Digital Drosophila Archive
85 lines
5.5 KiB
Markdown
85 lines
5.5 KiB
Markdown
# Phase4AG: author LIF + frozen legacy graded APL
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Execution revision r2: the first registered v1 attempt stopped before the first
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Brian tick because resolving the science-venv Python symlink launched system
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Python without NumPy. All v1 files and partial outputs are preserved separately.
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This revision retains the declared venv invocation path. It does not change any
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equation, arithmetic, mask, stimulus, gate, parameter or choice of trial.
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The complete original protocol is rerun from fresh state; no failed trial is
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overwritten or omitted, and the first version cannot be claimed numerical PASS.
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Authorized independent hypothesis branch. All old source, raw connectome, signs,
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W_syn, APL calibration, eligibility-v2 and 14,232-edge mask remain read-only.
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No learning, gain/threshold changes, new edges, top-K, heldout activity, game or RL.
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Author code provenance is the already archived MIT-licensed
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https://github.com/philshiu/Drosophila_brain_model at
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91bdd1e7dcf193f3e7ca5a8933497fcef63b7960; original model.py SHA-256
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fc45837d7122c6ce2a7f3f2f23c515992e4b232aadb919efabb72337fac88e4e.
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Associated paper: https://www.nature.com/articles/s41586-024-07763-9.
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This new MaleCNS/APL combination is not a strict reproduction of that model.
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The declared combination is author float64 LIF plus the exact old float32 graded
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APL operator after synapses/resets at end. All 5,845 original APL fast out-edges
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are zeroed only in the effective in-memory model, as in the legacy overlay.
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Only KC receives graded replacement; known 1,212 non-KC edges remain unreplaced.
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This existing scope limitation is not silently repaired in a compatibility test.
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Use the original full normalized I[2,166700]/O[2,4064] float32 bytes and original
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normalizers, even for induced real subgraphs. APL reads actual binary spikes,
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never requests, and keeps independent float32 state. Preserve unfused operations:
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state.mul_(float(f32(exp(-.1/20)))).add_(torch.mv(I, spikes_f32));
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h32=torch.matmul(state,O)*f32(.01). Promote h32 to float64, multiply by float64
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.001, then separately subtract from SI g. Do not alter TF32/default CuBLAS flags;
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record them. No added APL delay, drive dt/tau scaling, truncation or clipping.
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APL writes KC g unconditionally after author reset, including refractory KC.
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Author g integration is frozen during refractory; therefore negative overlay g
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may accumulate and differs from old dynamics. This is an explicit hypothesis,
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not an assertion of original author or measured physiology. Guarded writes or
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reset-before/after changes would be different hypotheses, not fixes to this one.
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Eight fixed metadata/connectivity-selected fixtures cover zero/reset, real PN/KC
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drive, current actual event feedback, duplicate natural/request event, 18-step
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arrival vs22-step refractory boundary, full fast APL mask, non-KC scope and mixed
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precision/high fan-in. No choice is made from runtime responses. Each GPU case
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has3 independent fresh runs. A Brian2 author oracle starts from its own state,
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computes its own actual spikes, and uses an isolated standard-I/O legacy-APL
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worker with independent state. No candidate inhibition trace is fed into Brian.
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All tick states, events, requests, queue order/guard, APL drive/state/h must agree
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exactly. The APL component is shared, so this is NOT a fully independent CPU
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oracle. Independently reconstruct old matrices using frozen initializer AST,
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replay actual events through original update AST, require exact component bytes;
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also quantify an independent float64 positive-sum arithmetic error bound. It is
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not a replacement for exact legacy equivalence. No arithmetic bound is adjusted.
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Only after all fixture gates pass, run two predeclared full-graph probes: first
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old calibration odor, first original seed, both encoder modes. Compare each with
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the independently running Brian + isolated APL worker. Save state hashes and sparse
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events, not a time-by-all-neurons dense spike matrix. If a numerical gate fails,
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stop CAL; keep diagnostic evidence and do not widen tolerance.
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Then run the complete original6 CAL odors ×5 original seeds ×2 original modes,
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100ms per run. Retain the ORIGINAL12-odor median for matched-energy encoding,
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original pn_drive target/RNG column order, original evaluation seeds51101..51105.
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Save Poisson requests before any trajectory. Only calibration dynamic cases are
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used; formal A/B and heldout remain unrun. Repeat first seed for every odor/mode.
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No per-odor pulse, amplitude, timing or threshold search. No learning.
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Recompute the full original4L representation criteria on CAL only: mean KC active
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fraction .03..09, min>0, max<.5, same-odor Jaccard>different, between Euclidean>
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within, >=3 pairs whose mean between exceeds THAT PAIR's pooled within distances,
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both modes. Add finiteness and exact repeat requirements. CAL PASS does not imply
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heldout generalization, timing/credit specificity, memory or learning usability.
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If CAL fails stop promotion and report it, not a least-bad candidate.
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Before/after verify SHA-256 of all original230239 files and AA--AF seals; rerun original five
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deterministic reference trials to new output. Archive code/env/source SHA,
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matrices/norms/masks/identities, requests and all outputs. No published candidate.
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Technical references (queried2026-09-30): Brian2 2.5.1 refractory documentation
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https://brian2.readthedocs.io/en/2.5.1/user/refractoriness.html (actual generated code
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remains authoritative); PyTorch numerical accuracy notes
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https://docs.pytorch.org/docs/2.14/notes/numerical_accuracy.html (cross-backend
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floating operations are not assumed bitwise equivalent).
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