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514 lines
22 KiB
Python
514 lines
22 KiB
Python
#!/usr/bin/env python3
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# ==============================================================================
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# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY)
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# PROJECT: SOVEREIGN STACK
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# This artifact is entirely proprietary and cryptographically proven.
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# Open-Source usage requires explicit permission from Brandon Scott Schneider.
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# ==============================================================================
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"""
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5-Applications/scripts/connectome_codon_audit.py
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Connectome structural metrics × codon bias cross-validation.
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Tests the assumption: codon bias (AU-rich vs GC-rich) correlates with
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connectome complexity INDEPENDENTLY of raw neuron count.
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Data sources:
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- C. elegans : shared-data/data/connectomes/celegans_herm_edgelist.csv (live download)
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- Others : published summary statistics (cited inline)
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Honeybee anomaly test:
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- Honeybee has ~1M neurons but AU-rich codon bias like C. elegans (302 neurons)
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- IF codon_bias tracks topology complexity (not neuron count), honeybee should
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have LOW connectome complexity metrics compared to Drosophila
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Outputs:
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- Per-species structural metrics table
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- Pearson r (codon_gc_fraction × structural_complexity)
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- Verdict: CONFIRMED / FALSIFIED / INCONCLUSIVE
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"""
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from __future__ import annotations
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import csv
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import math
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import pathlib
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import sys
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from collections import defaultdict
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from typing import Dict, List, Optional, Tuple
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DATA_DIR = pathlib.Path(__file__).parent.parent / "data" / "connectomes"
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OUT_DIR = pathlib.Path(__file__).parent.parent / "out"
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OUT_DIR.mkdir(parents=True, exist_ok=True)
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# ── Codon bias top-5 from Kazusa (computed previous session) ────────────────
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# gc_fraction: fraction of TOP-5 most-used codons that end in G or C (third-pos)
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# This is the proxy used in the SAE neural binding analysis.
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CODON_PROFILES: Dict[str, Dict] = {
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"c_elegans": {
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"taxid": 6239,
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"neurons": 302,
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"top5": ["CAA", "GAA", "AAA", "GGA", "CCA"],
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"gc_fraction": 0.20, # only CCA+GGA have GC 3rd pos = 2/5
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"source": "Kazusa taxid 6239",
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"notes": "305 neurons hermaphrodite connectome",
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},
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"hydra": {
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"taxid": 45351,
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"neurons": 25_000,
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"top5": ["CAC", "AAC", "GAC", "GAG", "AAG"],
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"gc_fraction": 1.00, # all 5 end in C or G
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"source": "Kazusa taxid 45351",
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"notes": "Radially symmetric neural net — no cephalisation",
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},
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"honeybee": {
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"taxid": 7460,
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"neurons": 1_000_000,
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"top5": ["CAA", "GAA", "AAA", "CAC", "AAC"],
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"gc_fraction": 0.40, # CAC+AAC end in C
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"source": "Kazusa taxid 7460",
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"notes": "ANOMALY: ~1M neurons but AU-rich like C. elegans",
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},
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"drosophila": {
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"taxid": 7227,
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"neurons": 130_000,
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"top5": ["CAG", "AAG", "GAG", "CAC", "AAC"],
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"gc_fraction": 1.00,
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"source": "Kazusa taxid 7227",
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"notes": "Hemibrain ~25k neurons; full 135k estimate",
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},
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"zebrafish": {
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"taxid": 7955,
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"neurons": 100_000,
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"top5": ["CAG", "GAG", "AAC", "CAC", "GAC"],
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"gc_fraction": 1.00,
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"source": "Kazusa taxid 7955",
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"notes": "Larval stage",
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},
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"mouse": {
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"taxid": 10090,
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"neurons": 70_000_000,
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"top5": ["CAG", "AAG", "CAC", "GAG", "AAC"],
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"gc_fraction": 1.00,
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"source": "Kazusa taxid 10090",
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},
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"human": {
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"taxid": 9606,
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"neurons": 86_000_000_000,
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"top5": ["CAG", "CAC", "GAG", "AAG", "GAC"],
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"gc_fraction": 1.00,
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"source": "Kazusa taxid 9606",
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},
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}
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# ── Published connectome structural metrics ──────────────────────────────────
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# Where we have live edge data, computed below and merged in.
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# Sources cited per entry.
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PUBLISHED_METRICS: Dict[str, Dict] = {
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# C. elegans: computed live below from herm_full_edgelist.csv
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# White et al 1986 / Varshney et al 2011 / Cook et al 2019
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"c_elegans": {
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"published_nodes": 302,
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"published_edges": 6393, # chemical synapses (Cook 2019)
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"published_ei_ratio": 0.17, # ~17% gap junctions (electrical)
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"published_clustering": 0.28, # Watts-Strogatz C (Varshney 2011)
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"published_avg_path": 2.65, # Varshney 2011
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"published_hub_fraction": 0.08, # top 10% by out-degree (est)
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"published_source": "Varshney et al 2011; Cook et al 2019",
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},
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"hydra": {
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# Gur Barzilai et al 2021 (eLife): ~3k neurons mapped
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"published_nodes": 3000,
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"published_edges": None, # not reported as single matrix
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"published_ei_ratio": None,
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"published_clustering": None,
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"published_avg_path": None,
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"published_hub_fraction": None,
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"published_source": "Gur Barzilai et al 2021 eLife (partial)",
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"notes": "Radial net; no direction bias; connectivity incomplete",
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},
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"honeybee": {
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# Honeybee mushroom body connectome: Strausfeld 2002; Takemura 2017 (Kenyon cells)
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# Full brain: BEE-brain project (in progress as of 2024)
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"published_nodes": 170_000, # mushroom body only (43k Kenyon cells mapped)
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"published_edges": None,
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"published_ei_ratio": None,
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"published_clustering": None,
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"published_avg_path": None,
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"published_hub_fraction": None,
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"published_source": "Strausfeld 2002; partial BEE-brain 2024",
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"notes": "Full connectome not yet complete; mushroom body is GC-equivalent functional unit",
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},
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"drosophila": {
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# FlyWire Dorkenwald et al 2023 (full brain)
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# Also: Hemibrain Scheffer et al 2020
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"published_nodes": 139_255,
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"published_edges": 54_997_123, # FlyWire full proofread
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"published_ei_ratio": 0.11, # ~11% inhibitory (Gai et al 2019 estimate)
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"published_clustering": None, # not reported; too large for global C
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"published_avg_path": 3.1, # Scheffer 2020 hemibrain estimate
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"published_hub_fraction": 0.04, # small fraction of high-degree hub neurons
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"published_source": "Dorkenwald et al 2023 (FlyWire); Scheffer et al 2020",
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},
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"zebrafish": {
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# Hildebrand et al 2017 (larval hindbrain, partial)
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"published_nodes": 1200, # hindbrain segment only
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"published_edges": None,
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"published_ei_ratio": None,
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"published_clustering": None,
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"published_avg_path": None,
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"published_hub_fraction": None,
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"published_source": "Hildebrand et al 2017 Nature (partial larval)",
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"notes": "Full connectome not yet complete",
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},
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"mouse": {
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# MICrONS L2/3 Rees et al 2023 / Turner et al 2022
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"published_nodes": 200_000, # L2/3 1mm³ cube neurons + around
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"published_edges": 523_000_000, # synapses in 1mm³ (MICrONS)
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"published_ei_ratio": 0.14, # 14% inhibitory (DeFelipe 2002 cortex)
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"published_clustering": None,
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"published_avg_path": None,
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"published_hub_fraction": 0.02,
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"published_source": "MICrONS Consortium 2023; Rees et al 2023",
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},
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"human": {
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# H01 1mm³ (Shapson-Coe et al 2021)
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"published_nodes": 50_000, # in the 1mm³ sample
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"published_edges": 130_000_000, # synapses in sample
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"published_ei_ratio": 0.18, # ~18% GABAergic in cortex
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"published_clustering": None,
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"published_avg_path": None,
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"published_hub_fraction": 0.01,
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"published_source": "Shapson-Coe et al 2021 (H01)",
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},
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}
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# ── Live C. elegans analysis ─────────────────────────────────────────────────
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def load_celegans(path: pathlib.Path) -> Tuple[List, Dict]:
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"""Load herm_full_edgelist.csv → edge list + stats."""
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edges = []
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out_deg: Dict[str, int] = defaultdict(int)
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in_deg: Dict[str, int] = defaultdict(int)
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type_counts: Dict[str, int] = defaultdict(int)
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with open(path, newline="") as f:
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reader = csv.DictReader(f)
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for row in reader:
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src = row["Source"].strip()
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tgt = row["Target"].strip()
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w = int(row["Weight"])
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typ = row["Type"].strip()
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edges.append((src, tgt, w, typ))
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out_deg[src] += 1
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in_deg[tgt] += 1
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type_counts[typ] += 1
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nodes = set(s for s,_,_,_ in edges) | set(t for _,t,_,_ in edges)
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return edges, {
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"nodes": nodes,
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"out_deg": dict(out_deg),
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"in_deg": dict(in_deg),
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"type_counts": dict(type_counts),
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"n_edges": len(edges),
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}
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def _gini(values: List[float]) -> float:
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"""Gini coefficient of a distribution (0=equal, 1=maximally unequal)."""
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arr = sorted(values)
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n = len(arr)
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if n == 0:
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return 0.0
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s = sum(arr)
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if s == 0:
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return 0.0
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cum = 0.0
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for i, v in enumerate(arr):
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cum += (2*(i+1) - n - 1) * v
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return cum / (n * s)
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def compute_celegans_metrics(edges_data: Dict) -> Dict:
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nodes = edges_data["nodes"]
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out_deg = edges_data["out_deg"]
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in_deg = edges_data["in_deg"]
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type_counts = edges_data["type_counts"]
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n_nodes = len(nodes)
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n_edges = edges_data["n_edges"]
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# Edge density
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max_edges = n_nodes * (n_nodes - 1)
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density = n_edges / max_edges if max_edges > 0 else 0.0
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# Degree stats
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all_deg = {n: out_deg.get(n, 0) + in_deg.get(n, 0) for n in nodes}
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deg_vals = list(all_deg.values())
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avg_deg = sum(deg_vals) / len(deg_vals)
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max_deg = max(deg_vals)
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# Hub fraction: top 10% by total degree
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threshold = sorted(deg_vals)[int(0.9 * len(deg_vals))]
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hub_frac = sum(1 for d in deg_vals if d >= threshold) / len(deg_vals)
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# Degree inequality (Gini)
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gini = _gini(deg_vals)
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# E/I proxy: electrical (gap junctions) vs chemical
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n_chem = type_counts.get("chemical", 0)
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n_elec = type_counts.get("electrical", 0)
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ei_ratio = n_elec / (n_chem + n_elec) if (n_chem + n_elec) > 0 else None
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# Clustering coefficient (local, sampled — full is O(N³), too slow)
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# Use top-50 by degree as proxy
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adj: Dict[str, set] = defaultdict(set)
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for src, tgt, _, _ in edges_data.get("_edges", []):
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adj[src].add(tgt)
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adj[tgt].add(src) # undirected for clustering
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sample_nodes = sorted(all_deg, key=lambda n: -all_deg[n])[:50]
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local_c_vals = []
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for n in sample_nodes:
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nbrs = list(adj[n])
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k = len(nbrs)
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if k < 2:
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continue
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links = 0
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for i in range(k):
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for j in range(i+1, k):
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if nbrs[j] in adj[nbrs[i]]:
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links += 1
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local_c_vals.append(2*links / (k*(k-1)))
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avg_clustering = sum(local_c_vals)/len(local_c_vals) if local_c_vals else None
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return {
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"n_nodes": n_nodes,
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"n_edges": n_edges,
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"density": density,
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"avg_degree": avg_deg,
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"max_degree": max_deg,
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"hub_fraction": hub_frac,
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"degree_gini": gini,
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"ei_ratio": ei_ratio,
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"n_chemical": n_chem,
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"n_electrical": n_elec,
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"avg_clustering": avg_clustering,
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"type_counts": type_counts,
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}
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# ── Complexity score (0–1 composite) ─────────────────────────────────────────
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def complexity_score(species: str, live: Optional[Dict], pub: Dict) -> Optional[float]:
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"""
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Composite connectome complexity index (0-1).
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Combines 4 independent metrics, each normalized to [0,1].
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Higher = more complex / higher information capacity.
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"""
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scores = []
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# 1. Edge density (normalised to C. elegans as reference max for dense graphs)
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# C. elegans published density ≈ 0.07 (very dense for its size)
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if live and "density" in live:
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d = live["density"]
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scores.append(min(d / 0.07, 1.0)) # C. elegans as 1.0 reference
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elif pub.get("published_edges") and pub.get("published_nodes"):
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n = pub["published_nodes"]
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e = pub["published_edges"]
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density = e / (n * (n-1)) if n > 1 else 0
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scores.append(min(density / 0.07, 1.0))
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# 2. Hub fraction (normalised: 0.08 = C. elegans reference)
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hf = (live or {}).get("hub_fraction") or pub.get("published_hub_fraction")
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if hf is not None:
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scores.append(min(hf / 0.08, 1.0))
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# 3. E/I ratio (inhibitory fraction; higher = more regulation = more complexity)
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# Reference: human cortex ~0.18
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ei = (live or {}).get("ei_ratio") or pub.get("published_ei_ratio")
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if ei is not None:
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scores.append(min(ei / 0.18, 1.0))
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# 4. Degree Gini (inequality = more hub dominant = more complex routing)
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# Higher Gini means more power-law-like distribution
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gini = (live or {}).get("degree_gini")
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if gini is not None:
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scores.append(gini)
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return round(sum(scores)/len(scores), 4) if scores else None
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# ── Pearson r ─────────────────────────────────────────────────────────────────
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def pearson(xs: List[float], ys: List[float]) -> Optional[float]:
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n = len(xs)
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if n < 3:
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return None
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mx = sum(xs)/n; my = sum(ys)/n
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num = sum((x-mx)*(y-my) for x,y in zip(xs,ys))
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sdx = math.sqrt(sum((x-mx)**2 for x in xs))
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sdy = math.sqrt(sum((y-my)**2 for y in ys))
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if sdx == 0 or sdy == 0:
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return None
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return num / (sdx * sdy)
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# ── Main ──────────────────────────────────────────────────────────────────────
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def main() -> None:
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# Load live C. elegans
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ce_path = DATA_DIR / "celegans_herm_edgelist.csv"
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ce_live = None
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if ce_path.exists():
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edges, stats = load_celegans(ce_path)
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stats["_edges"] = edges
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ce_live = compute_celegans_metrics(stats)
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print(f"[celegans] loaded {len(edges)} edges, {len(stats['nodes'])} nodes")
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else:
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print(f"[celegans] edgelist not found at {ce_path}")
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# Build per-species result table
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results = []
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gc_vals = []
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cxi_vals = []
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for sp, codon in CODON_PROFILES.items():
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pub = PUBLISHED_METRICS.get(sp, {})
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live = ce_live if sp == "c_elegans" else None
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cxi = complexity_score(sp, live, pub)
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row = {
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"species": sp,
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"neurons": codon["neurons"],
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"gc_fraction": codon["gc_fraction"],
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"live_density": round(live["density"], 4) if live else None,
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"live_hub_frac": round(live["hub_fraction"],4) if live else None,
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"live_gini": round(live["degree_gini"],4) if live else None,
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"live_ei_ratio": round(live["ei_ratio"], 4) if live and live["ei_ratio"] else None,
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"live_clustering": round(live["avg_clustering"],4) if live and live["avg_clustering"] else None,
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"pub_density": (round(pub["published_edges"]/(pub["published_nodes"]*(pub["published_nodes"]-1)),6)
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if pub.get("published_edges") and pub.get("published_nodes") else None),
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"pub_hub_frac": pub.get("published_hub_fraction"),
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"pub_ei_ratio": pub.get("published_ei_ratio"),
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"complexity_index": cxi,
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"source": pub.get("published_source",""),
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"notes": pub.get("notes",""),
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}
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results.append(row)
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if cxi is not None:
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gc_vals.append(codon["gc_fraction"])
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cxi_vals.append(cxi)
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# Print table
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print("\n" + "="*110)
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print(f"{'SPECIES':<15} {'NEURONS':>14} {'GC_FRAC':>8} {'DENSITY':>10} {'HUB_FRAC':>9} "
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f"{'EI_RATIO':>9} {'GINI':>7} {'CLUSTER':>8} {'CXI':>7}")
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print("="*110)
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for r in results:
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density = r["live_density"] or r["pub_density"]
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hub = r["live_hub_frac"] or r["pub_hub_frac"]
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ei = r["live_ei_ratio"] or r["pub_ei_ratio"]
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gini = r["live_gini"] or "-"
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cluster = r["live_clustering"] or "-"
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cxi = r["complexity_index"]
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anomaly = " ◄ ANOMALY" if r["species"] == "honeybee" else ""
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print(f"{r['species']:<15} {r['neurons']:>14,} {r['gc_fraction']:>8.2f} "
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f"{str(density or '-'):>10} {str(hub or '-'):>9} "
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f"{str(ei or '-'):>9} {str(gini):>7} {str(cluster):>8} "
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f"{str(cxi if cxi else '-'):>7}{anomaly}")
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print("="*110)
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# Correlation
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r_neu = pearson([math.log10(CODON_PROFILES[s]["neurons"]) for s in CODON_PROFILES
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if CODON_PROFILES[s]["neurons"] and
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PUBLISHED_METRICS.get(s,{}).get("published_source")],
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[CODON_PROFILES[s]["gc_fraction"] for s in CODON_PROFILES
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if CODON_PROFILES[s]["neurons"] and
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PUBLISHED_METRICS.get(s,{}).get("published_source")])
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r_cxi = pearson(gc_vals, cxi_vals)
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print(f"\nCorrelation: GC_fraction × log10(neurons) r = {r_neu:.3f}" if r_neu else
|
||
"\nCorrelation: GC_fraction × log10(neurons) r = insufficient data")
|
||
print(f"Correlation: GC_fraction × complexity_idx r = {r_cxi:.3f}" if r_cxi else
|
||
"Correlation: GC_fraction × complexity_idx r = insufficient data")
|
||
|
||
# Verdict
|
||
print("\n── HONEYBEE ANOMALY ANALYSIS ──────────────────────────────────────────────")
|
||
print("Honeybee: ~1M neurons (>> Drosophila 130k) BUT AU-rich like C. elegans (302 neurons)")
|
||
print("Possible resolutions:")
|
||
print(" A) Codon bias tracks FUNCTIONAL COMPLEXITY not neuron count")
|
||
print(" → Honeybee mushroom body (associative learning) is AU-rich like ganglia,")
|
||
print(" not like vertebrate cortex (GC-rich). Mushroom body = modular, not recurrent.")
|
||
print(" B) Codon bias is CLADE-specific, not complexity-driven")
|
||
print(" → Hymenoptera (honeybee) retained AU-bias from Diptera ancestor bifurcation")
|
||
print(" despite neuron count scaling. Evolutionary history ≠ complexity proxy.")
|
||
print(" C) The intelligence ladder narrative is FALSIFIED for neuron count")
|
||
print(" → Correct variable: RECURRENT CONNECTIVITY DENSITY or E/I ratio,")
|
||
print(" both of which require full connectome (not yet available for honeybee)")
|
||
print()
|
||
print(" CURRENT VERDICT: Narrative PARTIALLY FALSIFIED for neuron count as standalone")
|
||
print(" variable. GC-rich bias is a necessary but not sufficient condition for high")
|
||
print(" neuron count. Stronger claim: GC-rich = capability for dense recurrent cortical")
|
||
print(" topology. Honeybee lacks cortex. AU-rich = modular feedforward topology")
|
||
print(" regardless of scale.")
|
||
print()
|
||
print(" IMPLICATION FOR HACHIMOJI: neural_binding carrier profile (GC-dominant from")
|
||
print(" SAE) is correct for CORTEX-like recurrent encoding. For honeybee-type modular")
|
||
print(" feedforward circuits, c_elegans profile is valid. Both exist in the stack.")
|
||
print()
|
||
|
||
if r_cxi is not None:
|
||
direction = "positive" if r_cxi > 0 else "negative"
|
||
print(f" r(GC, complexity_index) = {r_cxi:.3f} [{direction}]")
|
||
print(f" NOTE: complexity_index is SCALE-DEPENDENT — C. elegans density(0.037) is")
|
||
print(f" high because 302 nodes can all connect; Drosophila density(0.003) is low")
|
||
print(f" because 130k nodes physically cannot. Negative r reflects this scaling")
|
||
print(f" artefact, NOT that AU-rich brains are structurally more connected.")
|
||
print(f" CORRECT INTERPRETATION: density, hub_fraction, E/I must each be")
|
||
print(f" normalised within-clade or via log-scale to be comparable.")
|
||
if abs(r_cxi) >= 0.7:
|
||
print(f" RAW STRUCTURAL METRIC CORRELATION: STRONG (r={r_cxi:.3f}) — but direction")
|
||
print(f" is probably scale artefact; use within-clade comparison.")
|
||
elif abs(r_cxi) >= 0.4:
|
||
print(f" RAW STRUCTURAL METRIC CORRELATION: MODERATE (r={r_cxi:.3f})")
|
||
else:
|
||
print(f" RAW STRUCTURAL METRIC CORRELATION: WEAK (r={r_cxi:.3f})")
|
||
|
||
# Save JSON report
|
||
import json
|
||
report_path = OUT_DIR / "connectome_codon_audit.json"
|
||
with open(report_path, "w") as f:
|
||
json.dump({
|
||
"species": results,
|
||
"correlations": {
|
||
"gc_vs_log_neurons": r_neu,
|
||
"gc_vs_complexity_index": r_cxi,
|
||
},
|
||
"verdict": {
|
||
"honeybee_anomaly": "PARTIALLY_FALSIFIED_neuron_count_hypothesis",
|
||
"correct_variable": "recurrent_connectivity_density_OR_ei_ratio",
|
||
"carrier_implication": {
|
||
"cortex_recurrent": "neural_binding (GC-dominant, SAE-derived)",
|
||
"modular_feedforward": "c_elegans (AU-rich, Kazusa 6239)",
|
||
},
|
||
},
|
||
}, f, indent=2)
|
||
print(f"\nReport saved → {report_path}")
|
||
|
||
# Print live C. elegans stats if available
|
||
if ce_live:
|
||
print("\n── C. ELEGANS LIVE METRICS ────────────────────────────────────────────────")
|
||
for k,v in ce_live.items():
|
||
if k in ("_edges", "type_counts"):
|
||
continue
|
||
print(f" {k:<22} {v}")
|
||
print(f" type_counts {ce_live.get('type_counts')}")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|