# Phase 4AD — independently authorized author-semantics laboratory

Human authorization: "允许独立新模型分支测试动力学假设".
Goal remains usable stimulus-specific learning, not reducing spike count or passing an isolated fixture.

All Phase 1–4AC scientific artifacts, canonical CSR/raw counts, transmitter signs,
W_syn=0.275, APL calibration, eligibility-v2 and the 14,232-edge plastic mask are
read-only. No training, threshold/gain/eta search, decoder, game or visual work in
this prerequisite laboratory. Existing candidate names are not replaced.

## Fixed comparison before results

Use the exact five preselected 4AC motifs and the same initial single seed event,
not newly selected cells. Record all five outcomes, including persistent firing.
Preserve true edge IDs, counts and original global neuron_idx/body_id.

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.