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#!/usr/bin/env python3
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"""Phase 4M: isolated KC->MBON efficacy mechanisms on frozen MaleCNS inputs."""
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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 json
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import resource
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import shutil
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import subprocess
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import sys
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import time
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from collections import 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 torch
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import yaml
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from phase4h_audit import sha256, verify_checksum, write_json
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from phase4i import Validator, phase4c_regression
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from phase4j import apply_v2, effect, verify_frozen
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from phase4k import dataset_checks, seal
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from phase4l import OdorSimulator, pn_drive
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MECHANISMS = ("additive", "multiplicative", "bounded_multiplicative")
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def frozen_checks(cfg: dict[str, Any]) -> dict[str, Any]:
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earlier = verify_frozen(cfg); later = {}
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for key, control in (("phase4j_root", "manifests/checksums.sha256"), ("phase4j_model_root", "checksums.sha256"),
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("phase4k_root", "manifests/checksums.sha256"), ("phase4k_model_root", "checksums.sha256"),
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("phase4l_root", "manifests/checksums.sha256"), ("phase4l_model_root", "checksums.sha256")):
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root = Path(cfg["paths"][key]); later[key] = verify_checksum(root, root / control)
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report = {"phase4c_to_4i": earlier["all_passed"], "phase4j_to_4l": {k: v["passed"] for k, v in later.items()}}
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report["all_passed"] = bool(earlier["all_passed"] and all(v["passed"] for v in later.values()))
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if not report["all_passed"]: raise RuntimeError("frozen Phase 4C-4L checksum failure")
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return report
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def frozen_hashes(cfg: dict[str, Any]) -> dict[str, str]:
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p = cfg["paths"]; root = Path(p["connectome_root"]); files = [root / "gpu/csr-indptr.int64.npy", root / "gpu/csr-indices.int32.npy", root / "gpu/csr-weights.int64.npy", root / "neuron-index/unified-metadata.parquet",
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Path(p["phase4h_root"]) / "plastic-mask/plastic-edge-candidates.parquet",
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Path(p["phase4j_root"]) / "audit/phase4j.py", Path(p["phase4j_model_root"]) / "phase4j.yaml",
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Path(p["phase4l_root"]) / "odor-encoder/frozen-odor-panel.parquet", Path(p["phase4l_root"]) / "odor-encoder/mapped-glomerular-vectors.npz",
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Path(p["phase4l_root"]) / "receptor-mapping/olfactory-mapping.parquet", Path(p["phase4l_root"]) / "manifests/checksums.sha256",
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Path(p["phase4l_model_root"]) / "checksums.sha256"]
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for f in files:
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if not f.is_file(): raise FileNotFoundError(f)
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return {str(f): sha256(f) for f in files}
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def load_odor_inputs(cfg: dict[str, Any]) -> tuple[pd.DataFrame, dict[str, np.ndarray], dict[str, Any], pd.DataFrame]:
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exp = Path(cfg["paths"]["phase4l_root"]); panel = pd.read_parquet(exp / "odor-encoder/frozen-odor-panel.parquet"); vecfile = np.load(exp / "odor-encoder/mapped-glomerular-vectors.npz"); vectors = {str(oid): vecfile[str(oid)].copy() for oid in panel.odor_id}
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selection = json.loads((exp / "odor-encoder/selection.json").read_text()); mapping = pd.read_parquet(exp / "receptor-mapping/olfactory-mapping.parquet")
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if selection["ODOR_A"]["odor_id"] not in vectors or selection["ODOR_B"]["odor_id"] not in vectors: raise RuntimeError("frozen odor identity mismatch")
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return panel, vectors, selection, mapping
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def anatomical_audit(v: Validator, cfg: dict[str, Any], exp: Path) -> tuple[np.ndarray, np.ndarray]:
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cand = v.cand; raw = np.asarray(v.rt.raw_np[v.edge_ids], dtype=np.int64); sign = v.rt.signs[cand.source_neuron_idx.to_numpy(np.int32)]
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w = (raw.astype(np.float32) * sign.astype(np.float32) * np.float32(cfg["model"]["w_syn_mv_per_anatomical_synapse"])).astype(np.float32)
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base = v.base_candidate.astype(np.float32)
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if not np.array_equal(raw, cand.raw_synapse_count.to_numpy(np.int64)): raise RuntimeError("plastic mask/raw CSR count mismatch")
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if not np.array_equal(w, base): raise RuntimeError("anatomical effective weight mismatch with frozen runtime")
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if not np.all(sign == 1): raise RuntimeError("presynaptic efficacy branch requires cholinergic positive KC candidates")
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if len(cand) != 14232 or len(np.unique(v.edge_ids)) != 14232: raise RuntimeError("plastic mask changed")
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report = {"plastic_mask_edges": len(cand), "raw_count_min": int(raw.min()), "raw_count_median": float(np.median(raw)), "raw_count_max": int(raw.max()),
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"raw_count_sum": int(raw.sum()), "anatomical_weight_min_mv": float(w.min()), "anatomical_weight_max_mv": float(w.max()),
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"all_cholinergic_positive": True, "anatomical_formula_verified_exactly": True, "base_CSR_read_only": True,
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"E_birth_state": 1.0, "other_brain_edges_E": 1.0}
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write_json(exp / "anatomical-weight/summary.json", report); return raw, w
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class ReplayEngine:
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"""Real KC spikes + existing KC->MBON edges + frozen MBON LIF, no invented edge."""
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def __init__(self, sim: OdorSimulator, targets: np.ndarray):
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self.sim = sim; self.targets = np.asarray(targets, np.int32); self.dt = sim.dt; self.rt = sim.rt; v = sim.v
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indices = v.rt.indices_np; sources = sim.sources; mask = np.isin(sources, v.kc) & np.isin(indices, self.targets); self.ids = np.flatnonzero(mask)
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self.src = sources[self.ids].astype(np.int32); self.target = indices[self.ids].astype(np.int32)
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pos = {int(x): i for i, x in enumerate(self.targets)}; self.target_local = np.fromiter((pos[int(x)] for x in self.target), np.int32, len(self.target))
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all_base = v.base.detach().cpu().numpy(); self.base = all_base[self.ids].astype(np.float32)
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can_index = {int(eid): i for i, eid in enumerate(v.edge_ids)}; self.candidate_local = np.fromiter((can_index.get(int(x), -1) for x in self.ids), np.int32, len(self.ids))
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self.by_source = {int(s): np.flatnonzero(self.src == s) for s in np.unique(self.src)}
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if not np.all(self.base > 0): raise RuntimeError("isolated KC replay contains nonpositive source weights")
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def run(self, spikes: pd.DataFrame, delta: np.ndarray, duration_ms: float) -> dict[str, Any]:
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weights = self.base.copy(); valid = self.candidate_local >= 0; weights[valid] += delta[self.candidate_local[valid]]
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grouped = {int(k): g.neuron_idx.to_numpy(np.int32) for k, g in spikes.groupby("step")}; nsteps = int(round(duration_ms / self.dt)); n = len(self.targets); q = np.zeros((self.rt.queue_len, n), np.float32)
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conductance = np.zeros(n, np.float32); voltage = np.full(n, self.rt.rest, np.float32); refractory = np.zeros(n, np.int32); counts = np.zeros(n, np.int32); first = np.full(n, -1, np.int32)
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integrated_g = np.zeros(n, np.float64); transmitted = 0.0; delivered = 0.0; wall0 = time.perf_counter()
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for step in range(nsteps):
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slot = step % self.rt.queue_len; conductance = conductance * np.float32(self.rt.g_decay) + q[slot]; delivered += float(q[slot].sum(dtype=np.float64)); q[slot].fill(0)
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integrated_g += conductance; refractory = np.maximum(refractory, 1) - 1; avail = refractory == 0
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voltage[avail] += np.float32(self.rt.alpha) * (conductance[avail] - (voltage[avail] - np.float32(self.rt.rest))); voltage[~avail] = np.float32(self.rt.reset_value)
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fired = avail & (voltage >= np.float32(self.rt.threshold)); counts += fired; first[(first < 0) & fired] = step; voltage[fired] = np.float32(self.rt.reset_value); refractory[fired] = self.rt.refractory_steps
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for source in grouped.get(step, np.empty(0, np.int32)):
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rel = self.by_source.get(int(source))
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if rel is not None:
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np.add.at(q[(step + self.rt.delay_steps) % self.rt.queue_len], self.target_local[rel], weights[rel]); transmitted += float(weights[rel].sum(dtype=np.float64))
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return {"counts": counts, "first": first, "final_g": conductance, "final_v": voltage, "integrated_g": integrated_g,
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"transmitted_weighted_events_mv": transmitted, "delivered_input_mv": delivered, "wall_seconds": time.perf_counter() - wall0}
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def replay_effect(pre: dict[str, Any], post: dict[str, Any], dt: float) -> dict[str, Any]:
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def first_ms(x: dict[str, Any]) -> float | None:
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a = x["first"]; return None if np.all(a < 0) else float(a[a >= 0].min() * dt)
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a, b = first_ms(pre), first_ms(post)
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return {"delta_g_max_abs_mv": float(np.abs(post["final_g"] - pre["final_g"]).max()), "delta_g_mean_abs_mv": float(np.abs(post["final_g"] - pre["final_g"]).mean()),
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"delta_v_max_abs_mv": float(np.abs(post["final_v"] - pre["final_v"]).max()), "delta_v_mean_abs_mv": float(np.abs(post["final_v"] - pre["final_v"]).mean()),
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"integrated_g_delta_sum_mv_steps": float((post["integrated_g"] - pre["integrated_g"]).sum()),
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"MBON_spikes_pre": int(pre["counts"].sum()), "MBON_spikes_post": int(post["counts"].sum()), "MBON_spike_count_delta": int(post["counts"].sum() - pre["counts"].sum()),
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"MBON_spike_L1": int(np.abs(post["counts"] - pre["counts"]).sum()), "first_latency_pre_ms": a, "first_latency_post_ms": b,
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"first_latency_delta_ms": None if a is None or b is None else float(b - a), "transmitted_delta_mv": float(post["transmitted_weighted_events_mv"] - pre["transmitted_weighted_events_mv"]),
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"transmitted_reduction_percent": float(100 * (pre["transmitted_weighted_events_mv"] - post["transmitted_weighted_events_mv"]) / pre["transmitted_weighted_events_mv"]) if pre["transmitted_weighted_events_mv"] else None,
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"MBON_total_KC_input_reduction_percent": float(100 * (pre["delivered_input_mv"] - post["delivered_input_mv"]) / pre["delivered_input_mv"]) if pre["delivered_input_mv"] else None,
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"isolated_input_scope": "all real KC->target-MBON edges, not all whole-brain MBON inputs"}
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def latency_direction(row: dict[str, Any]) -> bool:
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value = row.get("first_latency_delta_ms"); return bool(row["MBON_spike_count_delta"] < 0 or (value is not None and value > 0))
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def compare_A_B(a: dict[str, Any], b: dict[str, Any]) -> bool:
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if not latency_direction(a): return False
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if a["MBON_spike_L1"] > b["MBON_spike_L1"]: return True
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al = 0.0 if a["first_latency_delta_ms"] is None else max(float(a["first_latency_delta_ms"]), 0.0)
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bl = 0.0 if b["first_latency_delta_ms"] is None else max(float(b["first_latency_delta_ms"]), 0.0)
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return bool(al > bl and a["MBON_spike_L1"] >= b["MBON_spike_L1"])
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def main_experiment(v: Validator, sim: OdorSimulator, cfg: dict[str, Any], mcfg: dict[str, Any], exp: Path,
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panel: pd.DataFrame, vectors: dict[str, np.ndarray], selection: dict[str, Any], mapping: pd.DataFrame,
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anatomical: np.ndarray) -> tuple[dict[str, Any], dict[int, dict[int, dict[str, np.ndarray]]], ReplayEngine]:
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protocol = mcfg["protocol"]; points = list(map(int, protocol["pairing_counts"])); maxpair = max(points); seeds = list(map(int, protocol["train_seeds"])); eval_seeds = list(map(int, protocol["evaluation_seeds"]));
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if points != list(map(int, cfg["plasticity_accumulation"]["pairing_counts"])) or seeds != list(map(int, cfg["plasticity_accumulation"]["train_seeds"])) or eval_seeds != list(map(int, cfg["plasticity_accumulation"]["evaluation_seeds"])): raise RuntimeError("Phase 4L frozen pairing protocol mismatch")
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compartment = str(protocol["target_compartment_verbatim"]); dan = v.dan_targets(str(protocol["matched_dan_type"]), compartment); duration = float(protocol["duration_ms"])
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gloms = selection["glomeruli_order"]; matched_sum = float(np.median(panel.mapped_response_sum)); pn = {}
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for label in ("ODOR_A", "ODOR_B"):
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oid = selection[label]["odor_id"]; target, rate, _ = pn_drive(oid, vectors[oid], mapping, gloms, str(protocol["input_mode"]), matched_sum, cfg); pn[label] = (target, rate)
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src = v.cand.source_neuron_idx.to_numpy(np.int32); lookup = np.full(v.n, -1, np.int32); lookup[v.kc] = np.arange(len(v.kc), dtype=np.int32); loc = lookup[src]
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if np.any(loc < 0): raise RuntimeError("non-KC source in plastic mask")
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comp = v.cand.target_compartment_verbatim.astype(str).to_numpy() == compartment; k = float(cfg["eligibility_v2"]["eta_effective_mv"]) / float(cfg["model"]["w_syn_mv_per_anatomical_synapse"])
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if k <= 0 or k >= 1: raise RuntimeError("invalid frozen eta/Wsyn dimensionless conversion")
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bound = 1.0 - float(cfg["eligibility_v2"]["max_fractional_depression"]); checkpoints: dict[int, dict[int, dict[str, np.ndarray]]] = {}; histories = []; update_times = []; training_walls = []; reproduction = []
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zero = np.zeros(len(v.edge_ids), np.float32); target_mbon = np.unique(v.cand.loc[comp, "target_neuron_idx"].to_numpy(np.int32)); replay = ReplayEngine(sim, target_mbon)
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# Baseline replay uses the same deterministic evaluation KC spike trains for all mechanisms.
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evaluation = {}; pre_replay = {}
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for es in eval_seeds:
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for label, (targets, rates) in pn.items():
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run = sim.run_rates(targets, rates, es, duration, collect_kc_spikes=True); evaluation[(es, label)] = run["kc_spikes"]
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run["kc_spikes"].to_parquet(exp / "isolated-replay" / f"KC-spikes-{label}-seed-{es}.parquet", index=False)
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pre_replay[(es, label)] = replay.run(run["kc_spikes"], zero, duration)
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for ts in seeds:
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delta_add = zero.copy(); em = np.ones(len(v.edge_ids), np.float32); eb = np.ones(len(v.edge_ids), np.float32); checkpoints[ts] = {}; p4l = np.load(Path(cfg["paths"]["phase4l_root"]) / "plasticity-magnitude" / f"seed-{ts}-overlays.npz")
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if not np.array_equal(p4l["edge_id"], v.edge_ids): raise RuntimeError("Phase 4L additive checkpoint edge IDs differ")
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for pairing in range(1, maxpair + 1):
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run = sim.run_rates(*pn["ODOR_A"], ts + pairing, duration, delta=delta_add, dan_targets=dan, dan_pulses=[float(protocol["dan_time_ms"])])
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training_walls.append(run["wall_seconds"]); trace = run["kc_trace_at_dan"]; elig = trace[loc].astype(np.float32); active = comp & (elig > 0)
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old_m, old_b = em[active].copy(), eb[active].copy(); t0 = time.perf_counter()
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delta_add, _ = apply_v2(v, trace, compartment, 1.0, delta_add, cfg)
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factor = np.float32(1.0) - np.float32(k) * elig[active]; em[active] = np.maximum(np.float32(0.0), em[active] * factor)
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eb[active] = np.maximum(np.float32(bound), eb[active] * factor); update_times.append(time.perf_counter() - t0)
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for local, om, ob in zip(np.flatnonzero(active), old_m, old_b):
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histories.append({"train_seed": ts, "pairing": pairing, "edge_id": int(v.edge_ids[local]), "eligibility": float(elig[local]),
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"E_multiplicative_before": float(om), "E_multiplicative_after": float(em[local]), "E_bounded_before": float(ob), "E_bounded_after": float(eb[local])})
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if pairing in points:
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saved = p4l[f"pairing_{pairing}"].astype(np.float32); error = float(np.max(np.abs(saved - delta_add)))
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reproduction.append({"train_seed": ts, "pairing": pairing, "max_abs_error_mv": error, "exact": bool(np.array_equal(saved, delta_add))})
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if error > 1e-6: raise RuntimeError(f"Phase 4L additive training reproduction divergence seed={ts} pairing={pairing}: {error}")
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checkpoints[ts][pairing] = {"additive": delta_add.copy(), "multiplicative": em.copy(), "bounded_multiplicative": eb.copy(), "eligibility_at_last_DAN": elig.copy()}
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np.savez_compressed(exp / "efficacy-overlay" / f"E-seed-{ts}-pairings-{pairing}.npz", E_multiplicative=em, E_bounded_multiplicative=eb)
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p4l.close()
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pd.DataFrame(histories).to_parquet(exp / "efficacy-overlay/update-history.parquet", index=False)
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write_json(exp / "regression/phase4l-additive-reproduction.json", {"all_passed": all(x["max_abs_error_mv"] <= 1e-6 for x in reproduction), "checks": reproduction})
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if not np.all(np.isfinite([h["E_multiplicative_after"] for h in histories])) or not np.all(np.isfinite([h["E_bounded_after"] for h in histories])):
|
|
|
|
|
raise RuntimeError("nonfinite efficacy")
|
|
|
|
|
rows = []; replay_times = []
|
|
|
|
|
for ts, es in zip(seeds, eval_seeds):
|
|
|
|
|
for pairing in points:
|
|
|
|
|
state = checkpoints[ts][pairing]
|
|
|
|
|
for mechanism in MECHANISMS:
|
|
|
|
|
if mechanism == "additive": delta = state["additive"]; efficacy = 1.0 + delta / anatomical
|
|
|
|
|
else:
|
|
|
|
|
efficacy = state[mechanism]; delta = anatomical * (efficacy - 1.0)
|
|
|
|
|
if not np.all(np.isfinite(efficacy)) or np.any(efficacy < -1e-6) or np.any(efficacy > 1.0 + 1e-6): raise RuntimeError("invalid E state")
|
|
|
|
|
changed = np.abs(delta) > 1e-8; e_changed = efficacy[changed]; weighted_E = float(np.sum(anatomical[changed] * e_changed) / np.sum(anatomical[changed])) if np.any(changed) else 1.0
|
|
|
|
|
pairrow = {"train_seed": ts, "eval_seed": es, "pairings": pairing, "mechanism": mechanism, "updated_edges": int(changed.sum()),
|
|
|
|
|
"E_mean_all_mask": float(np.mean(efficacy)), "E_median_all_mask": float(np.median(efficacy)), "E_min_all_mask": float(np.min(efficacy)),
|
|
|
|
|
"E_mean_changed": float(np.mean(e_changed)) if len(e_changed) else 1.0, "E_median_changed": float(np.median(e_changed)) if len(e_changed) else 1.0,
|
|
|
|
|
"E_anatomical_weighted_changed": weighted_E, "effective_weight_delta_sum_mv": float(delta.sum()),
|
|
|
|
|
"effective_weight_reduction_percent_changed": float(100 * (1 - weighted_E)), "saturation_fraction_mask": float(np.count_nonzero(efficacy <= (bound + 1e-6)) / len(efficacy))}
|
|
|
|
|
result = {}
|
|
|
|
|
for label in ("ODOR_A", "ODOR_B"):
|
|
|
|
|
t0 = time.perf_counter(); post = replay.run(evaluation[(es, label)], delta, duration); replay_times.append(time.perf_counter() - t0)
|
|
|
|
|
result[label] = replay_effect(pre_replay[(es, label)], post, sim.dt)
|
|
|
|
|
pairrow.update({f"A_{k}": value for k, value in result["ODOR_A"].items() if k != "isolated_input_scope"})
|
|
|
|
|
pairrow.update({f"B_{k}": value for k, value in result["ODOR_B"].items() if k != "isolated_input_scope"})
|
|
|
|
|
pairrow["A_specific_functional"] = compare_A_B(result["ODOR_A"], result["ODOR_B"])
|
|
|
|
|
rows.append(pairrow)
|
|
|
|
|
table = pd.DataFrame(rows); table.to_parquet(exp / "isolated-replay/mechanism-comparison.parquet", index=False)
|
|
|
|
|
curve = []
|
|
|
|
|
for (mechanism, pairing), frame in table.groupby(["mechanism", "pairings"]):
|
|
|
|
|
curve.append({"mechanism": mechanism, "pairings": int(pairing), "seeds": int(len(frame)), "A_specific_functional_seeds": int(frame.A_specific_functional.sum()),
|
|
|
|
|
"E_mean_changed": float(frame.E_mean_changed.mean()), "E_median_changed": float(frame.E_median_changed.mean()), "E_anatomical_weighted_changed": float(frame.E_anatomical_weighted_changed.mean()),
|
|
|
|
|
"E_min_mask": float(frame.E_min_all_mask.min()), "effective_weight_reduction_percent_changed": float(frame.effective_weight_reduction_percent_changed.mean()),
|
|
|
|
|
"A_transmission_reduction_percent": float(frame.A_transmitted_reduction_percent.mean()), "B_transmission_reduction_percent": float(frame.B_transmitted_reduction_percent.mean()),
|
|
|
|
|
"A_total_KC_input_reduction_percent": float(frame.A_MBON_total_KC_input_reduction_percent.mean()), "B_total_KC_input_reduction_percent": float(frame.B_MBON_total_KC_input_reduction_percent.mean()),
|
|
|
|
|
"A_MBON_delta_g_max_abs_mv": float(frame.A_delta_g_max_abs_mv.mean()), "A_MBON_delta_v_max_abs_mv": float(frame.A_delta_v_max_abs_mv.mean()),
|
|
|
|
|
"A_MBON_spike_count_delta_mean": float(frame.A_MBON_spike_count_delta.mean()), "A_MBON_spike_L1_mean": float(frame.A_MBON_spike_L1.mean()),
|
|
|
|
|
"A_MBON_first_latency_delta_ms_mean": float(frame.A_first_latency_delta_ms.mean()) if frame.A_first_latency_delta_ms.notna().any() else None,
|
|
|
|
|
"B_MBON_spike_L1_mean": float(frame.B_MBON_spike_L1.mean()), "saturation_fraction_mask": float(frame.saturation_fraction_mask.mean())})
|
|
|
|
|
gate = {}; majority = int(mcfg["gates"]["seed_majority"])
|
|
|
|
|
for mech in MECHANISMS:
|
|
|
|
|
successes = [x for x in curve if x["mechanism"] == mech and x["A_specific_functional_seeds"] >= majority]
|
|
|
|
|
gate[mech] = {"pass": bool(successes), "first_passing_pairing": min((x["pairings"] for x in successes), default=None), "seed_majority": majority,
|
|
|
|
|
"endpoint_100_specific_functional_seeds": next(x["A_specific_functional_seeds"] for x in curve if x["mechanism"] == mech and x["pairings"] == 100)}
|
|
|
|
|
primary = gate["multiplicative"]["pass"] or gate["bounded_multiplicative"]["pass"]
|
|
|
|
|
report = {"status": "PASS-KC-MBON-FUNCTIONAL-TRANSFER-v2" if primary else "FAIL-KC-MBON-FUNCTIONAL-TRANSFER-v2", "presynaptic_functional_transfer_pass": primary,
|
|
|
|
|
"mechanism_gates": gate, "curve": curve, "same_KC_spikes_DAN_timing_mask_and_seeds": True, "phase4l_additive_reproduced": True,
|
|
|
|
|
"postsynaptic_branch_run": False, "no_dense_time_by_edge_state": True,
|
|
|
|
|
"performance": {"mean_training_brain_wall_seconds_per_pairing": float(np.mean(training_walls)), "mean_update_seconds_per_pairing": float(np.mean(update_times)),
|
|
|
|
|
"mean_isolated_replay_seconds": float(np.mean(replay_times)), "E_overlay_memory_bytes_two_float32_arrays": 2 * len(v.edge_ids) * 4}}
|
|
|
|
|
write_json(exp / "isolated-replay/summary.json", report)
|
|
|
|
|
return report, checkpoints, replay
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def anatomy_vs_efficacy(v: Validator, sim: OdorSimulator, cfg: dict[str, Any], exp: Path, anatomical: np.ndarray,
|
|
|
|
|
checkpoints: dict[int, dict[int, dict[str, np.ndarray]]], mcfg: dict[str, Any]) -> dict[str, Any]:
|
|
|
|
|
targets = v.cand.target_neuron_idx.to_numpy(np.int32); base = v.base.detach().cpu().numpy(); positive = np.maximum(base, 0.0)
|
|
|
|
|
target_total = np.bincount(v.rt.indices_np, weights=positive.astype(np.float64), minlength=v.n)
|
|
|
|
|
frames = []
|
|
|
|
|
for ts in map(int, mcfg["protocol"]["train_seeds"]):
|
|
|
|
|
state = checkpoints[ts][100]
|
|
|
|
|
for mechanism in ("multiplicative", "bounded_multiplicative"):
|
|
|
|
|
E = state[mechanism]; frame = v.cand[["edge_id", "source_neuron_idx", "source_body_id", "target_neuron_idx", "target_body_id", "raw_synapse_count", "target_compartment_verbatim"]].copy()
|
|
|
|
|
frame["train_seed"] = ts; frame["mechanism"] = mechanism; frame["anatomical_weight_mv"] = anatomical; frame["E_baseline"] = 1.0; frame["E_current"] = E
|
|
|
|
|
frame["effective_weight_mv"] = anatomical * E; frame["target_total_positive_anatomical_input_mv"] = target_total[targets]
|
|
|
|
|
frame["target_MBON_total_input_fraction_static"] = anatomical * E / np.maximum(target_total[targets], 1e-12)
|
|
|
|
|
frame["eligibility_at_last_DAN"] = state["eligibility_at_last_DAN"]
|
|
|
|
|
frame["DAN_channel"] = mcfg["protocol"]["matched_dan_type"]; frames.append(frame)
|
|
|
|
|
out = pd.concat(frames, ignore_index=True); out.to_parquet(exp / "anatomy-vs-efficacy/anatomy-vs-efficacy.parquet", index=False)
|
|
|
|
|
from scipy.stats import spearmanr
|
|
|
|
|
by = out[(out.train_seed == int(mcfg["protocol"]["train_seeds"][0])) & (out.mechanism == "bounded_multiplicative")]
|
|
|
|
|
rho = spearmanr(by.raw_synapse_count, by.effective_weight_mv).statistic
|
|
|
|
|
report = {"rows": int(len(out)), "unique_edges": int(out.edge_id.nunique()), "raw_count_vs_effective_weight_spearman_seed1_bounded": float(rho),
|
|
|
|
|
"high_anatomical_count_implies_high_current_effective_in_this_assumed_model_only": bool(rho > 0.9),
|
|
|
|
|
"physiological_influence_inferred_from_EM_count": False, "static_fraction_is_not_dynamic_MBON_input": True}
|
|
|
|
|
write_json(exp / "anatomy-vs-efficacy/summary.json", report); return report
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def run_wholebrain(v: Validator, sim: OdorSimulator, cfg: dict[str, Any], mcfg: dict[str, Any], exp: Path, panel: pd.DataFrame,
|
|
|
|
|
vectors: dict[str, np.ndarray], selection: dict[str, Any], mapping: pd.DataFrame,
|
|
|
|
|
anatomical: np.ndarray, checkpoints: dict[int, dict[int, dict[str, np.ndarray]]], isolated: dict[str, Any]) -> dict[str, Any]:
|
|
|
|
|
passed = [x for x in ("multiplicative", "bounded_multiplicative") if isolated["mechanism_gates"][x]["pass"]]
|
|
|
|
|
if not passed:
|
|
|
|
|
out = {"status": "NOT-RUN-ISOLATED-ENDPOINT-GATE-BLOCKED", "tested_mechanisms": [], "feedback_masks_memory": None}; write_json(exp / "whole-brain-replay/summary.json", out); return out
|
|
|
|
|
duration = float(mcfg["protocol"]["duration_ms"]); gloms = selection["glomeruli_order"]; matched = float(np.median(panel.mapped_response_sum)); pn = {}
|
|
|
|
|
for label in ("ODOR_A", "ODOR_B"):
|
|
|
|
|
oid = selection[label]["odor_id"]; pn[label] = pn_drive(oid, vectors[oid], mapping, gloms, str(mcfg["protocol"]["input_mode"]), matched, cfg)[:2]
|
|
|
|
|
rows = []; walls = []; zero = np.zeros(len(v.edge_ids), np.float32)
|
|
|
|
|
for ts, es in zip(map(int, mcfg["protocol"]["train_seeds"]), map(int, mcfg["protocol"]["evaluation_seeds"])):
|
|
|
|
|
baseline = {label: sim.run_rates(*pn[label], es, duration, delta=zero) for label in pn}
|
|
|
|
|
for mechanism in passed:
|
|
|
|
|
E = checkpoints[ts][100][mechanism]; delta = anatomical * (E - 1)
|
|
|
|
|
effects = {}
|
|
|
|
|
for label in pn:
|
|
|
|
|
post = sim.run_rates(*pn[label], es, duration, delta=delta); walls.append(post["wall_seconds"]); effects[label] = effect(baseline[label], post)
|
|
|
|
|
rows.append({"train_seed": ts, "eval_seed": es, "mechanism": mechanism, "odor": label, "MBON_spike_L1": effects[label]["MBON_spike_l1"],
|
|
|
|
|
"MBON_g_max_abs_mv": effects[label]["MBON_g_max_abs"], "MBON_v_max_abs_mv": effects[label]["MBON_v_max_abs"],
|
|
|
|
|
"MBON_latency_delta_ms": effects[label]["MBON_first_latency_delta_ms"], "downstream_spike_L1": effects[label]["downstream_spike_l1"],
|
|
|
|
|
"KC_spike_L1": int(np.abs(post["kc_counts"] - baseline[label]["kc_counts"]).sum()),
|
|
|
|
|
"finite": bool(post["finite"]), "active_neuron_fraction": float(np.count_nonzero(post["counts"]) / v.n), "wall_seconds": post["wall_seconds"]})
|
|
|
|
|
frame = pd.DataFrame(rows); frame.to_parquet(exp / "whole-brain-replay/results.parquet", index=False); gates = {}
|
|
|
|
|
for mechanism in passed:
|
|
|
|
|
sub = frame[frame.mechanism == mechanism]; a = sub[sub.odor == "ODOR_A"].sort_values("train_seed"); b = sub[sub.odor == "ODOR_B"].sort_values("train_seed")
|
|
|
|
|
wins = int(((a.MBON_spike_L1.to_numpy() > b.MBON_spike_L1.to_numpy()) & (a.MBON_spike_L1.to_numpy() > 0)).sum())
|
|
|
|
|
gates[mechanism] = {"A_specific_functional_seeds": wins, "pass": bool(wins >= int(mcfg["gates"]["seed_majority"]) and sub.finite.all() and sub.active_neuron_fraction.max() < .5),
|
|
|
|
|
"A_MBON_spike_L1_mean": float(a.MBON_spike_L1.mean()), "B_MBON_spike_L1_mean": float(b.MBON_spike_L1.mean()),
|
|
|
|
|
"A_downstream_spike_L1_mean": float(a.downstream_spike_L1.mean()), "B_downstream_spike_L1_mean": float(b.downstream_spike_L1.mean()),
|
|
|
|
|
"A_KC_spike_L1_mean": float(a.KC_spike_L1.mean()), "B_KC_spike_L1_mean": float(b.KC_spike_L1.mean())}
|
|
|
|
|
status = "PASS-WHOLE-BRAIN-KCMBON-TRANSFER" if any(x["pass"] for x in gates.values()) else "WHOLE-BRAIN-FEEDBACK-MASKS-MEMORY"
|
|
|
|
|
out = {"status": status, "tested_mechanisms": passed, "gates": gates, "feedback_masks_memory": status != "PASS-WHOLE-BRAIN-KCMBON-TRANSFER", "mean_post_brain_wall_seconds": float(np.mean(walls))}
|
|
|
|
|
write_json(exp / "whole-brain-replay/summary.json", out); return out
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def conditioning_gate(cfg: dict[str, Any], mcfg: dict[str, Any], exp: Path, isolated: dict[str, Any], whole: dict[str, Any], identity: dict[str, Any]) -> dict[str, Any]:
|
|
|
|
|
encoder = json.loads((Path(cfg["paths"]["phase4l_root"]) / "kc-representation/summary.json").read_text()); temporal = json.loads((Path(cfg["paths"]["phase4j_root"]) / "eligibility-v2/unit-tests.json").read_text())
|
|
|
|
|
gates = {"OlfactoryEncoder_v1": bool(encoder["candidate_generated"]), "TemporalEligibility_v2": bool(temporal["all_passed"]),
|
|
|
|
|
"KC_sparse_code": bool(encoder["modes"]["physiological"]["gate"] and encoder["modes"]["matched_energy"]["gate"]),
|
|
|
|
|
"A_B_specific_edges": bool(identity["A_specific_eligible_edges"] > 0 and identity["B_specific_eligible_edges"] > 0),
|
|
|
|
|
"isolated_functional_transfer_v2": bool(isolated["presynaptic_functional_transfer_pass"]),
|
|
|
|
|
"whole_brain_transfer": whole["status"] == "PASS-WHOLE-BRAIN-KCMBON-TRANSFER"}
|
|
|
|
|
report = {"status": "OPEN" if all(gates.values()) else "NOT-RUN-GATE-BLOCKED", "gates": gates, "formal_conditioning_run": False,
|
|
|
|
|
"acquisition": "NOT-RUN" if not all(gates.values()) else "PENDING", "controls": "NOT-RUN" if not all(gates.values()) else "PENDING"}
|
|
|
|
|
write_json(exp / "conditioning/gate.json", report); return report
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def integrity(cfg: dict[str, Any], mcfg: dict[str, Any], base_config: Path, exp: Path, before_hash: dict[str, str]) -> dict[str, Any]:
|
|
|
|
|
del mcfg
|
|
|
|
|
gc.collect(); torch.cuda.empty_cache(); checks = frozen_checks(cfg); data = dataset_checks(cfg); regression = phase4c_regression(cfg, base_config, exp); after = frozen_hashes(cfg)
|
|
|
|
|
report = {"all_passed": bool(checks["all_passed"] and data["all_passed"] and regression["all_passed"] and before_hash == after),
|
|
|
|
|
"frozen_checks": checks, "phase1_to_3b_dataset": data, "phase4c_reference_regression": regression,
|
|
|
|
|
"frozen_hashes_identical": before_hash == after, "before": before_hash, "after": after,
|
|
|
|
|
"base_CSR_modified": False, "APL_modified": False, "eligibility_v2_modified": False, "plastic_mask_expanded": False,
|
|
|
|
|
"OlfactoryEncoder_modified": False, "PlasticityAccumulation_modified": False}
|
|
|
|
|
write_json(exp / "audit/integrity.json", report); return report
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def main() -> None:
|
|
|
|
|
ap = argparse.ArgumentParser(); ap.add_argument("--config", type=Path, required=True); args = ap.parse_args(); mcfg = yaml.safe_load(args.config.read_text()); base_config = Path(mcfg["frozen_phase4l_config"])
|
|
|
|
|
cfg = yaml.safe_load(base_config.read_text()); cfg["paths"]["phase4l_root"] = cfg["paths"]["experiment_root"]; cfg["paths"]["phase4l_model_root"] = mcfg["frozen_phase4l_config"].rsplit("/", 1)[0]
|
|
|
|
|
exp, model = Path(mcfg["experiment_root"]), Path(mcfg["model_root"])
|
|
|
|
|
for sub in ("literature-audit", "anatomical-weight", "efficacy-overlay", "presynaptic", "postsynaptic", "anatomy-vs-efficacy", "isolated-replay", "whole-brain-replay", "conditioning", "controls", "performance", "regression", "audit", "manifests", "compiler-reports/extension-build"):
|
|
|
|
|
(exp / sub).mkdir(parents=True, exist_ok=True)
|
|
|
|
|
model.mkdir(parents=True, exist_ok=True)
|
|
|
|
|
if not (exp / "literature-audit/phase4m_literature_audit.json").is_file(): raise RuntimeError("literature audit must predate model implementation")
|
|
|
|
|
prior = frozen_checks(cfg); before_hash = frozen_hashes(cfg); write_json(exp / "audit/frozen-before.json", {"checks": prior, "hashes": before_hash})
|
|
|
|
|
shutil.copy2(args.config, exp / "audit/phase4m.yaml"); shutil.copy2(Path(__file__).resolve(), exp / "audit/phase4m.py")
|
|
|
|
|
panel, vectors, selection, mapping = load_odor_inputs(cfg); v = Validator(cfg, base_config, exp); sim = OdorSimulator(v, cfg, exp); raw, anatomical = anatomical_audit(v, cfg, exp)
|
|
|
|
|
isolated, checkpoints, replay = main_experiment(v, sim, cfg, mcfg, exp, panel, vectors, selection, mapping, anatomical)
|
|
|
|
|
anatomy = anatomy_vs_efficacy(v, sim, cfg, exp, anatomical, checkpoints, mcfg)
|
|
|
|
|
post = {"status": mcfg["postsynaptic"]["current_status"], "branch_defined": True, "run": False,
|
|
|
|
|
"reason": "postsynaptic nAChR evidence exists in adult Drosophila, but a quantitative rule for exact MaleCNS y5B'2a target and its receptor map is unavailable",
|
|
|
|
|
"combined_presynaptic_postsynaptic_run": False, "global_MBON_threshold_modified": False}
|
|
|
|
|
write_json(exp / "postsynaptic/summary.json", post)
|
|
|
|
|
whole = run_wholebrain(v, sim, cfg, mcfg, exp, panel, vectors, selection, mapping, anatomical, checkpoints, isolated)
|
|
|
|
|
edge_identity = json.loads((Path(cfg["paths"]["phase4l_root"]) / "eligible-edges/summary.json").read_text())
|
|
|
|
|
formal = conditioning_gate(cfg, mcfg, exp, isolated, whole, edge_identity)
|
|
|
|
|
# Formal conditioning implementation is intentionally gated separately: this
|
|
|
|
|
# diagnostic run never creates a memory claim from the efficacy comparison.
|
|
|
|
|
if formal["status"] == "OPEN":
|
|
|
|
|
formal["status"] = "NOT-RUN-IMPLEMENTATION-REQUIRES-CONTROLLED-FORMAL-PROTOCOL"
|
|
|
|
|
formal["formal_conditioning_run"] = False
|
|
|
|
|
formal["acquisition"] = "NOT-RUN"
|
|
|
|
|
formal["controls"] = "NOT-RUN"
|
|
|
|
|
write_json(exp / "conditioning/gate.json", formal)
|
|
|
|
|
controls = {"isolated_neutral_B": "RUN", "matched_seed_same_KC_spikes": "RUN", "D_zero": "analytic identity E=1, no dopamine update", "random_reward": "NOT-RUN-FORMAL-CONDITIONING-BLOCKED", "unrelated_DAN": "NOT-RUN-FORMAL-CONDITIONING-BLOCKED", "frozen_no_plasticity": "isolated E=1 reference replay"}
|
|
|
|
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write_json(exp / "controls/summary.json", controls)
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checkpoints_bytes = sum(p.stat().st_size for p in (exp / "efficacy-overlay").glob("E-seed-*.npz")); performance = isolated["performance"] | {"overlay_checkpoint_bytes_total": checkpoints_bytes,
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"peak_RAM_kib": int(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss), "peak_VRAM_bytes": int(torch.cuda.max_memory_allocated()),
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"whole_brain_mean_post_wall_seconds": whole.get("mean_post_brain_wall_seconds"), "dense_time_by_edge_state": False}
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write_json(exp / "performance/summary.json", performance)
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candidates = {"KCMBON-SynapticEfficacy-v1": True, "PresynapticEfficacy-v1": True,
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"KCMBON-FunctionalTransfer-v2": bool(isolated["presynaptic_functional_transfer_pass"]), "MaleCNS-OlfactoryAssociativeMemory-v1-candidate": False}
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del sim, v, replay, checkpoints; integrity_report = integrity(cfg, mcfg, base_config, exp, before_hash)
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summary = {"phase": "4M", "literature_audit": "literature-audit/phase4m_literature_audit.json", "E_math": mcfg["mechanisms"], "anatomical": json.loads((exp / "anatomical-weight/summary.json").read_text()),
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"isolated": isolated, "postsynaptic": post, "anatomy_vs_efficacy": anatomy, "whole_brain": whole, "conditioning": formal, "controls": controls,
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"performance": performance, "candidates": candidates, "integrity": integrity_report, "phase4n_started": False}
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write_json(exp / "audit/final-summary.json", summary); shutil.copy2(args.config, model / "phase4m.yaml"); shutil.copy2(Path(__file__).resolve(), model / "phase4m.py"); shutil.copy2(exp / "literature-audit/phase4m_literature_audit.json", model / "literature-audit.json")
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write_json(model / "VERSION.json", {"name": mcfg["name"], "candidates": candidates, "summary_sha256": sha256(exp / "audit/final-summary.json"), "base_CSR_read_only": True})
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seal(model, model / "MANIFEST.json", model / "checksums.sha256"); seal(exp, exp / "manifests/manifest.json", exp / "manifests/checksums.sha256")
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print(json.dumps({"phase": "4M", "isolated": isolated["status"], "whole_brain": whole["status"], "conditioning": formal["status"], "integrity": integrity_report["all_passed"]}, indent=2)); sys.stdout.flush()
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if __name__ == "__main__": main()
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Digital Drosophila Archive