
||||
- zeromq=4.3.4=h0e60522_1
|
||||
- zipp=3.15.0=pyhd8ed1ab_0
|
||||
- zlib=1.2.13=h8cc25b3_0
|
||||
- zstd=1.5.5=hd43e919_0
|
||||
@@ -0,0 +1,373 @@
|
||||
import pandas as pd
|
||||
from textwrap import dedent
|
||||
|
||||
# brian 2
|
||||
from brian2 import NeuronGroup, Synapses, PoissonInput, SpikeMonitor, Network
|
||||
from brian2 import mV, ms, Hz
|
||||
|
||||
# file handling
|
||||
from pathlib import Path
|
||||
|
||||
# parallelization
|
||||
from joblib import Parallel, delayed, parallel_backend
|
||||
from time import time
|
||||
|
||||
default_params = {
|
||||
# trials
|
||||
't_run' : 1000 * ms, # duration of trial
|
||||
'n_run' : 30, # number of runs
|
||||
|
||||
# network constants
|
||||
# Kakaria and de Bivort 2017 https://doi.org/10.3389/fnbeh.2017.00008
|
||||
'v_0' : -52 * mV, # resting potential
|
||||
'v_rst' : -52 * mV, # reset potential after spike
|
||||
'v_th' : -45 * mV, # threshold for spiking
|
||||
't_mbr' : 20 * ms, # membrane time scale (capacitance * resistance = .002 * uF * 10. * Mohm)
|
||||
|
||||
# Jürgensen et al https://doi.org/10.1088/2634-4386/ac3ba6
|
||||
'tau' : 5 * ms, # time constant
|
||||
|
||||
# Lazar et al https://doi.org/10.7554/eLife.62362
|
||||
't_rfc' : 2.2 * ms, # refractory period
|
||||
|
||||
# Paul et al 2015 doi: 10.3389/fncel.2015.00029
|
||||
't_dly' : 1.8*ms, # delay for changes in post-synaptic neuron
|
||||
|
||||
# Free parameter
|
||||
'w_syn' : .275 * mV, # weight per synapse (note: modulated by exponential decay)
|
||||
# Default activation rates
|
||||
'r_poi' : 150*Hz, # default rate of the Poisson inputs
|
||||
'r_poi2' : 0*Hz, # default rate of a 2nd class of Poisson inputs
|
||||
'f_poi' : 250, # scaling factor for Poisson synapse; 250 is sufficient to cause spiking
|
||||
|
||||
# equations for neurons # alpha synapse https://doi.org/10.1017/CBO9780511815706; See https://brian2.readthedocs.io/en/stable/user/converting_from_integrated_form.html
|
||||
'eqs' : dedent('''
|
||||
dv/dt = (v_0 - v + g) / t_mbr : volt (unless refractory)
|
||||
dg/dt = -g / tau : volt (unless refractory)
|
||||
rfc : second
|
||||
'''),
|
||||
# condition for spike
|
||||
'eq_th' : 'v > v_th',
|
||||
# rules for spike
|
||||
'eq_rst' : 'v = v_rst; w = 0; g = 0 * mV',
|
||||
}
|
||||
|
||||
|
||||
#######################
|
||||
# brian2 model setup
|
||||
def poi(neu, exc, exc2, params):
|
||||
'''Create PoissonInput for neurons.
|
||||
|
||||
For each neuron in 'names' a PoissonInput is generated and
|
||||
the refractory period of that neuron is set to 0 in NeuronGroup.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
neu : NeuronGroup
|
||||
Defined brian2.NeuronGroup object
|
||||
exc : list
|
||||
Indices of neurons for which to create Poisson input
|
||||
exc2 : list
|
||||
Indices of neurons for which to create Poisson input with `r_poi`
|
||||
params : dict
|
||||
Constants and equations that are used to construct the brian2 network model
|
||||
|
||||
Returns
|
||||
-------
|
||||
pois : list
|
||||
PoissonInput objects for each neuron in 'exc'
|
||||
neu : NeuronGroup
|
||||
NeuronGroup with adjusted refractory periods
|
||||
'''
|
||||
|
||||
pois = []
|
||||
for i in exc:
|
||||
p = PoissonInput(
|
||||
target=neu[i],
|
||||
target_var='v',
|
||||
N=1,
|
||||
rate=params['r_poi'],
|
||||
weight=params['w_syn']*params['f_poi']
|
||||
)
|
||||
neu[i].rfc = 0 * ms # no refractory period for Poisson targets
|
||||
pois.append(p)
|
||||
|
||||
for i in exc2:
|
||||
p = PoissonInput(
|
||||
target=neu[i],
|
||||
target_var='v',
|
||||
N=1,
|
||||
rate=params['r_poi2'],
|
||||
weight=params['w_syn']*params['f_poi']
|
||||
)
|
||||
neu[i].rfc = 0 * ms # no refractory period for Poisson targets
|
||||
pois.append(p)
|
||||
|
||||
return pois, neu
|
||||
|
||||
def silence(slnc, syn):
|
||||
'''Silence neuron by setting weights of all synapses from it to 0
|
||||
|
||||
Parameters
|
||||
----------
|
||||
slnc : list
|
||||
List of neuron indices to silence
|
||||
syn : brian2.Synapses
|
||||
Defined synapses object
|
||||
|
||||
Returns
|
||||
-------
|
||||
syn : brian2.Synapses
|
||||
Synapses with modified weights
|
||||
'''
|
||||
|
||||
for i in slnc:
|
||||
syn.w[' {} == i'.format(i)] = 0*mV
|
||||
|
||||
return syn
|
||||
|
||||
def create_model(path_comp, path_con, params):
|
||||
'''Create default network model.
|
||||
|
||||
Convert the "completeness materialization" and "connectivity" dataframes
|
||||
into a brian2 neural network model. Network constants and equations
|
||||
are defined at the beginning of this file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path_comp : str
|
||||
path to "completeness materialization" dataframe
|
||||
path_con : str
|
||||
path to "connectivity" dataframe
|
||||
params : dict
|
||||
Constants and equations that are used to construct the brian2 network model
|
||||
|
||||
|
||||
Returns
|
||||
-------
|
||||
neu : NeuronGroup
|
||||
brian2.NeuronGroup object with neurons as in 'path_comp'
|
||||
syn : Synapses
|
||||
brian2.Synapses object with connections as in 'path_con'
|
||||
spk_mon : SpikeMonitor
|
||||
brian2.SpikeMonitor object, which records time of spike events
|
||||
'''
|
||||
|
||||
# load neuron connectivity dataframes
|
||||
df_comp = pd.read_csv(path_comp, index_col=0)
|
||||
df_con = pd.read_parquet(path_con)
|
||||
|
||||
neu = NeuronGroup( # create neurons
|
||||
N=len(df_comp),
|
||||
model=params['eqs'],
|
||||
method='linear',
|
||||
threshold=params['eq_th'],
|
||||
reset=params['eq_rst'],
|
||||
refractory='rfc',
|
||||
name='default_neurons',
|
||||
namespace=params,
|
||||
)
|
||||
neu.v = params['v_0'] # initialize values
|
||||
neu.g = 0
|
||||
neu.rfc = params['t_rfc']
|
||||
|
||||
# create synapses
|
||||
syn = Synapses(neu, neu, 'w : volt', on_pre='g += w', delay=params['t_dly'], name='default_synapses')
|
||||
|
||||
# connect synapses
|
||||
i_pre = df_con.loc[:, 'Presynaptic_Index'].values
|
||||
i_post = df_con.loc[:, 'Postsynaptic_Index'].values
|
||||
syn.connect(i=i_pre, j=i_post)
|
||||
|
||||
# define connection weight
|
||||
syn.w = df_con.loc[:,'Excitatory x Connectivity'].values * params['w_syn']
|
||||
|
||||
# object to record spikes
|
||||
spk_mon = SpikeMonitor(neu)
|
||||
|
||||
return neu, syn, spk_mon
|
||||
|
||||
#####################
|
||||
# running simulations
|
||||
def get_spk_trn(spk_mon):
|
||||
'''Extracts spike times from 'spk_mon'
|
||||
|
||||
The spike times recorded in the SpikeMonitor object during
|
||||
simulation are converted to a list of times for each neurons.
|
||||
Returns dict with "brian ID": "list of spike times".
|
||||
|
||||
Parameters
|
||||
----------
|
||||
spk_mon : SpikeMonitor
|
||||
Contains recorded spike times
|
||||
|
||||
Returns
|
||||
-------
|
||||
spk_trn : dict
|
||||
Mapping between brian neuron IDs and spike times
|
||||
'''
|
||||
|
||||
spk_trn = {k: v for k, v in spk_mon.spike_trains().items() if len(v)}
|
||||
|
||||
return spk_trn
|
||||
|
||||
def construct_dataframe(res, exp_name, i2flyid):
|
||||
'''Take spike time dict and collects spikes in pandas dataframe
|
||||
|
||||
Parameters
|
||||
----------
|
||||
res : list
|
||||
List with spike time dicts for each trial
|
||||
exp_name : str
|
||||
Name of the experiment
|
||||
i2flyid : dict
|
||||
Mapping between Brian IDs and flywire IDs
|
||||
|
||||
Returns
|
||||
-------
|
||||
df : pandas.DataFrame
|
||||
Dataframe where each row is one spike
|
||||
'''
|
||||
|
||||
ids, ts, nrun = [], [], []
|
||||
|
||||
for n, i in enumerate(res):
|
||||
for j, k in i.items():
|
||||
ids.extend([j for _ in k])
|
||||
nrun.extend([n for _ in k])
|
||||
ts.extend([float(l) for l in k])
|
||||
|
||||
d = {
|
||||
't': ts,
|
||||
'trial': nrun,
|
||||
'flywire_id': [i2flyid[i] for i in ids],
|
||||
'exp_name': exp_name
|
||||
}
|
||||
df = pd.DataFrame(d)
|
||||
|
||||
return df
|
||||
|
||||
def run_trial(exc, exc2, slnc, path_comp, path_con, params):
|
||||
'''Run single trial of coactivation/silencng experiment
|
||||
|
||||
During the coactivation experiment, the neurons in 'exc' are
|
||||
Poisson inputs. The simulation runs for 't_run'.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
exc: list
|
||||
contains indices of neurons for PoissonInput
|
||||
exc2: list
|
||||
contains indices of neurons for PoissonInput of different frequency `r_poi2`
|
||||
slnc: list
|
||||
contains indices of neurons to silence
|
||||
path_comp: Path
|
||||
path to "completeness materialization" dataframe
|
||||
path_con: Path
|
||||
path to "connectivity" dataframe
|
||||
params : dict
|
||||
Constants and equations that are used to construct the brian2 network model
|
||||
|
||||
Returns
|
||||
-------
|
||||
spk_trn : dict
|
||||
Mapping between brian neuron IDs and spike times
|
||||
'''
|
||||
|
||||
# get default network
|
||||
neu, syn, spk_mon = create_model(path_comp, path_con, params)
|
||||
# define Poisson input for excitation
|
||||
poi_inp, neu = poi(neu, exc, exc2, params)
|
||||
# silence neurons
|
||||
syn = silence(slnc, syn)
|
||||
# collect in Network object
|
||||
net = Network(neu, syn, spk_mon, *poi_inp)
|
||||
|
||||
# run simulation
|
||||
net.run(duration=params['t_run'])
|
||||
|
||||
# spike times
|
||||
spk_trn = get_spk_trn(spk_mon)
|
||||
|
||||
return spk_trn
|
||||
|
||||
|
||||
def run_exp(exp_name, neu_exc, path_res, path_comp, path_con,
|
||||
params=default_params, neu_slnc=[], neu_exc2=[],
|
||||
n_proc=-1, force_overwrite=False):
|
||||
'''
|
||||
Run default network experiment
|
||||
Neurons in `neu_exc` are Poisson external inputs
|
||||
Neurons in `neu_slnc` are silenced
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
exp_name: str
|
||||
name of the experiment
|
||||
neu_exc: list
|
||||
contains custom names or flywire IDs of neurons to be excited
|
||||
path_res: str
|
||||
path to the output folder where spike data is stored
|
||||
path_comp: str
|
||||
path to "completeness materialization" dataframe
|
||||
path_con: str
|
||||
path to "connectivity" dataframe
|
||||
params : dict
|
||||
Constants and equations that are used to construct the brian2 network model
|
||||
neu_slnc : list (optional)
|
||||
contains custom names or flywire IDs of neurons to be silenced
|
||||
neu_exc2 : list (optional)
|
||||
contains custom names or flywire IDs of neurons to be excited with different frequency
|
||||
requires `r_poi2` to be defined in `params`
|
||||
n_proc: int (optional)
|
||||
number of cores to be used for parallel runs
|
||||
default: -1 (use all available cores)
|
||||
n_proc=1 is equivalent serial code
|
||||
force_overwrite : bool (optional)
|
||||
If True, overwrite output files, else skip simulation, by default False
|
||||
'''
|
||||
|
||||
# convert to Path objects
|
||||
path_res, path_comp, path_con = [ Path(i) for i in [path_res, path_comp, path_con] ]
|
||||
|
||||
# define output files
|
||||
path_save = path_res / '{}.parquet'.format(exp_name)
|
||||
if path_save.is_file() and not force_overwrite:
|
||||
print('>>> Skipping experiment {} because {} exists and force_overwrite = {}'.format(exp_name, path_save, force_overwrite))
|
||||
return
|
||||
|
||||
# load name/id mappings
|
||||
df_comp = pd.read_csv(path_comp, index_col=0) # load completeness dataframe
|
||||
flyid2i = {j: i for i, j in enumerate(df_comp.index)} # flywire id: biran ID
|
||||
i2flyid = {j: i for i, j in flyid2i.items()} # brian ID: flywire ID
|
||||
|
||||
# print info
|
||||
print('>>> Experiment: {}'.format(exp_name))
|
||||
print(' Output file: {}'.format(path_save))
|
||||
print(' Excited neurons: {}'.format(len(neu_exc + neu_exc2)))
|
||||
if neu_slnc:
|
||||
print(' Silenced neurons: {}'.format(len(neu_slnc)))
|
||||
|
||||
# start parallel calculation
|
||||
n_run = params['n_run']
|
||||
start = time()
|
||||
with parallel_backend('loky', n_jobs=n_proc):
|
||||
exc = [ flyid2i[n] for n in neu_exc ]
|
||||
exc2 = [ flyid2i[n] for n in neu_exc2 ]
|
||||
slnc = [ flyid2i[n] for n in neu_slnc ]
|
||||
res = Parallel()(
|
||||
delayed(
|
||||
run_trial)(exc, exc2, slnc, path_comp, path_con, params) for _ in range(n_run))
|
||||
|
||||
|
||||
# print simulation time
|
||||
walltime = time() - start
|
||||
print(' Elapsed time: {} s'.format(int(walltime)))
|
||||
|
||||
# dataframe with spike times
|
||||
df = construct_dataframe(res, exp_name, i2flyid)
|
||||
|
||||
# store spike data
|
||||
df.to_parquet(path_save, compression='brotli')
|
||||
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"binary": "/data/digital-drosophila/experiments/phase4ad-author-semantics/gpu-reference/extension-build/male_cns_lif_deterministic_v2_ext.so",
|
||||
"binary_sha256": "57f59f634cc8e3b03f4674a3ddb11ec933ab124feb0b9fa89d08fe26e60fcb7e",
|
||||
"build_seconds": 55.24202698399972,
|
||||
"flags": {
|
||||
"cuda": [
|
||||
"-O3",
|
||||
"--fmad=false",
|
||||
"-std=c++20"
|
||||
],
|
||||
"cxx": [
|
||||
"-O3",
|
||||
"-ffp-contract=off"
|
||||
]
|
||||
},
|
||||
"status": "PASS"
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
+20
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"weights_mv": [
|
||||
28.599998474121094,
|
||||
64.625,
|
||||
62.14999771118164,
|
||||
11.824999809265137
|
||||
],
|
||||
"weights_volt": [
|
||||
0.028599999845027924,
|
||||
0.06462500244379044,
|
||||
0.062150001525878906,
|
||||
0.011824999935925007
|
||||
]
|
||||
}
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
+6
@@ -0,0 +1,6 @@
|
||||
global_neuron_idx,local_index
|
||||
1197,0
|
||||
1381,1
|
||||
1511,2
|
||||
82312,3
|
||||
133666,4
|
||||
|
+102
@@ -0,0 +1,102 @@
|
||||
{
|
||||
"all_package_pins_match": true,
|
||||
"author_platform": "Windows",
|
||||
"author_python": "3.10.11",
|
||||
"binary_identical_author_environment_claimed": false,
|
||||
"packages": {
|
||||
"brian2": {
|
||||
"actual": "2.5.1",
|
||||
"expected": "2.5.1",
|
||||
"matches": true
|
||||
},
|
||||
"cython": {
|
||||
"actual": "0.29.33",
|
||||
"expected": "0.29.33",
|
||||
"matches": true
|
||||
},
|
||||
"jinja2": {
|
||||
"actual": "2.11.3",
|
||||
"expected": "2.11.3",
|
||||
"matches": true
|
||||
},
|
||||
"joblib": {
|
||||
"actual": "1.2.0",
|
||||
"expected": "1.2.0",
|
||||
"matches": true
|
||||
},
|
||||
"markupsafe": {
|
||||
"actual": "1.1.1",
|
||||
"expected": "1.1.1",
|
||||
"matches": true
|
||||
},
|
||||
"mpmath": {
|
||||
"actual": "1.3.0",
|
||||
"expected": "1.3.0",
|
||||
"matches": true
|
||||
},
|
||||
"numpy": {
|
||||
"actual": "1.22.3",
|
||||
"expected": "1.22.3",
|
||||
"matches": true
|
||||
},
|
||||
"packaging": {
|
||||
"actual": "23.1",
|
||||
"expected": "23.1",
|
||||
"matches": true
|
||||
},
|
||||
"pandas": {
|
||||
"actual": "1.4.3",
|
||||
"expected": "1.4.3",
|
||||
"matches": true
|
||||
},
|
||||
"pyarrow": {
|
||||
"actual": "11.0.0",
|
||||
"expected": "11.0.0",
|
||||
"matches": true
|
||||
},
|
||||
"pyparsing": {
|
||||
"actual": "3.0.9",
|
||||
"expected": "3.0.9",
|
||||
"matches": true
|
||||
},
|
||||
"python-dateutil": {
|
||||
"actual": "2.8.2",
|
||||
"expected": "2.8.2",
|
||||
"matches": true
|
||||
},
|
||||
"pytz": {
|
||||
"actual": "2023.3",
|
||||
"expected": "2023.3",
|
||||
"matches": true
|
||||
},
|
||||
"scipy": {
|
||||
"actual": "1.8.1",
|
||||
"expected": "1.8.1",
|
||||
"matches": true
|
||||
},
|
||||
"setuptools": {
|
||||
"actual": "67.8.0",
|
||||
"expected": "67.8.0",
|
||||
"matches": true
|
||||
},
|
||||
"six": {
|
||||
"actual": "1.16.0",
|
||||
"expected": "1.16.0",
|
||||
"matches": true
|
||||
},
|
||||
"sympy": {
|
||||
"actual": "1.12",
|
||||
"expected": "1.12",
|
||||
"matches": true
|
||||
},
|
||||
"wheel": {
|
||||
"actual": "0.38.4",
|
||||
"expected": "0.38.4",
|
||||
"matches": true
|
||||
}
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12",
|
||||
"python_executable": "/data/digital-drosophila/experiments/phase4ad-author-semantics/environment/.venv/bin/python",
|
||||
"python_patch_matches": false
|
||||
}
|
||||
+127
@@ -0,0 +1,127 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_7",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_seed_injection",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_8",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"user_code": "_cond = v > v_th",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"user_code": "None",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes",
|
||||
"type": "CallableProxyType"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"user_code": "None",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"user_code": "None",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_g = _array_default_neurons_g[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"user_code": "None",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+123
@@ -0,0 +1,123 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"config": {
|
||||
"author_precisions": [
|
||||
"float64",
|
||||
"float32"
|
||||
],
|
||||
"comparison": {
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"float64_state_max_abs_tolerance_mv": 1e-08,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"component_policies": [
|
||||
"frozen",
|
||||
"reset_g_only",
|
||||
"refractory_gate_only",
|
||||
"reset_and_refractory"
|
||||
],
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"human_authorization": "\u5141\u8bb8\u72ec\u7acb\u65b0\u6a21\u578b\u5206\u652f\u6d4b\u8bd5\u52a8\u529b\u5b66\u5047\u8bbe",
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"motifs": [
|
||||
"pair",
|
||||
"trio",
|
||||
"five",
|
||||
"feedforward",
|
||||
"no_input"
|
||||
],
|
||||
"name": "author-semantics-lab-v1",
|
||||
"no_training_or_candidate": true,
|
||||
"phase": "4AD",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"root": "/data/digital-drosophila",
|
||||
"seed_margin_mv": 1.0,
|
||||
"seed_node": 1381
|
||||
},
|
||||
"delay_steps": 18,
|
||||
"dt_ms": 0.1,
|
||||
"edge_ids": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"external_arrivals": {
|
||||
"events": 0,
|
||||
"ordering": "after default_synapses.pre"
|
||||
},
|
||||
"internal_voltage_unit": "volt",
|
||||
"nodes": [
|
||||
1197,
|
||||
1381,
|
||||
1511,
|
||||
82312,
|
||||
133666
|
||||
],
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"config": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"config": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"config": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"config": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"config": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"config": 0.275
|
||||
}
|
||||
},
|
||||
"precision": "float32",
|
||||
"preferences": {
|
||||
"codegen.target": "numpy",
|
||||
"core.default_float_dtype": "float32",
|
||||
"legacy.refractory_timing": false
|
||||
},
|
||||
"refractory_steps": 22,
|
||||
"returned_voltage_unit": "mV",
|
||||
"seed": {
|
||||
"injections": 0,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381
|
||||
},
|
||||
"steps": 10000,
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference"
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_seed_injection (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"applied_mv": -43.99999976158142,
|
||||
"injections": 1,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381,
|
||||
"spiked_at_zero": true
|
||||
}
|
||||
+20
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"weights_mv": [
|
||||
28.599998474121094,
|
||||
64.625,
|
||||
62.14999771118164,
|
||||
11.824999809265137
|
||||
],
|
||||
"weights_volt": [
|
||||
0.028599999845027924,
|
||||
0.06462500244379044,
|
||||
0.062150001525878906,
|
||||
0.011824999935925007
|
||||
]
|
||||
}
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"exact_events": true,
|
||||
"exact_counts": true,
|
||||
"ref_exact": true,
|
||||
"max_abs_mv": 0.00020179152488708496,
|
||||
"mean_abs_mv": 1.9821550031807453e-05,
|
||||
"same_event_prefix_max_abs_mv": 0.00020179152488708496,
|
||||
"first_event_divergence_step": null,
|
||||
"tolerance_mv": 0.001,
|
||||
"pass_oracle": true
|
||||
}
|
||||
+6
@@ -0,0 +1,6 @@
|
||||
global_neuron_idx,local_index
|
||||
1197,0
|
||||
1381,1
|
||||
1511,2
|
||||
82312,3
|
||||
133666,4
|
||||
|
+102
@@ -0,0 +1,102 @@
|
||||
{
|
||||
"all_package_pins_match": true,
|
||||
"author_platform": "Windows",
|
||||
"author_python": "3.10.11",
|
||||
"binary_identical_author_environment_claimed": false,
|
||||
"packages": {
|
||||
"brian2": {
|
||||
"actual": "2.5.1",
|
||||
"expected": "2.5.1",
|
||||
"matches": true
|
||||
},
|
||||
"cython": {
|
||||
"actual": "0.29.33",
|
||||
"expected": "0.29.33",
|
||||
"matches": true
|
||||
},
|
||||
"jinja2": {
|
||||
"actual": "2.11.3",
|
||||
"expected": "2.11.3",
|
||||
"matches": true
|
||||
},
|
||||
"joblib": {
|
||||
"actual": "1.2.0",
|
||||
"expected": "1.2.0",
|
||||
"matches": true
|
||||
},
|
||||
"markupsafe": {
|
||||
"actual": "1.1.1",
|
||||
"expected": "1.1.1",
|
||||
"matches": true
|
||||
},
|
||||
"mpmath": {
|
||||
"actual": "1.3.0",
|
||||
"expected": "1.3.0",
|
||||
"matches": true
|
||||
},
|
||||
"numpy": {
|
||||
"actual": "1.22.3",
|
||||
"expected": "1.22.3",
|
||||
"matches": true
|
||||
},
|
||||
"packaging": {
|
||||
"actual": "23.1",
|
||||
"expected": "23.1",
|
||||
"matches": true
|
||||
},
|
||||
"pandas": {
|
||||
"actual": "1.4.3",
|
||||
"expected": "1.4.3",
|
||||
"matches": true
|
||||
},
|
||||
"pyarrow": {
|
||||
"actual": "11.0.0",
|
||||
"expected": "11.0.0",
|
||||
"matches": true
|
||||
},
|
||||
"pyparsing": {
|
||||
"actual": "3.0.9",
|
||||
"expected": "3.0.9",
|
||||
"matches": true
|
||||
},
|
||||
"python-dateutil": {
|
||||
"actual": "2.8.2",
|
||||
"expected": "2.8.2",
|
||||
"matches": true
|
||||
},
|
||||
"pytz": {
|
||||
"actual": "2023.3",
|
||||
"expected": "2023.3",
|
||||
"matches": true
|
||||
},
|
||||
"scipy": {
|
||||
"actual": "1.8.1",
|
||||
"expected": "1.8.1",
|
||||
"matches": true
|
||||
},
|
||||
"setuptools": {
|
||||
"actual": "67.8.0",
|
||||
"expected": "67.8.0",
|
||||
"matches": true
|
||||
},
|
||||
"six": {
|
||||
"actual": "1.16.0",
|
||||
"expected": "1.16.0",
|
||||
"matches": true
|
||||
},
|
||||
"sympy": {
|
||||
"actual": "1.12",
|
||||
"expected": "1.12",
|
||||
"matches": true
|
||||
},
|
||||
"wheel": {
|
||||
"actual": "0.38.4",
|
||||
"expected": "0.38.4",
|
||||
"matches": true
|
||||
}
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12",
|
||||
"python_executable": "/data/digital-drosophila/experiments/phase4ad-author-semantics/environment/.venv/bin/python",
|
||||
"python_patch_matches": false
|
||||
}
|
||||
+127
@@ -0,0 +1,127 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_6",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_seed_injection",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_7",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"user_code": "_cond = v > v_th",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"user_code": "None",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes",
|
||||
"type": "CallableProxyType"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_7",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"user_code": "None",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_7",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"user_code": "None",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_g = _array_default_neurons_g[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"user_code": "None",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
@@ -0,0 +1,123 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"config": {
|
||||
"author_precisions": [
|
||||
"float64",
|
||||
"float32"
|
||||
],
|
||||
"comparison": {
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"float64_state_max_abs_tolerance_mv": 1e-08,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"component_policies": [
|
||||
"frozen",
|
||||
"reset_g_only",
|
||||
"refractory_gate_only",
|
||||
"reset_and_refractory"
|
||||
],
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"human_authorization": "\u5141\u8bb8\u72ec\u7acb\u65b0\u6a21\u578b\u5206\u652f\u6d4b\u8bd5\u52a8\u529b\u5b66\u5047\u8bbe",
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"motifs": [
|
||||
"pair",
|
||||
"trio",
|
||||
"five",
|
||||
"feedforward",
|
||||
"no_input"
|
||||
],
|
||||
"name": "author-semantics-lab-v1",
|
||||
"no_training_or_candidate": true,
|
||||
"phase": "4AD",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"root": "/data/digital-drosophila",
|
||||
"seed_margin_mv": 1.0,
|
||||
"seed_node": 1381
|
||||
},
|
||||
"delay_steps": 18,
|
||||
"dt_ms": 0.1,
|
||||
"edge_ids": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"external_arrivals": {
|
||||
"events": 0,
|
||||
"ordering": "after default_synapses.pre"
|
||||
},
|
||||
"internal_voltage_unit": "volt",
|
||||
"nodes": [
|
||||
1197,
|
||||
1381,
|
||||
1511,
|
||||
82312,
|
||||
133666
|
||||
],
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"config": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"config": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"config": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"config": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"config": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"config": 0.275
|
||||
}
|
||||
},
|
||||
"precision": "float32",
|
||||
"preferences": {
|
||||
"codegen.target": "numpy",
|
||||
"core.default_float_dtype": "float32",
|
||||
"legacy.refractory_timing": false
|
||||
},
|
||||
"refractory_steps": 22,
|
||||
"returned_voltage_unit": "mV",
|
||||
"seed": {
|
||||
"injections": 0,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381
|
||||
},
|
||||
"steps": 10000,
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference"
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_seed_injection (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"applied_mv": -43.99999976158142,
|
||||
"injections": 1,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381,
|
||||
"spiked_at_zero": true
|
||||
}
|
||||
+20
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"weights_mv": [
|
||||
28.6,
|
||||
64.625,
|
||||
62.150000000000006,
|
||||
11.825000000000001
|
||||
],
|
||||
"weights_volt": [
|
||||
0.0286,
|
||||
0.064625,
|
||||
0.062150000000000004,
|
||||
0.011825
|
||||
]
|
||||
}
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
+6
@@ -0,0 +1,6 @@
|
||||
global_neuron_idx,local_index
|
||||
1197,0
|
||||
1381,1
|
||||
1511,2
|
||||
82312,3
|
||||
133666,4
|
||||
|
+102
@@ -0,0 +1,102 @@
|
||||
{
|
||||
"all_package_pins_match": true,
|
||||
"author_platform": "Windows",
|
||||
"author_python": "3.10.11",
|
||||
"binary_identical_author_environment_claimed": false,
|
||||
"packages": {
|
||||
"brian2": {
|
||||
"actual": "2.5.1",
|
||||
"expected": "2.5.1",
|
||||
"matches": true
|
||||
},
|
||||
"cython": {
|
||||
"actual": "0.29.33",
|
||||
"expected": "0.29.33",
|
||||
"matches": true
|
||||
},
|
||||
"jinja2": {
|
||||
"actual": "2.11.3",
|
||||
"expected": "2.11.3",
|
||||
"matches": true
|
||||
},
|
||||
"joblib": {
|
||||
"actual": "1.2.0",
|
||||
"expected": "1.2.0",
|
||||
"matches": true
|
||||
},
|
||||
"markupsafe": {
|
||||
"actual": "1.1.1",
|
||||
"expected": "1.1.1",
|
||||
"matches": true
|
||||
},
|
||||
"mpmath": {
|
||||
"actual": "1.3.0",
|
||||
"expected": "1.3.0",
|
||||
"matches": true
|
||||
},
|
||||
"numpy": {
|
||||
"actual": "1.22.3",
|
||||
"expected": "1.22.3",
|
||||
"matches": true
|
||||
},
|
||||
"packaging": {
|
||||
"actual": "23.1",
|
||||
"expected": "23.1",
|
||||
"matches": true
|
||||
},
|
||||
"pandas": {
|
||||
"actual": "1.4.3",
|
||||
"expected": "1.4.3",
|
||||
"matches": true
|
||||
},
|
||||
"pyarrow": {
|
||||
"actual": "11.0.0",
|
||||
"expected": "11.0.0",
|
||||
"matches": true
|
||||
},
|
||||
"pyparsing": {
|
||||
"actual": "3.0.9",
|
||||
"expected": "3.0.9",
|
||||
"matches": true
|
||||
},
|
||||
"python-dateutil": {
|
||||
"actual": "2.8.2",
|
||||
"expected": "2.8.2",
|
||||
"matches": true
|
||||
},
|
||||
"pytz": {
|
||||
"actual": "2023.3",
|
||||
"expected": "2023.3",
|
||||
"matches": true
|
||||
},
|
||||
"scipy": {
|
||||
"actual": "1.8.1",
|
||||
"expected": "1.8.1",
|
||||
"matches": true
|
||||
},
|
||||
"setuptools": {
|
||||
"actual": "67.8.0",
|
||||
"expected": "67.8.0",
|
||||
"matches": true
|
||||
},
|
||||
"six": {
|
||||
"actual": "1.16.0",
|
||||
"expected": "1.16.0",
|
||||
"matches": true
|
||||
},
|
||||
"sympy": {
|
||||
"actual": "1.12",
|
||||
"expected": "1.12",
|
||||
"matches": true
|
||||
},
|
||||
"wheel": {
|
||||
"actual": "0.38.4",
|
||||
"expected": "0.38.4",
|
||||
"matches": true
|
||||
}
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12",
|
||||
"python_executable": "/data/digital-drosophila/experiments/phase4ad-author-semantics/environment/.venv/bin/python",
|
||||
"python_patch_matches": false
|
||||
}
|
||||
+127
@@ -0,0 +1,127 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_5",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_seed_injection",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_6",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"user_code": "_cond = v > v_th",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"user_code": "None",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes",
|
||||
"type": "CallableProxyType"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_6",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"user_code": "None",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_6",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"user_code": "None",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_g = _array_default_neurons_g[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"user_code": "None",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+123
@@ -0,0 +1,123 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"config": {
|
||||
"author_precisions": [
|
||||
"float64",
|
||||
"float32"
|
||||
],
|
||||
"comparison": {
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"float64_state_max_abs_tolerance_mv": 1e-08,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"component_policies": [
|
||||
"frozen",
|
||||
"reset_g_only",
|
||||
"refractory_gate_only",
|
||||
"reset_and_refractory"
|
||||
],
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"human_authorization": "\u5141\u8bb8\u72ec\u7acb\u65b0\u6a21\u578b\u5206\u652f\u6d4b\u8bd5\u52a8\u529b\u5b66\u5047\u8bbe",
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"motifs": [
|
||||
"pair",
|
||||
"trio",
|
||||
"five",
|
||||
"feedforward",
|
||||
"no_input"
|
||||
],
|
||||
"name": "author-semantics-lab-v1",
|
||||
"no_training_or_candidate": true,
|
||||
"phase": "4AD",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"root": "/data/digital-drosophila",
|
||||
"seed_margin_mv": 1.0,
|
||||
"seed_node": 1381
|
||||
},
|
||||
"delay_steps": 18,
|
||||
"dt_ms": 0.1,
|
||||
"edge_ids": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"external_arrivals": {
|
||||
"events": 0,
|
||||
"ordering": "after default_synapses.pre"
|
||||
},
|
||||
"internal_voltage_unit": "volt",
|
||||
"nodes": [
|
||||
1197,
|
||||
1381,
|
||||
1511,
|
||||
82312,
|
||||
133666
|
||||
],
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"config": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"config": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"config": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"config": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"config": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"config": 0.275
|
||||
}
|
||||
},
|
||||
"precision": "float64",
|
||||
"preferences": {
|
||||
"codegen.target": "numpy",
|
||||
"core.default_float_dtype": "float64",
|
||||
"legacy.refractory_timing": false
|
||||
},
|
||||
"refractory_steps": 22,
|
||||
"returned_voltage_unit": "mV",
|
||||
"seed": {
|
||||
"injections": 0,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381
|
||||
},
|
||||
"steps": 10000,
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference"
|
||||
}
|
||||
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_seed_injection (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
+7
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"applied_mv": -44.0,
|
||||
"injections": 1,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381,
|
||||
"spiked_at_zero": true
|
||||
}
|
||||
+20
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1337065,
|
||||
1467355,
|
||||
1559765,
|
||||
20992891
|
||||
],
|
||||
"weights_mv": [
|
||||
28.6,
|
||||
64.625,
|
||||
62.150000000000006,
|
||||
11.825000000000001
|
||||
],
|
||||
"weights_volt": [
|
||||
0.0286,
|
||||
0.064625,
|
||||
0.062150000000000004,
|
||||
0.011825
|
||||
]
|
||||
}
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"exact_events": true,
|
||||
"exact_counts": true,
|
||||
"ref_exact": true,
|
||||
"max_abs_mv": 3.765876499528531e-13,
|
||||
"mean_abs_mv": 3.893371799264915e-14,
|
||||
"same_event_prefix_max_abs_mv": 3.765876499528531e-13,
|
||||
"first_event_divergence_step": null,
|
||||
"tolerance_mv": 1e-08,
|
||||
"pass_oracle": true
|
||||
}
|
||||
+6
@@ -0,0 +1,6 @@
|
||||
global_neuron_idx,local_index
|
||||
1197,0
|
||||
1381,1
|
||||
1511,2
|
||||
82312,3
|
||||
133666,4
|
||||
|
+102
@@ -0,0 +1,102 @@
|
||||
{
|
||||
"all_package_pins_match": true,
|
||||
"author_platform": "Windows",
|
||||
"author_python": "3.10.11",
|
||||
"binary_identical_author_environment_claimed": false,
|
||||
"packages": {
|
||||
"brian2": {
|
||||
"actual": "2.5.1",
|
||||
"expected": "2.5.1",
|
||||
"matches": true
|
||||
},
|
||||
"cython": {
|
||||
"actual": "0.29.33",
|
||||
"expected": "0.29.33",
|
||||
"matches": true
|
||||
},
|
||||
"jinja2": {
|
||||
"actual": "2.11.3",
|
||||
"expected": "2.11.3",
|
||||
"matches": true
|
||||
},
|
||||
"joblib": {
|
||||
"actual": "1.2.0",
|
||||
"expected": "1.2.0",
|
||||
"matches": true
|
||||
},
|
||||
"markupsafe": {
|
||||
"actual": "1.1.1",
|
||||
"expected": "1.1.1",
|
||||
"matches": true
|
||||
},
|
||||
"mpmath": {
|
||||
"actual": "1.3.0",
|
||||
"expected": "1.3.0",
|
||||
"matches": true
|
||||
},
|
||||
"numpy": {
|
||||
"actual": "1.22.3",
|
||||
"expected": "1.22.3",
|
||||
"matches": true
|
||||
},
|
||||
"packaging": {
|
||||
"actual": "23.1",
|
||||
"expected": "23.1",
|
||||
"matches": true
|
||||
},
|
||||
"pandas": {
|
||||
"actual": "1.4.3",
|
||||
"expected": "1.4.3",
|
||||
"matches": true
|
||||
},
|
||||
"pyarrow": {
|
||||
"actual": "11.0.0",
|
||||
"expected": "11.0.0",
|
||||
"matches": true
|
||||
},
|
||||
"pyparsing": {
|
||||
"actual": "3.0.9",
|
||||
"expected": "3.0.9",
|
||||
"matches": true
|
||||
},
|
||||
"python-dateutil": {
|
||||
"actual": "2.8.2",
|
||||
"expected": "2.8.2",
|
||||
"matches": true
|
||||
},
|
||||
"pytz": {
|
||||
"actual": "2023.3",
|
||||
"expected": "2023.3",
|
||||
"matches": true
|
||||
},
|
||||
"scipy": {
|
||||
"actual": "1.8.1",
|
||||
"expected": "1.8.1",
|
||||
"matches": true
|
||||
},
|
||||
"setuptools": {
|
||||
"actual": "67.8.0",
|
||||
"expected": "67.8.0",
|
||||
"matches": true
|
||||
},
|
||||
"six": {
|
||||
"actual": "1.16.0",
|
||||
"expected": "1.16.0",
|
||||
"matches": true
|
||||
},
|
||||
"sympy": {
|
||||
"actual": "1.12",
|
||||
"expected": "1.12",
|
||||
"matches": true
|
||||
},
|
||||
"wheel": {
|
||||
"actual": "0.38.4",
|
||||
"expected": "0.38.4",
|
||||
"matches": true
|
||||
}
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12",
|
||||
"python_executable": "/data/digital-drosophila/experiments/phase4ad-author-semantics/environment/.venv/bin/python",
|
||||
"python_patch_matches": false
|
||||
}
|
||||
+127
@@ -0,0 +1,127 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_seed_injection",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_5",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"user_code": "_cond = v > v_th",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"user_code": "None",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes",
|
||||
"type": "CallableProxyType"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_5",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"user_code": "None",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_5",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"user_code": "None",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_g = _array_default_neurons_g[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"user_code": "None",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
+4
@@ -0,0 +1,4 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
||||
51,1511
|
||||
104,1197
|
||||
|
@@ -0,0 +1,123 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"config": {
|
||||
"author_precisions": [
|
||||
"float64",
|
||||
"float32"
|
||||
],
|
||||
"comparison": {
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"float64_state_max_abs_tolerance_mv": 1e-08,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"component_policies": [
|
||||
"frozen",
|
||||
"reset_g_only",
|
||||
"refractory_gate_only",
|
||||
"reset_and_refractory"
|
||||
],
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"human_authorization": "\u5141\u8bb8\u72ec\u7acb\u65b0\u6a21\u578b\u5206\u652f\u6d4b\u8bd5\u52a8\u529b\u5b66\u5047\u8bbe",
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"motifs": [
|
||||
"pair",
|
||||
"trio",
|
||||
"five",
|
||||
"feedforward",
|
||||
"no_input"
|
||||
],
|
||||
"name": "author-semantics-lab-v1",
|
||||
"no_training_or_candidate": true,
|
||||
"phase": "4AD",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"root": "/data/digital-drosophila",
|
||||
"seed_margin_mv": 1.0,
|
||||
"seed_node": 1381
|
||||
},
|
||||
"delay_steps": 18,
|
||||
"dt_ms": 0.1,
|
||||
"edge_ids": [
|
||||
1337065,
|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
"external_arrivals": {
|
||||
"events": 0,
|
||||
"ordering": "after default_synapses.pre"
|
||||
},
|
||||
"internal_voltage_unit": "volt",
|
||||
"nodes": [
|
||||
1197,
|
||||
1381,
|
||||
1511,
|
||||
82312,
|
||||
133666
|
||||
],
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"config": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"config": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"config": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"config": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"config": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"config": 0.275
|
||||
}
|
||||
},
|
||||
"precision": "float64",
|
||||
"preferences": {
|
||||
"codegen.target": "numpy",
|
||||
"core.default_float_dtype": "float64",
|
||||
"legacy.refractory_timing": false
|
||||
},
|
||||
"refractory_steps": 22,
|
||||
"returned_voltage_unit": "mV",
|
||||
"seed": {
|
||||
"injections": 0,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381
|
||||
},
|
||||
"steps": 10000,
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference"
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_seed_injection (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"applied_mv": -44.0,
|
||||
"injections": 1,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381,
|
||||
"spiked_at_zero": true
|
||||
}
|
||||
+47
@@ -0,0 +1,47 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1336648,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
"weights_mv": [
|
||||
57.75,
|
||||
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|
||||
28.599998474121094,
|
||||
71.2249984741211,
|
||||
64.625,
|
||||
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|
||||
62.14999771118164,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
],
|
||||
"weights_volt": [
|
||||
0.057750001549720764,
|
||||
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|
||||
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|
||||
0.07122500240802765,
|
||||
0.06462500244379044,
|
||||
0.01759999990463257,
|
||||
0.062150001525878906,
|
||||
0.07782500237226486,
|
||||
0.01924999989569187,
|
||||
0.026125000789761543,
|
||||
0.015675000846385956,
|
||||
0.020899999886751175,
|
||||
0.011824999935925007
|
||||
]
|
||||
}
|
||||
+611
@@ -0,0 +1,611 @@
|
||||
step,neuron_idx
|
||||
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|
||||
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||||
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||||
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|
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|
||||
|
+6
@@ -0,0 +1,6 @@
|
||||
global_neuron_idx,local_index
|
||||
1197,0
|
||||
1381,1
|
||||
1511,2
|
||||
82312,3
|
||||
133666,4
|
||||
|
+102
@@ -0,0 +1,102 @@
|
||||
{
|
||||
"all_package_pins_match": true,
|
||||
"author_platform": "Windows",
|
||||
"author_python": "3.10.11",
|
||||
"binary_identical_author_environment_claimed": false,
|
||||
"packages": {
|
||||
"brian2": {
|
||||
"actual": "2.5.1",
|
||||
"expected": "2.5.1",
|
||||
"matches": true
|
||||
},
|
||||
"cython": {
|
||||
"actual": "0.29.33",
|
||||
"expected": "0.29.33",
|
||||
"matches": true
|
||||
},
|
||||
"jinja2": {
|
||||
"actual": "2.11.3",
|
||||
"expected": "2.11.3",
|
||||
"matches": true
|
||||
},
|
||||
"joblib": {
|
||||
"actual": "1.2.0",
|
||||
"expected": "1.2.0",
|
||||
"matches": true
|
||||
},
|
||||
"markupsafe": {
|
||||
"actual": "1.1.1",
|
||||
"expected": "1.1.1",
|
||||
"matches": true
|
||||
},
|
||||
"mpmath": {
|
||||
"actual": "1.3.0",
|
||||
"expected": "1.3.0",
|
||||
"matches": true
|
||||
},
|
||||
"numpy": {
|
||||
"actual": "1.22.3",
|
||||
"expected": "1.22.3",
|
||||
"matches": true
|
||||
},
|
||||
"packaging": {
|
||||
"actual": "23.1",
|
||||
"expected": "23.1",
|
||||
"matches": true
|
||||
},
|
||||
"pandas": {
|
||||
"actual": "1.4.3",
|
||||
"expected": "1.4.3",
|
||||
"matches": true
|
||||
},
|
||||
"pyarrow": {
|
||||
"actual": "11.0.0",
|
||||
"expected": "11.0.0",
|
||||
"matches": true
|
||||
},
|
||||
"pyparsing": {
|
||||
"actual": "3.0.9",
|
||||
"expected": "3.0.9",
|
||||
"matches": true
|
||||
},
|
||||
"python-dateutil": {
|
||||
"actual": "2.8.2",
|
||||
"expected": "2.8.2",
|
||||
"matches": true
|
||||
},
|
||||
"pytz": {
|
||||
"actual": "2023.3",
|
||||
"expected": "2023.3",
|
||||
"matches": true
|
||||
},
|
||||
"scipy": {
|
||||
"actual": "1.8.1",
|
||||
"expected": "1.8.1",
|
||||
"matches": true
|
||||
},
|
||||
"setuptools": {
|
||||
"actual": "67.8.0",
|
||||
"expected": "67.8.0",
|
||||
"matches": true
|
||||
},
|
||||
"six": {
|
||||
"actual": "1.16.0",
|
||||
"expected": "1.16.0",
|
||||
"matches": true
|
||||
},
|
||||
"sympy": {
|
||||
"actual": "1.12",
|
||||
"expected": "1.12",
|
||||
"matches": true
|
||||
},
|
||||
"wheel": {
|
||||
"actual": "0.38.4",
|
||||
"expected": "0.38.4",
|
||||
"matches": true
|
||||
}
|
||||
},
|
||||
"platform": "Linux-5.15.0-194-generic-x86_64-with-glibc2.35",
|
||||
"python": "3.10.12",
|
||||
"python_executable": "/data/digital-drosophila/experiments/phase4ad-author-semantics/environment/.venv/bin/python",
|
||||
"python_patch_matches": false
|
||||
}
|
||||
+127
@@ -0,0 +1,127 @@
|
||||
[
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_neurons",
|
||||
"order": 0,
|
||||
"type": "NeuronGroup",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "default_synapses",
|
||||
"order": 0,
|
||||
"type": "Synapses",
|
||||
"when": "start"
|
||||
},
|
||||
{
|
||||
"abstract_code": "not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n_g = g*exp(-dt*int(not_refractory)/tau)\n_v = g*tau*(-exp(dt*int(not_refractory)/t_mbr) + exp(dt*int(not_refractory)/tau))*exp(-dt*int(not_refractory)/t_mbr)*exp(-dt*int(not_refractory)/tau)/(t_mbr - tau) + v*exp(-dt*int(not_refractory)/t_mbr) + v_0 - v_0*exp(-dt*int(not_refractory)/t_mbr)\ng = _g\nv = _v",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n dt = _array_defaultclock_dt[0]\n _lio_1 = (- dt) / tau\n _lio_2 = exp(_lio_1)\n _lio_3 = tau / (t_mbr - tau)\n _lio_4 = dt / t_mbr\n _lio_5 = dt / tau\n _lio_6 = (- dt) / t_mbr\n _lio_7 = v_0 - v_0\n _lio_8 = v_0 - (v_0 * exp(_lio_6))\n _lio_9 = ((_lio_3 * ((- exp(_lio_4)) + exp(_lio_5))) * exp(_lio_6)) * exp(_lio_1)\n _lio_10 = exp(_lio_6)\n # vector code\n _vectorisation_idx = LazyArange(N)\n g = _array_default_neurons_g.copy()\n t = _array_defaultclock_t[0]\n dt = _array_defaultclock_dt[0]\n rfc = _array_default_neurons_rfc\n v = _array_default_neurons_v.copy()\n lastspike = _array_default_neurons_lastspike\n not_refractory = _array_default_neurons_not_refractory.copy()\n not_refractory = timestep(t - lastspike, dt) >= timestep(rfc, dt)\n _g = _numpy.where(not_refractory, _lio_2 * g, g)\n _v = _numpy.where(not_refractory, _lio_8 + ((_lio_9 * g) + (_lio_10 * v)), _lio_7 + v)\n g[not_refractory] = _g[not_refractory]\n v[not_refractory] = _v[not_refractory]\n _array_default_neurons_g[:] = g\n _array_default_neurons_v[:] = v\n _array_default_neurons_not_refractory[:] = not_refractory\n",
|
||||
"name": "default_neurons_stateupdater_codeobject_3",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_stateupdater",
|
||||
"order": 0,
|
||||
"type": "StateUpdater",
|
||||
"user_code": "g = -g*int(not_refractory)/tau\nv = (g - v + v_0)*int(not_refractory)/t_mbr",
|
||||
"when": "groups"
|
||||
},
|
||||
{
|
||||
"active": true,
|
||||
"code_objects": [],
|
||||
"name": "oracle_seed_injection",
|
||||
"order": 0,
|
||||
"type": "NetworkOperation",
|
||||
"when": "before_thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_cond = (v > v_th) and not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _array_default_neurons__spikespace[-1] = 0\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n numpy_True = _numpy.bool_(True)\n numpy_False = _numpy.bool_(False)\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = LazyArange(N)\n v = _array_default_neurons_v\n not_refractory = _array_default_neurons_not_refractory\n _cond = (v > v_th) & not_refractory\n if _cond is True or _cond is numpy_True:\n _events = _numpy.arange(N)\n elif _cond is False or _cond is numpy_False:\n _events = _numpy.array([])\n else:\n _events, = _cond.nonzero()\n _array_default_neurons__spikespace[-1] = len(_events)\n _array_default_neurons__spikespace[:len(_events)] = _events\n # Set the neuron to refractory\n _array_default_neurons_not_refractory[_events] = False\n _array_default_neurons_lastspike[_events] = _array_defaultclock_t[0]\n",
|
||||
"name": "default_neurons_spike_thresholder_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_thresholder",
|
||||
"order": 0,
|
||||
"type": "Thresholder",
|
||||
"user_code": "_cond = v > v_th",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_i = _source_i\n_to_record_t = _source_t",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _n_events = _array_default_neurons__spikespace[-1]\n if _n_events > 0:\n _events = _array_default_neurons__spikespace[:_n_events]\n # Take subgroups into account\n if _source_start != 0 or _source_stop != _source_N:\n _events = _events[(_events >= _source_start) & (_events < _source_stop)]\n _n_events = len(_events)\n if _n_events > 0:\n _vectorisation_idx = 1\n _curlen = _array_spikemonitor_N[0]\n _newlen = _curlen + _n_events\n _owner.resize(_newlen)\n _array_spikemonitor_N[0] = _newlen\n _vectorisation_idx = _events\n _idx = _events\n _source_i = _array_default_neurons_i[_idx]\n _source_t = _array_defaultclock_t[0]\n _to_record_i = _source_i\n _to_record_t = _source_t\n _dynamic_array_spikemonitor_t[_curlen:_newlen] = _to_record_t\n _dynamic_array_spikemonitor_i[_curlen:_newlen] = _to_record_i\n _array_spikemonitor_count[_events - _source_start] += 1\n",
|
||||
"name": "spikemonitor_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "spikemonitor",
|
||||
"order": 1,
|
||||
"type": "SpikeMonitor",
|
||||
"user_code": "None",
|
||||
"when": "thresholds"
|
||||
},
|
||||
{
|
||||
"abstract_code": "g += w\n",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.initialise_queue()\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _owner.push_spikes()\n",
|
||||
"name": "default_synapses_pre_push_spikes",
|
||||
"type": "CallableProxyType"
|
||||
},
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n _spiking_synapses = _queue.peek()\n if len(_spiking_synapses):\n # scalar code\n # vector code\n _idx = _spiking_synapses\n _vectorisation_idx = _idx\n # Abstract code: g += w\n _postsynaptic_idx = _array_default_synapses__synaptic_post[_idx]\n w = _array_default_synapses_w[_idx]\n not_refractory = _array_default_neurons_not_refractory[_postsynaptic_idx]\n _numpy.add.at(_array_default_neurons_g, _postsynaptic_idx[not_refractory], w[not_refractory])\n # Advance the spike queue\n _queue.advance()\n",
|
||||
"name": "default_synapses_pre_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_synapses_pre",
|
||||
"order": -1,
|
||||
"type": "SynapticPathway",
|
||||
"user_code": "None",
|
||||
"when": "synapses"
|
||||
},
|
||||
{
|
||||
"abstract_code": "v = v_rst; w = 0; g = 0 * mV",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # scalar code\n _vectorisation_idx = 1\n w = 0\n # vector code\n _idx = _array_default_neurons__spikespace[:_array_default_neurons__spikespace[-1]]\n _vectorisation_idx = _idx\n v = v_rst\n g = 0.0\n _array_default_neurons_g[_idx] = g\n _array_default_neurons_v[_idx] = v\n",
|
||||
"name": "default_neurons_spike_resetter_codeobject_4",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "default_neurons_spike_resetter",
|
||||
"order": 0,
|
||||
"type": "Resetter",
|
||||
"user_code": "None",
|
||||
"when": "resets"
|
||||
},
|
||||
{
|
||||
"abstract_code": "_to_record_v = _source_v\n_to_record_g = _source_g\n_to_record_lastspike = _source_lastspike\n_to_record_not_refractory = _source_not_refractory",
|
||||
"active": true,
|
||||
"code_objects": [
|
||||
{
|
||||
"generated_code": "after_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nbefore_run:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # EMPTY_CODE_BLOCK -- overwrite in child template\nrun:\n import numpy as _numpy\n from brian2.codegen.runtime.numpy_rt.numpy_rt import LazyArange\n # Resize dynamic arrays\n _new_len = len(_dynamic_array_oracle_end_state_t) + 1\n _owner.resize(_new_len)\n # Store values\n _dynamic_array_oracle_end_state_t[-1] = _array_defaultclock_t[0]\n # scalar code\n _vectorisation_idx = 1\n # vector code\n _vectorisation_idx = _array_oracle_end_state__indices\n _idx = _array_oracle_end_state__indices\n _source_not_refractory = _array_default_neurons_not_refractory[_idx]\n _source_g = _array_default_neurons_g[_idx]\n _source_v = _array_default_neurons_v[_idx]\n _source_lastspike = _array_default_neurons_lastspike[_idx]\n _to_record_v = _source_v\n _to_record_g = _source_g\n _to_record_lastspike = _source_lastspike\n _to_record_not_refractory = _source_not_refractory\n _dynamic_array_oracle_end_state_v[-1, :] = _to_record_v\n _dynamic_array_oracle_end_state_g[-1, :] = _to_record_g\n _dynamic_array_oracle_end_state_lastspike[-1, :] = _to_record_lastspike\n _dynamic_array_oracle_end_state_not_refractory[-1, :] = _to_record_not_refractory\n",
|
||||
"name": "oracle_end_state_codeobject",
|
||||
"type": "CallableProxyType"
|
||||
}
|
||||
],
|
||||
"name": "oracle_end_state",
|
||||
"order": 0,
|
||||
"type": "StateMonitor",
|
||||
"user_code": "None",
|
||||
"when": "end"
|
||||
}
|
||||
]
|
||||
@@ -0,0 +1,132 @@
|
||||
{
|
||||
"author_commit": "91bdd1e7dcf193f3e7ca5a8933497fcef63b7960",
|
||||
"author_sha256": "fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e",
|
||||
"config": {
|
||||
"author_precisions": [
|
||||
"float64",
|
||||
"float32"
|
||||
],
|
||||
"comparison": {
|
||||
"exact_spike_events_required_for_oracle_validation": true,
|
||||
"float32_state_max_abs_tolerance_mv": 0.001,
|
||||
"float64_state_max_abs_tolerance_mv": 1e-08,
|
||||
"precision_cross_comparison_may_diverge_but_must_be_reported": true
|
||||
},
|
||||
"component_policies": [
|
||||
"frozen",
|
||||
"reset_g_only",
|
||||
"refractory_gate_only",
|
||||
"reset_and_refractory"
|
||||
],
|
||||
"dt_ms": 0.1,
|
||||
"duration_ms": 1000.0,
|
||||
"experiment": "experiments/phase4ad-author-semantics",
|
||||
"human_authorization": "\u5141\u8bb8\u72ec\u7acb\u65b0\u6a21\u578b\u5206\u652f\u6d4b\u8bd5\u52a8\u529b\u5b66\u5047\u8bbe",
|
||||
"late_start_ms": 500.0,
|
||||
"model": {
|
||||
"dt_ms": 0.1,
|
||||
"membrane_tau_ms": 20.0,
|
||||
"refractory_ms": 2.2,
|
||||
"reset_potential_mv": -52.0,
|
||||
"resting_potential_mv": -52.0,
|
||||
"synaptic_delay_ms": 1.8,
|
||||
"synaptic_tau_ms": 5.0,
|
||||
"threshold_mv": -45.0,
|
||||
"w_syn_mv_per_anatomical_synapse": 0.275
|
||||
},
|
||||
"model_directory": "models/male-cns-lif/author-semantics-lab-v1",
|
||||
"motifs": [
|
||||
"pair",
|
||||
"trio",
|
||||
"five",
|
||||
"feedforward",
|
||||
"no_input"
|
||||
],
|
||||
"name": "author-semantics-lab-v1",
|
||||
"no_training_or_candidate": true,
|
||||
"phase": "4AD",
|
||||
"prior_experiment": "experiments/phase4ac-lif-semantics",
|
||||
"prior_model": "models/male-cns-lif/lif-semantics-audit-v1",
|
||||
"root": "/data/digital-drosophila",
|
||||
"seed_margin_mv": 1.0,
|
||||
"seed_node": 1381
|
||||
},
|
||||
"delay_steps": 18,
|
||||
"dt_ms": 0.1,
|
||||
"edge_ids": [
|
||||
1336648,
|
||||
1336655,
|
||||
1337065,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
20992294,
|
||||
20992891
|
||||
],
|
||||
"external_arrivals": {
|
||||
"events": 0,
|
||||
"ordering": "after default_synapses.pre"
|
||||
},
|
||||
"internal_voltage_unit": "volt",
|
||||
"nodes": [
|
||||
1197,
|
||||
1381,
|
||||
1511,
|
||||
82312,
|
||||
133666
|
||||
],
|
||||
"parameter_checks": {
|
||||
"membrane_tau_ms": {
|
||||
"author": 20.0,
|
||||
"config": 20.0
|
||||
},
|
||||
"refractory_ms": {
|
||||
"author": 2.2,
|
||||
"config": 2.2
|
||||
},
|
||||
"reset_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"resting_potential_mv": {
|
||||
"author": -52.0,
|
||||
"config": -52.0
|
||||
},
|
||||
"synaptic_delay_ms": {
|
||||
"author": 1.8,
|
||||
"config": 1.8
|
||||
},
|
||||
"synaptic_tau_ms": {
|
||||
"author": 5.0,
|
||||
"config": 5.0
|
||||
},
|
||||
"threshold_mv": {
|
||||
"author": -45.0,
|
||||
"config": -45.0
|
||||
},
|
||||
"w_syn_mv_per_anatomical_synapse": {
|
||||
"author": 0.275,
|
||||
"config": 0.275
|
||||
}
|
||||
},
|
||||
"precision": "float32",
|
||||
"preferences": {
|
||||
"codegen.target": "numpy",
|
||||
"core.default_float_dtype": "float32",
|
||||
"legacy.refractory_timing": false
|
||||
},
|
||||
"refractory_steps": 22,
|
||||
"returned_voltage_unit": "mV",
|
||||
"seed": {
|
||||
"injections": 0,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381
|
||||
},
|
||||
"steps": 10000,
|
||||
"weight_rule": "raw_count multiplied by unchanged author w_syn; no sign inference"
|
||||
}
|
||||
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"schedule": [
|
||||
"start",
|
||||
"groups",
|
||||
"thresholds",
|
||||
"synapses",
|
||||
"resets",
|
||||
"end"
|
||||
],
|
||||
"summary": " object | part of | Clock dt | when | order | active\n------------------------------------------------+---------------------------------+----------------------+-------------------+-------+-------\ndefault_neurons_stateupdater (StateUpdater) | default_neurons (NeuronGroup) | 100. us (every step) | groups | 0 | yes \noracle_seed_injection (NetworkOperation) | -- | 100. us (every step) | before_thresholds | 0 | yes \ndefault_neurons_spike_thresholder (Thresholder) | default_neurons (NeuronGroup) | 100. us (every step) | thresholds | 0 | yes \nspikemonitor (SpikeMonitor) | spikemonitor (SpikeMonitor) | 100. us (every step) | thresholds | 1 | yes \ndefault_synapses_pre (SynapticPathway) | default_synapses (Synapses) | 100. us (every step) | synapses | -1 | yes \ndefault_neurons_spike_resetter (Resetter) | default_neurons (NeuronGroup) | 100. us (every step) | resets | 0 | yes \noracle_end_state (StateMonitor) | oracle_end_state (StateMonitor) | 100. us (every step) | end | 0 | yes "
|
||||
}
|
||||
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"applied_mv": -43.99999976158142,
|
||||
"injections": 1,
|
||||
"margin_mv": 1.0,
|
||||
"requested_global_node": 1381,
|
||||
"spiked_at_zero": true
|
||||
}
|
||||
@@ -0,0 +1,47 @@
|
||||
{
|
||||
"edge_id": [
|
||||
1336648,
|
||||
1336655,
|
||||
1337065,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
20992285,
|
||||
20992291,
|
||||
20992294,
|
||||
20992891
|
||||
],
|
||||
"weights_mv": [
|
||||
57.75,
|
||||
47.29999923706055,
|
||||
28.599998474121094,
|
||||
71.2249984741211,
|
||||
64.625,
|
||||
17.599998474121094,
|
||||
62.14999771118164,
|
||||
77.82499694824219,
|
||||
19.249998092651367,
|
||||
26.125,
|
||||
15.675000190734863,
|
||||
20.899999618530273,
|
||||
11.824999809265137
|
||||
],
|
||||
"weights_volt": [
|
||||
0.057750001549720764,
|
||||
0.04729999974370003,
|
||||
0.028599999845027924,
|
||||
0.07122500240802765,
|
||||
0.06462500244379044,
|
||||
0.01759999990463257,
|
||||
0.062150001525878906,
|
||||
0.07782500237226486,
|
||||
0.01924999989569187,
|
||||
0.026125000789761543,
|
||||
0.015675000846385956,
|
||||
0.020899999886751175,
|
||||
0.011824999935925007
|
||||
]
|
||||
}
|
||||
+611
@@ -0,0 +1,611 @@
|
||||
step,neuron_idx
|
||||
0,1381
|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
1272,1381
|
||||
1292,1197
|
||||
1310,1511
|
||||
1334,1381
|
||||
1345,133666
|
||||
1355,1197
|
||||
1375,1511
|
||||
1395,1381
|
||||
1418,1197
|
||||
1426,133666
|
||||
1440,1511
|
||||
1461,1381
|
||||
1475,1197
|
||||
1501,1511
|
||||
1514,133666
|
||||
1523,1381
|
||||
1543,1197
|
||||
1561,1511
|
||||
1585,1381
|
||||
1596,133666
|
||||
1606,1197
|
||||
1626,1511
|
||||
1641,82312
|
||||
1646,1381
|
||||
1669,1197
|
||||
1677,133666
|
||||
1691,1511
|
||||
1712,1381
|
||||
1726,1197
|
||||
1752,1511
|
||||
1765,133666
|
||||
1774,1381
|
||||
1794,1197
|
||||
1812,1511
|
||||
1836,1381
|
||||
1847,133666
|
||||
1857,1197
|
||||
1877,1511
|
||||
1897,1381
|
||||
1920,1197
|
||||
1928,133666
|
||||
1942,1511
|
||||
1963,1381
|
||||
1977,1197
|
||||
2003,1511
|
||||
2016,133666
|
||||
2025,1381
|
||||
2045,1197
|
||||
2063,1511
|
||||
2087,1381
|
||||
2098,133666
|
||||
2108,1197
|
||||
2128,1511
|
||||
2148,1381
|
||||
2171,1197
|
||||
2179,133666
|
||||
2193,1511
|
||||
2214,1381
|
||||
2228,1197
|
||||
2254,1511
|
||||
2267,133666
|
||||
2276,1381
|
||||
2296,1197
|
||||
2314,1511
|
||||
2338,1381
|
||||
2349,133666
|
||||
2359,1197
|
||||
2379,1511
|
||||
2394,82312
|
||||
2399,1381
|
||||
2422,1197
|
||||
2430,133666
|
||||
2444,1511
|
||||
2465,1381
|
||||
2479,1197
|
||||
2505,1511
|
||||
2518,133666
|
||||
2527,1381
|
||||
2547,1197
|
||||
2565,1511
|
||||
2589,1381
|
||||
2600,133666
|
||||
2610,1197
|
||||
2630,1511
|
||||
2650,1381
|
||||
2673,1197
|
||||
2681,133666
|
||||
2695,1511
|
||||
2716,1381
|
||||
2730,1197
|
||||
2756,1511
|
||||
2769,133666
|
||||
2778,1381
|
||||
2798,1197
|
||||
2816,1511
|
||||
2840,1381
|
||||
2851,133666
|
||||
2861,1197
|
||||
2881,1511
|
||||
2901,1381
|
||||
2924,1197
|
||||
2932,133666
|
||||
2946,1511
|
||||
2967,1381
|
||||
2981,1197
|
||||
3007,1511
|
||||
3020,133666
|
||||
3029,1381
|
||||
3049,1197
|
||||
3067,1511
|
||||
3091,1381
|
||||
3102,133666
|
||||
3112,1197
|
||||
3132,1511
|
||||
3147,82312
|
||||
3152,1381
|
||||
3175,1197
|
||||
3183,133666
|
||||
3197,1511
|
||||
3218,1381
|
||||
3232,1197
|
||||
3258,1511
|
||||
3271,133666
|
||||
3280,1381
|
||||
3300,1197
|
||||
3318,1511
|
||||
3342,1381
|
||||
3353,133666
|
||||
3363,1197
|
||||
3383,1511
|
||||
3403,1381
|
||||
3426,1197
|
||||
3434,133666
|
||||
3448,1511
|
||||
3469,1381
|
||||
3483,1197
|
||||
3509,1511
|
||||
3522,133666
|
||||
3531,1381
|
||||
3551,1197
|
||||
3569,1511
|
||||
3593,1381
|
||||
3604,133666
|
||||
3614,1197
|
||||
3634,1511
|
||||
3654,1381
|
||||
3677,1197
|
||||
3685,133666
|
||||
3699,1511
|
||||
3720,1381
|
||||
3734,1197
|
||||
3760,1511
|
||||
3773,133666
|
||||
3782,1381
|
||||
3802,1197
|
||||
3820,1511
|
||||
3844,1381
|
||||
3855,133666
|
||||
3865,1197
|
||||
3885,1511
|
||||
3900,82312
|
||||
3905,1381
|
||||
3928,1197
|
||||
3936,133666
|
||||
3950,1511
|
||||
3971,1381
|
||||
3985,1197
|
||||
4011,1511
|
||||
4024,133666
|
||||
4033,1381
|
||||
4053,1197
|
||||
4071,1511
|
||||
4095,1381
|
||||
4106,133666
|
||||
4116,1197
|
||||
4136,1511
|
||||
4156,1381
|
||||
4179,1197
|
||||
4187,133666
|
||||
4201,1511
|
||||
4222,1381
|
||||
4236,1197
|
||||
4262,1511
|
||||
4275,133666
|
||||
4284,1381
|
||||
4304,1197
|
||||
4322,1511
|
||||
4346,1381
|
||||
4357,133666
|
||||
4367,1197
|
||||
4387,1511
|
||||
4407,1381
|
||||
4430,1197
|
||||
4438,133666
|
||||
4452,1511
|
||||
4473,1381
|
||||
4487,1197
|
||||
4513,1511
|
||||
4526,133666
|
||||
4535,1381
|
||||
4555,1197
|
||||
4573,1511
|
||||
4597,1381
|
||||
4608,133666
|
||||
4618,1197
|
||||
4638,1511
|
||||
4653,82312
|
||||
4658,1381
|
||||
4681,1197
|
||||
4689,133666
|
||||
4703,1511
|
||||
4724,1381
|
||||
4738,1197
|
||||
4764,1511
|
||||
4777,133666
|
||||
4786,1381
|
||||
4806,1197
|
||||
4824,1511
|
||||
4848,1381
|
||||
4859,133666
|
||||
4869,1197
|
||||
4889,1511
|
||||
4909,1381
|
||||
4932,1197
|
||||
4940,133666
|
||||
4954,1511
|
||||
4975,1381
|
||||
4989,1197
|
||||
5015,1511
|
||||
5028,133666
|
||||
5037,1381
|
||||
5057,1197
|
||||
5075,1511
|
||||
5099,1381
|
||||
5110,133666
|
||||
5120,1197
|
||||
5140,1511
|
||||
5160,1381
|
||||
5183,1197
|
||||
5191,133666
|
||||
5205,1511
|
||||
5226,1381
|
||||
5240,1197
|
||||
5266,1511
|
||||
5279,133666
|
||||
5288,1381
|
||||
5308,1197
|
||||
5326,1511
|
||||
5350,1381
|
||||
5361,133666
|
||||
5371,1197
|
||||
5391,1511
|
||||
5406,82312
|
||||
5411,1381
|
||||
5434,1197
|
||||
5442,133666
|
||||
5456,1511
|
||||
5477,1381
|
||||
5491,1197
|
||||
5517,1511
|
||||
5530,133666
|
||||
5539,1381
|
||||
5559,1197
|
||||
5577,1511
|
||||
5601,1381
|
||||
5612,133666
|
||||
5622,1197
|
||||
5642,1511
|
||||
5662,1381
|
||||
5685,1197
|
||||
5693,133666
|
||||
5707,1511
|
||||
5728,1381
|
||||
5742,1197
|
||||
5768,1511
|
||||
5781,133666
|
||||
5790,1381
|
||||
5810,1197
|
||||
5828,1511
|
||||
5852,1381
|
||||
5863,133666
|
||||
5873,1197
|
||||
5893,1511
|
||||
5913,1381
|
||||
5936,1197
|
||||
5944,133666
|
||||
5958,1511
|
||||
5979,1381
|
||||
5993,1197
|
||||
6019,1511
|
||||
6032,133666
|
||||
6041,1381
|
||||
6061,1197
|
||||
6079,1511
|
||||
6103,1381
|
||||
6114,133666
|
||||
6124,1197
|
||||
6144,1511
|
||||
6159,82312
|
||||
6164,1381
|
||||
6187,1197
|
||||
6195,133666
|
||||
6209,1511
|
||||
6230,1381
|
||||
6244,1197
|
||||
6270,1511
|
||||
6283,133666
|
||||
6292,1381
|
||||
6312,1197
|
||||
6330,1511
|
||||
6354,1381
|
||||
6365,133666
|
||||
6375,1197
|
||||
6395,1511
|
||||
6415,1381
|
||||
6438,1197
|
||||
6446,133666
|
||||
6460,1511
|
||||
6481,1381
|
||||
6495,1197
|
||||
6521,1511
|
||||
6534,133666
|
||||
6543,1381
|
||||
6563,1197
|
||||
6581,1511
|
||||
6605,1381
|
||||
6616,133666
|
||||
6626,1197
|
||||
6646,1511
|
||||
6666,1381
|
||||
6689,1197
|
||||
6697,133666
|
||||
6711,1511
|
||||
6732,1381
|
||||
6746,1197
|
||||
6772,1511
|
||||
6785,133666
|
||||
6794,1381
|
||||
6814,1197
|
||||
6832,1511
|
||||
6856,1381
|
||||
6867,133666
|
||||
6877,1197
|
||||
6897,1511
|
||||
6912,82312
|
||||
6917,1381
|
||||
6940,1197
|
||||
6948,133666
|
||||
6962,1511
|
||||
6983,1381
|
||||
6997,1197
|
||||
7023,1511
|
||||
7036,133666
|
||||
7045,1381
|
||||
7065,1197
|
||||
7083,1511
|
||||
7107,1381
|
||||
7118,133666
|
||||
7128,1197
|
||||
7148,1511
|
||||
7168,1381
|
||||
7191,1197
|
||||
7199,133666
|
||||
7213,1511
|
||||
7234,1381
|
||||
7248,1197
|
||||
7274,1511
|
||||
7287,133666
|
||||
7296,1381
|
||||
7316,1197
|
||||
7334,1511
|
||||
7358,1381
|
||||
7369,133666
|
||||
7379,1197
|
||||
7399,1511
|
||||
7419,1381
|
||||
7442,1197
|
||||
7450,133666
|
||||
7464,1511
|
||||
7485,1381
|
||||
7499,1197
|
||||
7525,1511
|
||||
7538,133666
|
||||
7547,1381
|
||||
7567,1197
|
||||
7585,1511
|
||||
7609,1381
|
||||
7620,133666
|
||||
7630,1197
|
||||
7650,1511
|
||||
7665,82312
|
||||
7670,1381
|
||||
7693,1197
|
||||
7701,133666
|
||||
7715,1511
|
||||
7736,1381
|
||||
7750,1197
|
||||
7776,1511
|
||||
7789,133666
|
||||
7798,1381
|
||||
7818,1197
|
||||
7836,1511
|
||||
7860,1381
|
||||
7871,133666
|
||||
7881,1197
|
||||
7901,1511
|
||||
7921,1381
|
||||
7944,1197
|
||||
7952,133666
|
||||
7966,1511
|
||||
7987,1381
|
||||
8001,1197
|
||||
8027,1511
|
||||
8040,133666
|
||||
8049,1381
|
||||
8069,1197
|
||||
8087,1511
|
||||
8111,1381
|
||||
8122,133666
|
||||
8132,1197
|
||||
8152,1511
|
||||
8172,1381
|
||||
8195,1197
|
||||
8203,133666
|
||||
8217,1511
|
||||
8238,1381
|
||||
8252,1197
|
||||
8278,1511
|
||||
8291,133666
|
||||
8300,1381
|
||||
8320,1197
|
||||
8338,1511
|
||||
8362,1381
|
||||
8373,133666
|
||||
8383,1197
|
||||
8403,1511
|
||||
8418,82312
|
||||
8423,1381
|
||||
8446,1197
|
||||
8454,133666
|
||||
8468,1511
|
||||
8489,1381
|
||||
8503,1197
|
||||
8529,1511
|
||||
8542,133666
|
||||
8551,1381
|
||||
8571,1197
|
||||
8589,1511
|
||||
8613,1381
|
||||
8624,133666
|
||||
8634,1197
|
||||
8654,1511
|
||||
8674,1381
|
||||
8697,1197
|
||||
8705,133666
|
||||
8719,1511
|
||||
8740,1381
|
||||
8754,1197
|
||||
8780,1511
|
||||
8793,133666
|
||||
8802,1381
|
||||
8822,1197
|
||||
8840,1511
|
||||
8864,1381
|
||||
8875,133666
|
||||
8885,1197
|
||||
8905,1511
|
||||
8925,1381
|
||||
8948,1197
|
||||
8956,133666
|
||||
8970,1511
|
||||
8991,1381
|
||||
9005,1197
|
||||
9031,1511
|
||||
9044,133666
|
||||
9053,1381
|
||||
9073,1197
|
||||
9091,1511
|
||||
9115,1381
|
||||
9126,133666
|
||||
9136,1197
|
||||
9156,1511
|
||||
9171,82312
|
||||
9176,1381
|
||||
9199,1197
|
||||
9207,133666
|
||||
9221,1511
|
||||
9242,1381
|
||||
9256,1197
|
||||
9282,1511
|
||||
9295,133666
|
||||
9304,1381
|
||||
9324,1197
|
||||
9342,1511
|
||||
9366,1381
|
||||
9377,133666
|
||||
9387,1197
|
||||
9407,1511
|
||||
9427,1381
|
||||
9450,1197
|
||||
9458,133666
|
||||
9472,1511
|
||||
9493,1381
|
||||
9507,1197
|
||||
9533,1511
|
||||
9546,133666
|
||||
9555,1381
|
||||
9575,1197
|
||||
9593,1511
|
||||
9617,1381
|
||||
9628,133666
|
||||
9638,1197
|
||||
9658,1511
|
||||
9678,1381
|
||||
9701,1197
|
||||
9709,133666
|
||||
9723,1511
|
||||
9744,1381
|
||||
9758,1197
|
||||
9784,1511
|
||||
9797,133666
|
||||
9806,1381
|
||||
9826,1197
|
||||
9844,1511
|
||||
9868,1381
|
||||
9879,133666
|
||||
9889,1197
|
||||
9909,1511
|
||||
9924,82312
|
||||
9929,1381
|
||||
9952,1197
|
||||
9960,133666
|
||||
9974,1511
|
||||
9995,1381
|
||||
|
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user