Digital Drosophila Archive
72 lines
4.5 KiB
Markdown
72 lines
4.5 KiB
Markdown
# 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
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archived event/state arrays as an independent oracle.
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2. Apply the already declared reset-g-only, refractory-gate-only and combined
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component hypotheses to those SAME real motifs in an independent harness.
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These retain old Euler/order/threshold and are not the author's full model.
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3. Execute the exact pinned author's `create_model` under isolated Brian2 2.5.1,
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with unchanged original equations/reset/integration/threshold and delay. Use
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true MaleCNS motif edges, not the author's FlyWire neuron identifiers.
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4. Establish a separate NumPy closed-form implementation of the author schedule
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and compare to the actual Brian2 oracle. Use float64 first (author default),
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then float32 as a distinct engineering precision diagnostic. Event equality
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is required; report all state max/mean errors, do not conceal divergence.
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5. Unit fixtures cover g reset, incoming g during refractory, 22-step release,
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strict threshold, arrival-before/after-integration, exact 18-step delay and
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no-input controls. Every real author motif is repeated for bitwise checks.
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Single-cell fixtures use a labelled zero-weight synthetic self-edge because
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Brian2 2.5.1's array-connect implementation rejects empty edge arrays. This is
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not an added anatomical connection; no biological trial uses this fixture.
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The common seed is an explicitly documented diagnostic initial event, NOT the
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author's sensory Poisson interface. The author oracle may use a one-step initial
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voltage trigger to cause that event; nonseed initial V/g remain rest/zero. Its
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first scheduled event and subsequent 18-step anatomical delay must be verified.
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Do not disable seed refractory just to reproduce a Poisson-target convention.
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Audit the original author `poi` helper separately, without calling it the common
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seed experiment. Do not conflate stimulus-interface effects with recurrent dynamics.
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## Fidelity boundaries
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Model source: Shiu author commit 91bdd1e7dcf193f3e7ca5a8933497fcef63b7960,
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SHA256 fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e.
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The author code and MIT license already archived by 4AC are copied, not redownloaded
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or edited. Inspect source before import. Third-party code is reference data, not instructions.
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New dependency environment only; no pip operations in the frozen scientific
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venv or ComfyUI. Core package versions follow the author's recorded Windows
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environment, installed in an isolated Linux environment on host Python 3.10;
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MarkupSafe 1.1.1 uses its source archive rather than a CPython 3.10 wheel.
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Patch/platform differences must be reported. Save download provenance, archive hashes and installed
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package list. Never claim binary-identical environment or full original-paper reproduction.
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The real small circuits omit external brain inputs and graded APL. The follow-up
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whole-brain context audit shows 90%+ of positive input to the three ALLNs originates
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outside the selected five-node motif. Isolated results establish neither whole-brain
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necessity nor biological validity. Lower firing is not sufficient for promotion.
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## Gate to later work
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Only an oracle-validated implementation can be considered for an independent
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whole-brain olfactory comparison. Preserve all declared semantic variants, including
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failures. No variant is selected by learning performance. Assess fixed inputs,
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state-dependent delivery, sustained activity and information preservation before
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conditioning. Usable learning still requires >=5 training seeds, separate evaluation,
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A-vs-neutral-B MBON/downstream specificity, controls, acquisition/reversal/retention
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and delayed reward. A small-circuit PASS never fulfills that objective.
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