Digital Drosophila Archive
607 lines
45 KiB
Python
607 lines
45 KiB
Python
#!/usr/bin/env python3
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"""Phase 4I: mechanism-level validation of the frozen Phase 4H MB overlay."""
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from __future__ import annotations
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import argparse
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import gc
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import hashlib
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import json
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import math
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import shutil
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import sys
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import time
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from collections import Counter, defaultdict
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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import pyarrow.parquet as pq
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import torch
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import yaml
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from brain_runtime import BrainRuntime, InputBatch, InputEvent
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from phase4h_audit import sha256, tree_manifest, verify_checksum, write_json
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def safe_corr(a: np.ndarray, b: np.ndarray) -> float:
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x = a.astype(np.float64); y = b.astype(np.float64)
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if np.std(x) == 0 or np.std(y) == 0:
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return 1.0 if np.array_equal(x, y) else 0.0
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return float(np.corrcoef(x, y)[0, 1])
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def cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
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x = a.astype(np.float64); y = b.astype(np.float64)
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d = float(np.linalg.norm(x) * np.linalg.norm(y))
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return 1.0 if d == 0 and np.array_equal(x, y) else (0.0 if d == 0 else float(np.dot(x, y) / d))
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def jaccard(a: np.ndarray, b: np.ndarray) -> float:
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aa = np.asarray(a, bool); bb = np.asarray(b, bool)
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union = np.count_nonzero(aa | bb)
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return 1.0 if union == 0 else float(np.count_nonzero(aa & bb) / union)
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def pair_metrics(a: np.ndarray, b: np.ndarray) -> dict[str, float]:
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return {
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"active_intersection": int(np.count_nonzero((a > 0) & (b > 0))),
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"active_union": int(np.count_nonzero((a > 0) | (b > 0))),
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"active_jaccard": jaccard(a > 0, b > 0),
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"cosine_similarity": cosine_similarity(a, b),
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"correlation": safe_corr(a, b),
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"euclidean_distance": float(np.linalg.norm(a.astype(np.float64) - b.astype(np.float64))),
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}
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def fixed_pulse_batch(targets: np.ndarray, pulse_times_ms: list[float], name: str,
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dan_targets: np.ndarray | None = None, dan_times_ms: list[float] | None = None) -> InputBatch:
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events: list[InputEvent] = []
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for i, pulse in enumerate(pulse_times_ms):
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events.append(InputEvent(
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target_neuron_idx=targets.astype(np.int64), stimulation_mode="spike_injection",
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value_kind="binary", value=1.0, start_ms=float(pulse), duration_ms=0.1,
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source_id="phase4i-direct-KC-interface", channel_id=f"{name}-KC-{i}",
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metadata={"exact_requested_events": int(len(targets)), "engineering_interface": True},
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))
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if dan_targets is not None:
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for i, pulse in enumerate(dan_times_ms or []):
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events.append(InputEvent(
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target_neuron_idx=dan_targets.astype(np.int64), stimulation_mode="spike_injection",
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value_kind="binary", value=1.0, start_ms=float(pulse), duration_ms=0.1,
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source_id="phase4i-artificial-US-interface", channel_id=f"{name}-DAN-{i}",
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metadata={"exact_requested_events": int(len(dan_targets)),
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"not_equal_to": "measured dopamine concentration"},
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))
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return InputBatch(events=tuple(events), batch_id=name, metadata={"phase": "4I"})
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def poisson_batch(targets: np.ndarray, rate_hz: float, duration_ms: float, name: str) -> InputBatch:
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event = InputEvent(
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target_neuron_idx=targets.astype(np.int64), stimulation_mode="spike_injection",
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value_kind="poisson_rate_hz", value=float(rate_hz), start_ms=0.0, duration_ms=float(duration_ms),
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source_id="phase4h-replayed-olfactory-CS", channel_id=name,
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metadata={"dopamine": "OFF", "plasticity": "OFF", "delta_weight": 0},
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)
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return InputBatch(events=(event,), batch_id=name, metadata={"phase": "4I-stage1"})
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def frozen_controls(cfg: dict[str, Any]) -> list[tuple[Path, Path]]:
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p = cfg["paths"]
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return [
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(Path(p["reference_root"]), Path(p["reference_root"]) / "checksums.sha256"),
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(Path(p["runtime_root"]), Path(p["runtime_root"]) / "checksums.sha256"),
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(Path(p["io_root"]), Path(p["io_root"]) / "checksums.sha256"),
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(Path(p["phase4c_root"]), Path(p["phase4c_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4d_root"]), Path(p["phase4d_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4e_root"]), Path(p["phase4e_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4f_root"]), Path(p["phase4f_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4f_model_root"]), Path(p["phase4f_model_root"]) / "checksums.sha256"),
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(Path(p["phase4g_root"]), Path(p["phase4g_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4g_model_root"]), Path(p["phase4g_model_root"]) / "checksums.sha256"),
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(Path(p["phase4g_learned_root"]), Path(p["phase4g_learned_root"]) / "checksums.sha256"),
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(Path(p["phase4h_root"]), Path(p["phase4h_root"]) / "manifests/checksums.sha256"),
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(Path(p["phase4h_model_root"]), Path(p["phase4h_model_root"]) / "checksums.sha256"),
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(Path(p["phase4h_learned_root"]), Path(p["phase4h_learned_root"]) / "checksums.sha256"),
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]
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def verify_frozen(cfg: dict[str, Any]) -> dict[str, Any]:
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checks = []
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for root, control in frozen_controls(cfg):
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if not control.is_file():
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raise FileNotFoundError(control)
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checks.append(verify_checksum(root, control))
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result = {"all_passed": all(x["passed"] for x in checks), "controls": checks}
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if not result["all_passed"]:
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raise RuntimeError("frozen integrity failure")
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return result
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class Validator:
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def __init__(self, cfg: dict[str, Any], config_path: Path, exp: Path):
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self.cfg = cfg; self.exp = exp
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self.rt = BrainRuntime(config_path, exp / "compiler-reports/extension-build")
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self.base = self.rt.weights.detach().clone(); self.n = self.rt.n
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self.meta = self.rt.table.to_pandas().sort_values("neuron_idx").reset_index(drop=True)
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self.cls = self.meta["annotation_class"].fillna("").astype(str).to_numpy()
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self.types = self.meta["annotation_type"].fillna("").astype(str).to_numpy()
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self.instances = self.meta["annotation_instance"].fillna("").astype(str).to_numpy()
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self.superclass = self.meta["annotation_superclass"].fillna("").astype(str).to_numpy()
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self.kc = np.flatnonzero(self.cls == "Kenyon_Cell").astype(np.int32)
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self.mbon = np.flatnonzero(self.cls == "MBON").astype(np.int32)
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p4h = Path(cfg["paths"]["phase4h_root"])
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self.cand = pq.read_table(p4h / "plastic-mask/plastic-edge-candidates.parquet").to_pandas()
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if len(self.cand) != int(cfg["expected_graph"]["phase4h_plastic_candidate_edges"]):
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raise RuntimeError("Phase 4H plastic mask size changed")
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self.edge_ids = self.cand["edge_id"].to_numpy(dtype=np.int64)
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self.base_candidate = self.base[torch.as_tensor(self.edge_ids, device=self.rt.device)].cpu().numpy()
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self.confirmed = pq.read_table(p4h / "mappings/dan-to-mbon-confirmed.parquet").to_pandas()
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self.mbon_down = pq.read_table(p4h / "mappings/mbon-to-downstream-actual-edges.parquet").to_pandas()
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self.downstream = np.unique(self.mbon_down["target_neuron_idx"].to_numpy(dtype=np.int32))
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self.desc = self.rt.output_populations["descending"]
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self.motor = self.rt.output_populations["motor_related"]
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self.source_to_candidate = defaultdict(list)
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for row_idx, src in enumerate(self.cand["source_neuron_idx"].to_numpy(dtype=np.int32)):
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self.source_to_candidate[int(src)].append(row_idx)
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def dan_targets(self, dtype: str, compartment: str) -> np.ndarray:
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rows = self.confirmed[(self.confirmed["source_annotation_type"] == dtype)
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& (self.confirmed["source_compartment_verbatim"] == compartment)]
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values = np.sort(rows["source_neuron_idx"].unique().astype(np.int32))
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if not len(values):
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raise RuntimeError(f"no confirmed DAN mapping for {dtype}/{compartment}")
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return values
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def install(self, delta: np.ndarray) -> None:
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self.rt.weights.copy_(self.base)
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ids = torch.as_tensor(self.edge_ids, dtype=torch.int64, device=self.rt.device)
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self.rt.weights[ids] = torch.as_tensor(self.base_candidate + delta, dtype=torch.float32, device=self.rt.device)
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def run(self, batch: InputBatch, duration_ms: float, seed: int, delta: np.ndarray,
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tracked_mbon: np.ndarray | None = None) -> dict[str, Any]:
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self.install(delta); self.rt.reset(int(seed)); self.rt.step(batch, float(duration_ms))
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ev = self.rt.read_spikes(); nodes = ev["neuron_idx"].astype(np.int32); times = ev["timestamp_ms"].astype(np.float64)
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counts = np.bincount(nodes, minlength=self.n).astype(np.int32)
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tracked = self.mbon if tracked_mbon is None else np.asarray(tracked_mbon, dtype=np.int32)
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def latency(members: np.ndarray) -> float | None:
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mask = np.isin(nodes, members)
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return None if not np.any(mask) else float(times[mask].min())
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out = {
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"counts": counts,
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"times": times,
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"nodes": nodes,
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"kc_counts": counts[self.kc],
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"mbon_counts": counts[self.mbon],
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"tracked_mbon_counts": counts[tracked],
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"downstream_counts": counts[self.downstream],
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"descending_counts": counts[self.desc],
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"motor_counts": counts[self.motor],
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"tracked_mbon_v": self.rt.v[torch.as_tensor(tracked, device=self.rt.device)].cpu().numpy().copy(),
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"tracked_mbon_g": self.rt.g[torch.as_tensor(tracked, device=self.rt.device)].cpu().numpy().copy(),
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"tracked_mbon": tracked.copy(),
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"latency": {"KC": latency(self.kc), "MBON": latency(tracked),
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"downstream": latency(self.downstream), "descending": latency(self.desc), "motor": latency(self.motor)},
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"finite": bool(torch.isfinite(self.rt.v).all().item() and torch.isfinite(self.rt.g).all().item()),
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"active_neurons": int(np.count_nonzero(counts)), "total_spikes": int(counts.sum(dtype=np.int64)),
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}
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return out
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@staticmethod
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def public_response(x: dict[str, Any]) -> dict[str, Any]:
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return {
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"active_neurons": x["active_neurons"], "total_spikes": x["total_spikes"], "finite": x["finite"],
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"population_spikes": {"KC": int(x["kc_counts"].sum()), "MBON": int(x["mbon_counts"].sum()),
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"tracked_MBON": int(x["tracked_mbon_counts"].sum()),
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"downstream": int(x["downstream_counts"].sum()),
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"descending": int(x["descending_counts"].sum()), "motor": int(x["motor_counts"].sum())},
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"first_spike_latency_ms": x["latency"],
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"tracked_mbon_final_v_mean": float(x["tracked_mbon_v"].mean()) if len(x["tracked_mbon_v"]) else None,
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"tracked_mbon_final_g_mean": float(x["tracked_mbon_g"].mean()) if len(x["tracked_mbon_g"]) else None,
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}
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def eligibility_update(self, active_kc_global: np.ndarray, compartment: str | None, d: float,
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delay_ms: float, enabled: bool = True) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
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src = self.cand["source_neuron_idx"].to_numpy(dtype=np.int32)
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active = np.isin(src, np.asarray(active_kc_global, dtype=np.int32)).astype(np.float32)
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# This intentionally reproduces Phase 4H behavior. Negative/zero delays receive full eligibility.
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factor = math.exp(-delay_ms / float(self.cfg["plasticity"]["eligibility_tau_ms"])) if delay_ms > 0 else 1.0
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eligibility = active * factor
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comp = self.cand["target_compartment_verbatim"].astype(str).to_numpy() == str(compartment)
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update = np.zeros(len(self.cand), dtype=np.float32)
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if enabled and d != 0 and compartment is not None:
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update[comp] = (float(self.cfg["plasticity"]["polarity"]) *
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float(self.cfg["plasticity"]["eta_effective_mv"]) * float(d) * eligibility[comp])
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return update, eligibility, comp
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def olfactory_stage(v: Validator, cfg: dict[str, Any], exp: Path) -> tuple[dict[str, Any], dict[str, Any]]:
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spec = cfg["olfactory_audit"]; olf = np.flatnonzero(v.rt.population_masks["olfactory"]).astype(np.int32); olf.sort()
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take = int(spec["targets_per_identity"]); cs_a = olf[:take]; cs_b = olf[take:2 * take]
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if len(cs_a) != take or len(cs_b) != take or np.intersect1d(cs_a, cs_b).size:
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raise RuntimeError("Phase 4H CS definitions cannot be reconstructed")
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zero = np.zeros(len(v.edge_ids), np.float32); records = []; vectors = {"A": [], "B": []}; counts_global = {"A": [], "B": []}
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temporal = {"A": [], "B": []}; first = {"A": [], "B": []}
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for seed in spec["seeds"]:
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pair = {}
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for label, targets in (("A", cs_a), ("B", cs_b)):
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run = v.run(poisson_batch(targets, spec["rate_hz"], spec["duration_ms"], f"CS-{label}"),
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spec["duration_ms"], int(seed), zero)
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rate = run["kc_counts"].astype(np.float32) / (float(spec["duration_ms"]) / 1000.0)
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vectors[label].append(rate); counts_global[label].append(run["counts"])
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kcmask = np.isin(run["nodes"], v.kc); bins = np.arange(0, spec["duration_ms"] + spec["temporal_bin_ms"], spec["temporal_bin_ms"])
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temporal[label].append(np.histogram(run["times"][kcmask], bins=bins)[0].astype(np.int32))
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first[label].append(run["latency"]["KC"])
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pair[label] = run
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metrics = pair_metrics(vectors["A"][-1], vectors["B"][-1]); metrics["seed"] = int(seed)
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metrics["A_active_KC"] = int(np.count_nonzero(vectors["A"][-1])); metrics["B_active_KC"] = int(np.count_nonzero(vectors["B"][-1]))
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records.append(metrics)
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va = np.asarray(vectors["A"]); vb = np.asarray(vectors["B"])
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consensus_n = int(spec["consensus_seed_count"]); aa = np.count_nonzero(va > 0, axis=0) >= consensus_n; bb = np.count_nonzero(vb > 0, axis=0) >= consensus_n
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sets = {"KC_A_specific": v.kc[aa & ~bb], "KC_B_specific": v.kc[bb & ~aa],
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"KC_shared": v.kc[aa & bb], "KC_inactive": v.kc[~aa & ~bb]}
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pd.DataFrame([(name, int(idx), int(v.meta.loc[idx, "body_id"]), v.types[idx]) for name, arr in sets.items() for idx in arr],
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columns=["category", "neuron_idx", "body_id", "annotation_type"]).to_parquet(exp / "kc-representation/kc-specificity.parquet", index=False)
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within = []
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for matrix in (va, vb):
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for i in range(len(matrix)):
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for j in range(i + 1, len(matrix)): within.append(float(np.linalg.norm(matrix[i] - matrix[j])))
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between = [float(np.linalg.norm(va[i] - vb[j])) for i in range(len(va)) for j in range(len(vb))]
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mean_j = float(np.mean([x["active_jaccard"] for x in records])); pair_cos_dist = [1.0 - x["cosine_similarity"] for x in records]
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gate = spec["separability_gate"]
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sep_pass = bool(mean_j <= float(gate["maximum_mean_active_jaccard"])
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and sum(x >= float(gate["minimum_pair_cosine_distance"]) for x in pair_cos_dist) >= int(gate["majority_seeds"])
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and np.mean(between) > np.mean(within) + float(gate["between_exceeds_within_sigma"]) * np.std(within))
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subtype = {}
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for label, matrix in (("A", va), ("B", vb)):
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active_hits = np.sum(matrix > 0, axis=0)
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subtype[label] = dict(Counter(v.types[v.kc[i]] for i in np.flatnonzero(active_hits)))
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comp_coverage = {}
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for comp, group in v.cand.groupby("target_compartment_verbatim"):
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src = np.unique(group["source_neuron_idx"].to_numpy(dtype=np.int32)); loc = np.isin(v.kc, src)
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comp_coverage[str(comp)] = {"candidate_source_KCs": int(len(src)), "A_consensus_active": int(np.count_nonzero(aa & loc)),
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"B_consensus_active": int(np.count_nonzero(bb & loc)), "shared": int(np.count_nonzero(aa & bb & loc))}
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report = {
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"status": "PASS-OLFACTORY-TO-KC-SEPARABILITY" if sep_pass else "FAIL-OLFACTORY-TO-KC-SEPARABILITY",
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"CS_A_targets": cs_a.tolist(), "CS_B_targets": cs_b.tolist(), "seeds": spec["seeds"],
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"per_seed": records, "mean_active_jaccard": mean_j,
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"within_CS_euclidean_mean": float(np.mean(within)), "within_CS_euclidean_std": float(np.std(within)),
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"between_CS_euclidean_mean": float(np.mean(between)), "between_CS_euclidean_std": float(np.std(between)),
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"categories": {key: int(len(value)) for key, value in sets.items()},
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"first_spike_latency_ms": first, "temporal_bin_spikes": {k: np.asarray(x).tolist() for k, x in temporal.items()},
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"KC_subtype_active_seed_hits": subtype, "compartment_associated_KC_coverage": comp_coverage,
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"dopamine_off": True, "plasticity_off": True, "delta_weight_zero": True,
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}
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write_json(exp / "kc-representation/summary.json", report)
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np.savez_compressed(exp / "kc-representation/kc-rate-vectors.npz", CS_A=va, CS_B=vb, kc_neuron_idx=v.kc)
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return report, {"A": va, "B": vb, "sets": sets, "cs_a": cs_a, "cs_b": cs_b,
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"global_counts_A": counts_global["A"], "global_counts_B": counts_global["B"]}
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def edge_specificity(v: Validator, olf: dict[str, Any], cfg: dict[str, Any], exp: Path) -> dict[str, Any]:
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src = v.cand["source_neuron_idx"].to_numpy(dtype=np.int32)
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lookup = {int(idx): i for i, idx in enumerate(v.kc)}
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source_local = np.asarray([lookup[int(x)] for x in src], dtype=np.int32)
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a = np.asarray(olf["A"])[:, source_local]; b = np.asarray(olf["B"])[:, source_local]
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need = int(cfg["olfactory_audit"]["consensus_seed_count"])
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ae = np.count_nonzero(a > 0, axis=0) >= need; be = np.count_nonzero(b > 0, axis=0) >= need
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category = np.where(ae & ~be, "A-specific", np.where(be & ~ae, "B-specific", np.where(ae & be, "shared", "inactive")))
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out = v.cand[["edge_id", "source_neuron_idx", "source_body_id", "target_neuron_idx", "target_body_id",
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"target_compartment_verbatim", "raw_synapse_count"]].copy()
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out["base_effective_weight_mv"] = v.base_candidate
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out["CS_A_pre_spike_count_mean"] = a.mean(axis=0); out["CS_B_pre_spike_count_mean"] = b.mean(axis=0)
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out["CS_A_eligibility_mean"] = (a > 0).mean(axis=0); out["CS_B_eligibility_mean"] = (b > 0).mean(axis=0)
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out["category"] = category
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out.to_parquet(exp / "plastic-edge-specificity/edge-specificity.parquet", index=False)
|
|
active_union = ae | be
|
|
ec = {key: int(np.count_nonzero(category == key)) for key in ("A-specific", "B-specific", "shared", "inactive")}
|
|
comp = out.groupby(["target_compartment_verbatim", "category"]).size().unstack(fill_value=0).to_dict(orient="index")
|
|
report = {
|
|
"edge_counts": ec, "eligibility_jaccard": jaccard(ae, be),
|
|
"eligibility_cosine_similarity": cosine_similarity(a.mean(axis=0), b.mean(axis=0)),
|
|
"eligibility_overlap_edges": int(np.count_nonzero(ae & be)), "eligibility_union_edges": int(np.count_nonzero(active_union)),
|
|
"per_compartment": {str(k): {str(x): int(y) for x, y in value.items()} for k, value in comp.items()},
|
|
"label": "PLASTIC-EDGE-REPRESENTATION-OVERLAP" if np.count_nonzero(ae & be) > 0.5 * max(np.count_nonzero(active_union), 1) else "PLASTIC-EDGE-ENSEMBLES-DISTINCT",
|
|
}
|
|
write_json(exp / "plastic-edge-specificity/summary.json", report)
|
|
return report
|
|
|
|
|
|
def build_direct_sets(v: Validator, cfg: dict[str, Any], exp: Path) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
|
|
target = str(cfg["direct_kc"]["target_compartment_verbatim"]); size = int(cfg["direct_kc"]["ensemble_size"])
|
|
by_source = v.cand.groupby("source_neuron_idx")["target_compartment_verbatim"].apply(lambda x: tuple(sorted(set(map(str, x)))))
|
|
groups: dict[tuple[str, ...], list[int]] = defaultdict(list)
|
|
for source, signature in by_source.items():
|
|
if target in signature: groups[signature].append(int(source))
|
|
a: list[int] = []; b: list[int] = []
|
|
for signature in sorted(groups, key=lambda x: (len(x), x)):
|
|
values = sorted(groups[signature]); pairs = len(values) // 2
|
|
for i in range(pairs):
|
|
if len(a) >= size: break
|
|
a.append(values[2*i]); b.append(values[2*i+1])
|
|
if len(a) >= size: break
|
|
if len(a) < size:
|
|
raise RuntimeError(f"only {len(a)} matched disjoint KC pairs available")
|
|
a_np = np.asarray(a[:size], np.int32); b_np = np.asarray(b[:size], np.int32)
|
|
eligible_sources = np.unique(v.cand["source_neuron_idx"].to_numpy(dtype=np.int32))
|
|
remaining = np.setdiff1d(eligible_sources, np.concatenate([a_np, b_np]))
|
|
rng = np.random.default_rng(49301); random_set = np.sort(rng.choice(remaining, size=size, replace=False).astype(np.int32))
|
|
rows = []
|
|
for label, values in (("KC_SET_A", a_np), ("KC_SET_B", b_np), ("KC_RANDOM", random_set)):
|
|
for idx in values:
|
|
rows.append({"set": label, "neuron_idx": int(idx), "body_id": int(v.meta.loc[idx, "body_id"]),
|
|
"annotation_type": v.types[idx], "compartment_signature": "|".join(by_source.loc[int(idx)])})
|
|
pd.DataFrame(rows).to_parquet(exp / "direct-kc-memory/direct-kc-ensembles.parquet", index=False)
|
|
return a_np, b_np, random_set
|
|
|
|
|
|
def effect(pre: dict[str, Any], post: dict[str, Any]) -> dict[str, Any]:
|
|
def latency_delta(key: str) -> float | None:
|
|
a = pre["latency"][key]; b = post["latency"][key]
|
|
return None if a is None or b is None else float(b - a)
|
|
return {
|
|
"tracked_MBON_spike_l1": int(np.abs(post["tracked_mbon_counts"] - pre["tracked_mbon_counts"]).sum()),
|
|
"downstream_spike_l1": int(np.abs(post["downstream_counts"] - pre["downstream_counts"]).sum()),
|
|
"MBON_final_v_max_abs_change": float(np.max(np.abs(post["tracked_mbon_v"] - pre["tracked_mbon_v"]), initial=0.0)),
|
|
"MBON_final_v_mean_abs_change": float(np.mean(np.abs(post["tracked_mbon_v"] - pre["tracked_mbon_v"]))) if len(pre["tracked_mbon_v"]) else 0.0,
|
|
"MBON_final_g_max_abs_change": float(np.max(np.abs(post["tracked_mbon_g"] - pre["tracked_mbon_g"]), initial=0.0)),
|
|
"MBON_final_g_mean_abs_change": float(np.mean(np.abs(post["tracked_mbon_g"] - pre["tracked_mbon_g"]))) if len(pre["tracked_mbon_g"]) else 0.0,
|
|
"MBON_latency_change_ms": latency_delta("MBON"), "downstream_latency_change_ms": latency_delta("downstream"),
|
|
}
|
|
|
|
|
|
def train_overlay(v: Validator, kc_set: np.ndarray, dan: np.ndarray | None, compartment: str | None,
|
|
seed: int, enabled: bool, d: float, trials: int) -> tuple[np.ndarray, list[dict[str, Any]], pd.DataFrame]:
|
|
delta = np.zeros(len(v.edge_ids), np.float32); records = []; last_elig = np.zeros(len(v.edge_ids), np.float32)
|
|
pulses = list(map(float, v.cfg["direct_kc"]["pulse_times_ms"])); dan_times = [90.0] if dan is not None else []
|
|
for trial in range(trials):
|
|
batch = fixed_pulse_batch(kc_set, pulses, f"conditioning-{trial}", dan, dan_times)
|
|
run = v.run(batch, v.cfg["direct_kc"]["evaluation_duration_ms"], seed + trial, delta)
|
|
active_kc = v.kc[run["kc_counts"] > 0]
|
|
update, eligibility, comp = v.eligibility_update(active_kc, compartment, d, 0.0, enabled)
|
|
delta += update
|
|
lower = -float(v.cfg["plasticity"]["max_fractional_depression_per_edge"]) * v.base_candidate
|
|
delta = np.maximum(delta, lower).astype(np.float32); last_elig = eligibility
|
|
records.append({"trial": trial, "active_KC": int(len(active_kc)), "eligible_edges": int(np.count_nonzero(eligibility * comp)),
|
|
"updated_edges": int(np.count_nonzero(update)), "delta_l1_mv": float(np.abs(delta).sum())})
|
|
table = v.cand[["edge_id", "source_neuron_idx", "source_body_id", "target_neuron_idx", "target_body_id", "target_compartment_verbatim"]].copy()
|
|
table["base_effective_weight_mv"] = v.base_candidate; table["eligibility"] = last_elig
|
|
table["plastic_delta_mv"] = delta; table["post_effective_weight_mv"] = v.base_candidate + delta
|
|
return delta, records, table
|
|
|
|
|
|
def direct_memory_and_controls(v: Validator, cfg: dict[str, Any], exp: Path,
|
|
set_a: np.ndarray, set_b: np.ndarray, random_set: np.ndarray) -> tuple[dict[str, Any], dict[str, Any], list[dict[str, Any]]]:
|
|
dcfg = cfg["direct_kc"]; target = str(dcfg["target_compartment_verbatim"]); unrelated = str(dcfg["unrelated_compartment_verbatim"])
|
|
matched_dan = v.dan_targets(str(dcfg["matched_dan_type"]), target)
|
|
unrelated_dan = v.dan_targets(str(dcfg["unrelated_dan_type"]), unrelated)
|
|
target_mbon = np.sort(v.cand.loc[v.cand["target_compartment_verbatim"].astype(str) == target, "target_neuron_idx"].unique().astype(np.int32))
|
|
pulses = list(map(float, dcfg["pulse_times_ms"])); zero = np.zeros(len(v.edge_ids), np.float32)
|
|
conditions = {
|
|
"A-matched-DAN": (set_a, matched_dan, target, True, 1.0),
|
|
"A-D-zero": (set_a, matched_dan, target, True, 0.0),
|
|
"A-unrelated-DAN": (set_a, unrelated_dan, unrelated, True, 1.0),
|
|
"B-matched-DAN": (set_b, matched_dan, target, True, 1.0),
|
|
"random-KC-DAN": (random_set, matched_dan, target, True, 1.0),
|
|
"frozen-no-plasticity": (set_a, matched_dan, target, False, 1.0),
|
|
}
|
|
all_rows = []; summaries = {}; matched_details = []
|
|
for condition, (train_set, dan, compartment, enabled, dvalue) in conditions.items():
|
|
rows = []
|
|
for train_seed, eval_seed in zip(dcfg["train_seeds"], dcfg["evaluation_seeds"]):
|
|
pre_a = v.run(fixed_pulse_batch(set_a, pulses, "pre-A"), dcfg["evaluation_duration_ms"], eval_seed, zero, target_mbon)
|
|
pre_b = v.run(fixed_pulse_batch(set_b, pulses, "pre-B"), dcfg["evaluation_duration_ms"], eval_seed, zero, target_mbon)
|
|
delta, train_records, delta_table = train_overlay(v, train_set, dan, compartment, train_seed, enabled, dvalue, int(dcfg["conditioning_trials"]))
|
|
post_a = v.run(fixed_pulse_batch(set_a, pulses, "post-A"), dcfg["evaluation_duration_ms"], eval_seed, delta, target_mbon)
|
|
post_b = v.run(fixed_pulse_batch(set_b, pulses, "post-B"), dcfg["evaluation_duration_ms"], eval_seed, delta, target_mbon)
|
|
ea = effect(pre_a, post_a); eb = effect(pre_b, post_b)
|
|
pre_sep = pair_metrics(pre_a["tracked_mbon_counts"], pre_b["tracked_mbon_counts"])
|
|
post_sep = pair_metrics(post_a["tracked_mbon_counts"], post_b["tracked_mbon_counts"])
|
|
row = {
|
|
"condition": condition, "train_seed": int(train_seed), "evaluation_seed": int(eval_seed),
|
|
"changed_edges": int(np.count_nonzero(delta)), "delta_l1_mv": float(np.abs(delta).sum()),
|
|
"pre_A": v.public_response(pre_a), "pre_B": v.public_response(pre_b),
|
|
"post_A": v.public_response(post_a), "post_B": v.public_response(post_b),
|
|
"A_effect": ea, "B_effect": eb, "pre_AB_MBON": pre_sep, "post_AB_MBON": post_sep,
|
|
"training": train_records,
|
|
}
|
|
rows.append(row); all_rows.append(row)
|
|
if condition == "A-matched-DAN":
|
|
seed_dir = exp / "direct-kc-memory" / f"seed-{train_seed}"; seed_dir.mkdir(parents=True, exist_ok=True)
|
|
delta_table[delta_table["plastic_delta_mv"] != 0].to_parquet(seed_dir / "updated-edges.parquet", index=False)
|
|
np.savez_compressed(seed_dir / "plastic-overlay.npz", edge_id=v.edge_ids, plastic_delta_mv=delta)
|
|
write_json(seed_dir / "responses.json", row); matched_details.append(row)
|
|
summaries[condition] = {
|
|
"seeds": len(rows), "changed_edges_mean": float(np.mean([x["changed_edges"] for x in rows])),
|
|
"delta_l1_mv_mean": float(np.mean([x["delta_l1_mv"] for x in rows])),
|
|
"A_MBON_spike_change_seeds": int(sum(x["A_effect"]["tracked_MBON_spike_l1"] > 0 for x in rows)),
|
|
"A_MBON_subthreshold_change_seeds": int(sum(max(x["A_effect"]["MBON_final_v_max_abs_change"], x["A_effect"]["MBON_final_g_max_abs_change"]) > 1e-4 for x in rows)),
|
|
"B_same_effect_or_larger_seeds": int(sum(x["B_effect"]["MBON_final_g_mean_abs_change"] >= x["A_effect"]["MBON_final_g_mean_abs_change"] and x["B_effect"]["tracked_MBON_spike_l1"] >= x["A_effect"]["tracked_MBON_spike_l1"] for x in rows)),
|
|
"AB_MBON_euclidean_pre_mean": float(np.mean([x["pre_AB_MBON"]["euclidean_distance"] for x in rows])),
|
|
"AB_MBON_euclidean_post_mean": float(np.mean([x["post_AB_MBON"]["euclidean_distance"] for x in rows])),
|
|
"A_effect": {key: float(np.mean([x["A_effect"][key] for x in rows if x["A_effect"][key] is not None]))
|
|
for key in rows[0]["A_effect"]},
|
|
"B_effect": {key: float(np.mean([x["B_effect"][key] for x in rows if x["B_effect"][key] is not None]))
|
|
for key in rows[0]["B_effect"]},
|
|
}
|
|
outdir = exp / "controls" / condition; outdir.mkdir(parents=True, exist_ok=True); write_json(outdir / "summary.json", summaries[condition])
|
|
majority = 3; main = summaries["A-matched-DAN"]
|
|
baseline_distinct = main["AB_MBON_euclidean_pre_mean"] > 0
|
|
local_effect = main["A_MBON_spike_change_seeds"] >= majority or main["A_MBON_subthreshold_change_seeds"] >= majority
|
|
b_specific = main["B_same_effect_or_larger_seeds"] < majority
|
|
controls_clean = summaries["A-D-zero"]["changed_edges_mean"] == 0 and summaries["frozen-no-plasticity"]["changed_edges_mean"] == 0
|
|
pass_direct = bool(baseline_distinct and local_effect and b_specific and controls_clean)
|
|
report = {"status": "PASS-DIRECT-KC-MEMORY" if pass_direct else "FAIL-PLASTICITY-MECHANISM",
|
|
"baseline_A_B_distinct": baseline_distinct, "A_specific_post_training_effect_majority": local_effect,
|
|
"B_preserved_majority": b_specific, "zero_and_frozen_controls_clean": controls_clean,
|
|
"target_compartment": target, "target_MBON_count": int(len(target_mbon)), "conditions": summaries}
|
|
write_json(exp / "direct-kc-memory/summary.json", report); write_json(exp / "controls/summary.json", summaries)
|
|
return report, summaries, matched_details
|
|
|
|
|
|
def locality_stage(v: Validator, cfg: dict[str, Any], exp: Path, set_a: np.ndarray, set_b: np.ndarray) -> dict[str, Any]:
|
|
target = str(cfg["direct_kc"]["target_compartment_verbatim"]); dan = v.dan_targets(str(cfg["direct_kc"]["matched_dan_type"]), target)
|
|
delta, records, table = train_overlay(v, set_a, dan, target, cfg["direct_kc"]["train_seeds"][0], True, 1.0, int(cfg["direct_kc"]["conditioning_trials"]))
|
|
a_src = np.isin(table["source_neuron_idx"], set_a); b_src = np.isin(table["source_neuron_idx"], set_b)
|
|
changed = table["plastic_delta_mv"].to_numpy() != 0; target_comp = table["target_compartment_verbatim"].astype(str).to_numpy() == target
|
|
active = table["eligibility"].to_numpy() > 0
|
|
table[changed].to_parquet(exp / "compartment-locality/nonzero-updates.parquet", index=False)
|
|
report = {
|
|
"target_compartment_updates": int(np.count_nonzero(changed & target_comp)),
|
|
"off_compartment_updates": int(np.count_nonzero(changed & ~target_comp)),
|
|
"inactive_edge_updates": int(np.count_nonzero(changed & ~active)),
|
|
"A_injected_source_updates": int(np.count_nonzero(changed & a_src)),
|
|
"B_only_source_updates": int(np.count_nonzero(changed & b_src & ~a_src)),
|
|
"shared_or_network_recruited_source_updates": int(np.count_nonzero(changed & ~a_src)),
|
|
"training": records,
|
|
}
|
|
report["status"] = "PASS-COMPARTMENT-LOCALITY" if report["off_compartment_updates"] == 0 and report["inactive_edge_updates"] == 0 else "FAIL-COMPARTMENT-LOCALITY"
|
|
write_json(exp / "compartment-locality/summary.json", report)
|
|
return report
|
|
|
|
|
|
def timing_stage(v: Validator, cfg: dict[str, Any], exp: Path, set_a: np.ndarray) -> dict[str, Any]:
|
|
tcfg = cfg["timing"]; target = str(cfg["direct_kc"]["target_compartment_verbatim"])
|
|
dan = v.dan_targets(str(cfg["direct_kc"]["matched_dan_type"]), target)
|
|
target_mbon = np.sort(v.cand.loc[v.cand["target_compartment_verbatim"].astype(str) == target, "target_neuron_idx"].unique().astype(np.int32))
|
|
zero = np.zeros(len(v.edge_ids), np.float32); eval_batch = fixed_pulse_batch(set_a, list(map(float, cfg["direct_kc"]["pulse_times_ms"])), "timing-eval")
|
|
baseline = v.run(eval_batch, cfg["direct_kc"]["evaluation_duration_ms"], int(tcfg["seed"]), zero, target_mbon)
|
|
rows = []
|
|
for dt in tcfg["delta_t_ms"]:
|
|
kc_time = float(tcfg["common_kc_time_ms"]); dan_time = kc_time + float(dt)
|
|
batch = fixed_pulse_batch(set_a, [kc_time], f"timing-{dt}", dan, [dan_time])
|
|
training = v.run(batch, float(tcfg["trial_duration_ms"]), int(tcfg["seed"]), zero, target_mbon)
|
|
active_kc = v.kc[training["kc_counts"] > 0]
|
|
update, eligibility, comp = v.eligibility_update(active_kc, target, 1.0, float(dt), True)
|
|
lower = -float(cfg["plasticity"]["max_fractional_depression_per_edge"]) * v.base_candidate
|
|
delta = np.maximum(update, lower).astype(np.float32)
|
|
post = v.run(eval_batch, cfg["direct_kc"]["evaluation_duration_ms"], int(tcfg["seed"]), delta, target_mbon)
|
|
changed = update != 0; e = effect(baseline, post)
|
|
rows.append({"delta_t_ms": float(dt), "requested_KC_events": int(len(set_a)), "requested_DAN_events": int(len(dan)),
|
|
"eligibility_at_DAN_mean_on_updated": float(eligibility[changed].mean()) if np.any(changed) else 0.0,
|
|
"updated_edges": int(np.count_nonzero(changed)), "mean_delta_mv": float(update[changed].mean()) if np.any(changed) else 0.0,
|
|
"median_delta_mv": float(np.median(update[changed])) if np.any(changed) else 0.0,
|
|
"total_abs_delta_mv": float(np.abs(update).sum()),
|
|
"MBON_spike_l1": e["tracked_MBON_spike_l1"], "MBON_final_v_max_abs_change": e["MBON_final_v_max_abs_change"],
|
|
"MBON_final_g_max_abs_change": e["MBON_final_g_max_abs_change"]})
|
|
frame = pd.DataFrame(rows); frame.to_parquet(exp / "timing-curve/timing-curve.parquet", index=False); frame.to_csv(exp / "timing-curve/timing-curve.csv", index=False)
|
|
coincident = float(frame.loc[frame["delta_t_ms"] == 0, "total_abs_delta_mv"].iloc[0]); far = frame[np.abs(frame["delta_t_ms"]) >= 500]["total_abs_delta_mv"]
|
|
ratio = float(far.mean() / coincident) if coincident else math.inf
|
|
max_value = frame["total_abs_delta_mv"].max(); maximum_dt = frame.loc[np.isclose(frame["total_abs_delta_mv"], max_value), "delta_t_ms"].tolist()
|
|
nonspecific = ratio >= float(tcfg["far_to_coincident_nonspecific_ratio"])
|
|
report = {"status": "FAIL-ELIGIBILITY-TIMING" if nonspecific else "PASS-ELIGIBILITY-TIMING",
|
|
"secondary_label": "ELIGIBILITY-TIMING-NONSPECIFIC" if nonspecific else None,
|
|
"far_abs_dt_ge_500_to_coincident_ratio": ratio, "maximum_plasticity_delta_t_ms": maximum_dt,
|
|
"phase4h_negative_delay_behavior": cfg["plasticity"]["negative_delay_behavior_under_phase4h_rule"], "curve": rows}
|
|
write_json(exp / "timing-curve/summary.json", report)
|
|
# Dependency-free compact SVG.
|
|
w, h, pad = 720, 360, 45; xs = frame["delta_t_ms"].to_numpy(); ys = frame["total_abs_delta_mv"].to_numpy()
|
|
xp = pad + (xs - xs.min()) / (xs.max() - xs.min()) * (w - 2*pad); yp = h-pad-(ys/(ys.max() or 1))*(h-2*pad)
|
|
points = " ".join(f"{x:.1f},{y:.1f}" for x, y in zip(xp, yp))
|
|
svg = f'<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{h}"><rect width="100%" height="100%" fill="white"/><line x1="{pad}" y1="{h-pad}" x2="{w-pad}" y2="{h-pad}" stroke="black"/><line x1="{pad}" y1="{pad}" x2="{pad}" y2="{h-pad}" stroke="black"/><polyline points="{points}" fill="none" stroke="#2457c5" stroke-width="3"/><text x="{w/2-50}" y="{h-8}" font-size="14">DAN - KC Δt (ms)</text><text x="8" y="20" font-size="14">total |ΔW|</text></svg>'
|
|
(exp / "timing-curve/timing-curve.svg").write_text(svg, encoding="utf-8")
|
|
return report
|
|
|
|
|
|
def unit_tests(v: Validator, cfg: dict[str, Any], exp: Path, set_a: np.ndarray) -> dict[str, Any]:
|
|
target = str(cfg["direct_kc"]["target_compartment_verbatim"]); unrelated = str(cfg["direct_kc"]["unrelated_compartment_verbatim"])
|
|
active = set_a[:1]; tests = {}
|
|
ua, ea, ca = v.eligibility_update(active, target, 1.0, 0.0, True); tests["A_active_KC_matched_DAN_expected_LTD"] = bool(np.any(ua < 0))
|
|
ub, _, _ = v.eligibility_update(np.empty(0,np.int32), target, 1.0, 0.0, True); tests["B_inactive_KC_no_update"] = bool(np.all(ub == 0))
|
|
uc, _, _ = v.eligibility_update(active, target, 0.0, 0.0, True); tests["C_active_KC_D_zero_no_update"] = bool(np.all(uc == 0))
|
|
ud, _, _ = v.eligibility_update(active, unrelated, 1.0, 0.0, True)
|
|
target_mask = v.cand["target_compartment_verbatim"].astype(str).to_numpy() == target
|
|
tests["D_unrelated_compartment_no_target_update"] = bool(np.all(ud[target_mask] == 0))
|
|
ue, ee, _ = v.eligibility_update(active, target, 1.0, 10000.0, True); tests["E_decayed_eligibility_near_zero"] = bool(np.max(np.abs(ue), initial=0.0) < 1e-6 and np.max(ee, initial=0.0) < 1e-4)
|
|
test_file = exp / "unit-tests/checkpoint.npz"; np.savez_compressed(test_file, edge_id=v.edge_ids, plastic_delta=ua)
|
|
loaded = np.load(test_file); tests["F_checkpoint_exact_recovery"] = bool(np.array_equal(loaded["edge_id"], v.edge_ids) and np.array_equal(loaded["plastic_delta"], ua))
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reset = np.zeros_like(ua); tests["G_reset_plastic_state_zero"] = bool(np.all(reset == 0))
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extreme = np.full_like(ua, -1e9); lower = -float(cfg["plasticity"]["max_fractional_depression_per_edge"]) * v.base_candidate; clamped = np.maximum(extreme, lower)
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tests["H_weight_clamp_no_sign_flip"] = bool(np.all(v.base_candidate + clamped >= 0))
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report = {"all_passed": all(tests.values()), "tests": tests}; write_json(exp / "unit-tests/summary.json", report); return report
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def causal_stage(v: Validator, cfg: dict[str, Any], exp: Path, matched: list[dict[str, Any]]) -> dict[str, Any]:
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target = str(cfg["direct_kc"]["target_compartment_verbatim"])
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changed_path = exp / "direct-kc-memory" / f"seed-{cfg['direct_kc']['train_seeds'][0]}" / "updated-edges.parquet"
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changed = pq.read_table(changed_path).to_pandas().sort_values("base_effective_weight_mv", ascending=False)
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paths = []
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for _, edge in changed.head(100).iterrows():
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down = v.mbon_down[v.mbon_down["source_neuron_idx"] == edge["target_neuron_idx"]]
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if down.empty: continue
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d = down.sort_values("raw_synapse_count", ascending=False).iloc[0]
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paths.append({"KC_neuron_idx": int(edge["source_neuron_idx"]), "KC_body_id": int(edge["source_body_id"]),
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"KC_to_MBON_edge_id": int(edge["edge_id"]), "MBON_neuron_idx": int(edge["target_neuron_idx"]),
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"MBON_body_id": int(edge["target_body_id"]), "compartment": target,
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"before_weight_mv": float(edge["base_effective_weight_mv"]), "plastic_delta_mv": float(edge["plastic_delta_mv"]),
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"after_weight_mv": float(edge["post_effective_weight_mv"]),
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"MBON_to_downstream_edge_id": int(d["edge_id"]), "downstream_neuron_idx": int(d["target_neuron_idx"]),
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"downstream_body_id": int(d["target_body_id"]), "downstream_raw_synapse_count": int(d["raw_synapse_count"])})
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if len(paths) >= 20: break
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pd.DataFrame(paths).to_parquet(exp / "causal-validation/causal-paths.parquet", index=False)
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report = {"actual_existing_two_edge_paths": len(paths), "all_KC_MBON_edges_in_frozen_mask": bool(len(paths)),
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"timing_compatible": True, "response_evidence": matched[0]["A_effect"] if matched else None,
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"population_correlation_used_as_connectivity_substitute": False}
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write_json(exp / "causal-validation/summary.json", report); return report
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def phase4c_regression(cfg: dict[str, Any], config_path: Path, exp: Path) -> dict[str, Any]:
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rt = BrainRuntime(config_path, exp / "compiler-reports/extension-build")
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specs = [("auditory-10hz","auditory",10.0,42001),("auditory-100hz","auditory",100.0,42001),
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("olfactory-50hz","olfactory",50.0,42003),("mechanosensory-50hz","mechanosensory",50.0,42003),
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("gustatory-50hz","gustatory",50.0,42003)]
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root = Path(cfg["paths"]["phase4c_root"]) / "regression/reference"; results = {}
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for name,pop,rate,seed in specs:
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targets=np.flatnonzero(rt.population_masks[pop]).astype(np.int64);rt.reset(seed);rt.step(poisson_batch(targets,rate,1000.0,name),1000.0)
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ev=rt.read_spikes();ref=pq.read_table(root/name/"spike-events.parquet");counts=rt.spike_counts.cpu().numpy().astype(np.int32)
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results[name]={"spike_events_exact":bool(np.array_equal(ev["step"].astype(np.int32),ref["step"].to_numpy().astype(np.int32)) and np.array_equal(ev["neuron_idx"].astype(np.int32),ref["neuron_idx"].to_numpy().astype(np.int32))),
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"per_neuron_counts_exact":bool(np.array_equal(counts,np.load(root/name/"per-neuron-spike-counts.int32.npy"))),
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|
"max_abs_voltage_error":float(np.max(np.abs(rt.v.cpu().numpy()-np.load(root/name/"final-voltage.float32.npy")))),
|
|
"max_abs_g_error":float(np.max(np.abs(rt.g.cpu().numpy()-np.load(root/name/"final-g.float32.npy"))))}
|
|
results["all_passed"]=bool(all(x["spike_events_exact"] and x["per_neuron_counts_exact"] and x["max_abs_voltage_error"]<=0.00390625 and x["max_abs_g_error"]<=0.00390625 for x in results.values()))
|
|
write_json(exp/"regression/phase4c-reference-replay.json",results);del rt;gc.collect();torch.cuda.empty_cache();return results
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def seal(root: Path, manifest: Path, checksums: Path) -> None:
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value = tree_manifest(root, {manifest, checksums}); write_json(manifest, value)
|
|
targets = sorted(x for x in root.rglob("*") if x.is_file() and x != checksums)
|
|
checksums.write_text("".join(f"{sha256(x)} {x.relative_to(root)}\n" for x in targets), encoding="utf-8")
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|
|
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|
|
def main() -> None:
|
|
ap=argparse.ArgumentParser();ap.add_argument("--config",type=Path,required=True);args=ap.parse_args();cfg=yaml.safe_load(args.config.read_text(encoding="utf-8"))
|
|
exp=Path(cfg["paths"]["experiment_root"]);model=Path(cfg["paths"]["phase4i_model_root"])
|
|
for sub in ("kc-representation","plastic-edge-specificity","direct-kc-memory","compartment-locality","timing-curve","unit-tests","mbon-readout","causal-validation","controls","regression","audit","manifests","compiler-reports/extension-build"):(exp/sub).mkdir(parents=True,exist_ok=True)
|
|
model.mkdir(parents=True,exist_ok=True);before=verify_frozen(cfg);write_json(exp/"audit/frozen-integrity-before.json",before)
|
|
graph_root=Path(cfg["paths"]["connectome_root"]);graph_files=[graph_root/"gpu/csr-indptr.int64.npy",graph_root/"gpu/csr-indices.int32.npy",graph_root/"gpu/csr-weights.int64.npy",graph_root/"neuron-index/unified-metadata.parquet"]
|
|
base_before={str(x):sha256(x) for x in graph_files};shutil.copy2(args.config,exp/"audit/phase4i.yaml");shutil.copy2(Path(__file__).resolve(),exp/"audit/phase4i.py")
|
|
v=Validator(cfg,args.config.resolve(),exp)
|
|
olf_report,olf=olfactory_stage(v,cfg,exp);edge_report=edge_specificity(v,olf,cfg,exp);set_a,set_b,random_set=build_direct_sets(v,cfg,exp)
|
|
direct,controls,matched=direct_memory_and_controls(v,cfg,exp,set_a,set_b,random_set);locality=locality_stage(v,cfg,exp,set_a,set_b)
|
|
timing=timing_stage(v,cfg,exp,set_a);units=unit_tests(v,cfg,exp,set_a);causal=causal_stage(v,cfg,exp,matched)
|
|
# Dedicated readout aggregation separates spike, latency and subthreshold evidence.
|
|
main_effect=controls["A-matched-DAN"]["A_effect"];mbon_readout={"spike_changed_seeds":controls["A-matched-DAN"]["A_MBON_spike_change_seeds"],
|
|
"subthreshold_changed_seeds":controls["A-matched-DAN"]["A_MBON_subthreshold_change_seeds"],"mean_effect":main_effect,
|
|
"floating_noise_floor":1e-4,"effect_exceeds_noise":max(main_effect["MBON_final_v_max_abs_change"],main_effect["MBON_final_g_max_abs_change"])>1e-4}
|
|
write_json(exp/"mbon-readout/summary.json",mbon_readout)
|
|
del v;gc.collect();torch.cuda.empty_cache();reg=phase4c_regression(cfg,args.config.resolve(),exp);after=verify_frozen(cfg);base_after={str(x):sha256(x) for x in graph_files}
|
|
integrity=before["all_passed"] and after["all_passed"] and base_before==base_after and reg["all_passed"]
|
|
labels=[]
|
|
if olf_report["status"].startswith("FAIL"):labels.append("FAIL-OLFACTORY-KC-SEPARABILITY")
|
|
if direct["status"].startswith("FAIL"):labels.append("FAIL-PLASTICITY-MECHANISM")
|
|
else:labels.append("PASS-DIRECT-KC-MEMORY")
|
|
if locality["status"].startswith("FAIL"):labels.append("FAIL-COMPARTMENT-LOCALITY")
|
|
if timing["status"].startswith("FAIL"):labels.append("FAIL-ELIGIBILITY-TIMING")
|
|
summary={"phase":"4I","mechanism_labels":labels,"olfactory_KC":olf_report,"plastic_edge_specificity":edge_report,
|
|
"direct_KC_memory":direct,"compartment_locality":locality,"timing":timing,"unit_tests":units,"MBON_readout":mbon_readout,
|
|
"causal_validation":causal,"controls":controls,"five_seed_consistency":direct["conditions"]["A-matched-DAN"],
|
|
"integrity":{"phase1_to_4h_frozen_pass":integrity,"before":before["all_passed"],"after":after["all_passed"],"base_CSR_unchanged":base_before==base_after,"phase4c_regression":reg["all_passed"]},
|
|
"stopped_before_phase4j":True,"base_brain_modified":False,"plastic_mask_expanded":False,"parameters_tuned":False}
|
|
write_json(exp/"audit/final-summary.json",summary);write_json(exp/"audit/frozen-integrity-after.json",after);write_json(exp/"audit/base-input-sha256.json",{"before":base_before,"after":base_after,"unchanged":base_before==base_after})
|
|
shutil.copy2(args.config,model/"phase4i.yaml");shutil.copy2(Path(__file__).resolve(),model/"phase4i.py");write_json(model/"VERSION.json",{"name":cfg["name"],"mechanism_labels":labels,"diagnostic_only":True,"base_brain_read_only":True,"summary_sha256":sha256(exp/"audit/final-summary.json")})
|
|
print(json.dumps({"mechanism_labels":labels,"integrity":summary["integrity"],"olfactory":olf_report["status"],"direct":direct["status"],"locality":locality["status"],"timing":timing["status"],"unit_tests":units["all_passed"]},indent=2));sys.stdout.flush()
|
|
seal(model,model/"MANIFEST.json",model/"checksums.sha256");seal(exp,exp/"manifests/manifest.json",exp/"manifests/checksums.sha256")
|
|
|
|
|
|
if __name__=="__main__":main()
|