mirror of
https://github.com/rndlabsoy/fly-brain-full.git
synced 2026-09-10 11:20:43 +02:00
Enrique Manuel Rojas Aliaga
dc9336d45d
- Changed affiliation from Independent Researcher to Facultad de Ingenieria y Arquitectura, Universidad de San Martin de Porres - Updated contact email to enrique_rojas1@usmp.pe - Updated hardware specs from i7-13620H/RTX 4060 to i9-285HX/RTX 5090 - Regenerated paper PDF and figures with updated metadata - bioRxiv resubmission: BIORXIV/2026/712244
1334 lines
64 KiB
Python
1334 lines
64 KiB
Python
#!/usr/bin/env python3
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"""
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Generate bioRxiv-quality scientific paper PDF with publication figures.
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Emergent Individuality in Whole-Brain Connectome Simulations of Drosophila.
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Author: Enrique Manuel Rojas Aliaga - Universidad de San Martin de Porres
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"""
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import os
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import sys
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import numpy as np
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import pandas as pd
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from matplotlib.patches import FancyBboxPatch
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from matplotlib.lines import Line2D
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import torch
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from pathlib import Path
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from fpdf import FPDF
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from datetime import datetime
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# ═══════════════════════════════════════════════════════════════════════
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# Configuration
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# ═══════════════════════════════════════════════════════════════════════
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BASE = Path(__file__).resolve().parent
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HIST = BASE / "consciousness_history"
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DATA = BASE / "data"
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OUT = BASE / "paper_figures"
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OUT.mkdir(exist_ok=True)
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# Publication style (Nature-like)
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plt.rcParams.update({
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'font.family': 'serif',
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'font.serif': ['Times New Roman', 'DejaVu Serif', 'serif'],
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'font.size': 8,
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'axes.titlesize': 9,
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'axes.labelsize': 8,
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'xtick.labelsize': 7,
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'ytick.labelsize': 7,
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'legend.fontsize': 7,
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'figure.dpi': 300,
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'savefig.dpi': 300,
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'savefig.bbox': 'tight',
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'axes.spines.top': False,
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'axes.spines.right': False,
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'axes.linewidth': 0.6,
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'xtick.major.width': 0.6,
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'ytick.major.width': 0.6,
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'lines.linewidth': 1.0,
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'patch.linewidth': 0.6,
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'mathtext.fontset': 'dejavuserif',
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})
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# Color palette (colorblind-friendly)
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C_FLY0 = '#2166AC'
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C_FLY1 = '#B2182B'
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C_BASE = '#666666'
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C_ESCAPE = '#E66101'
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C_WALKING = '#5E3C99'
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C_GROOMING = '#1B7837'
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C_FLIGHT = '#D6604D'
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print("=" * 70)
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print(" GENERATING PAPER: Figures + PDF (bioRxiv format)")
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print("=" * 70)
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# ═══════════════════════════════════════════════════════════════════════
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# Load Data
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[1/12] Loading consciousness data...")
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def load_session(name):
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path = HIST / name / "consciousness_timeline.csv"
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if path.exists():
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df = pd.read_csv(path)
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if len(df) > 0 and 'CI' in df.columns:
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return df
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return None
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# All two-fly sessions (paired)
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TWO_FLY_PAIRS = [
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("session_20260311_225504_fly0", "session_20260311_225556_fly1", "22:55"),
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("session_20260311_230255_fly0", "session_20260311_230347_fly1", "23:02"),
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("session_20260311_230853_fly0", "session_20260311_230945_fly1", "23:08"),
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("session_20260311_233655_fly0", "session_20260311_233746_fly1", "23:36*"),
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("session_20260312_071403_fly0", "session_20260312_071508_fly1", "07:14"),
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("session_20260312_074258_fly0", "session_20260312_074353_fly1", "07:42"),
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("session_20260312_083414_fly0", "session_20260312_083508_fly1", "08:34"),
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("session_20260312_094510_fly0", "session_20260312_094601_fly1", "09:45"),
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]
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# Overnight session (longest, primary data)
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ov_fly0 = load_session("session_20260311_233655_fly0")
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ov_fly1 = load_session("session_20260311_233746_fly1")
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# Latest session
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lat_fly0 = load_session("session_20260312_094510_fly0")
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lat_fly1 = load_session("session_20260312_094601_fly1")
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# Early single-fly sessions
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early1 = load_session("session_20260311_134345")
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early2 = load_session("session_20260311_210436")
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print(f" Overnight: fly0={len(ov_fly0)} pts, fly1={len(ov_fly1)} pts")
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print(f" Latest: fly0={len(lat_fly0)} pts, fly1={len(lat_fly1)} pts")
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print(f" Two-fly pairs: {len(TWO_FLY_PAIRS)}")
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# Load plasticity data
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print("\n[2/12] Loading plasticity weights...")
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w_base = torch.load(DATA / "plastic_weights.pt", map_location='cpu', weights_only=True)
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w_fly0 = torch.load(DATA / "plastic_weights_fly0.pt", map_location='cpu', weights_only=True)
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w_fly1 = torch.load(DATA / "plastic_weights_fly1.pt", map_location='cpu', weights_only=True)
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if hasattr(w_base, 'to_dense'):
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w_base = w_base.to_dense().flatten()
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if hasattr(w_fly0, 'to_dense'):
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w_fly0 = w_fly0.to_dense().flatten()
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if hasattr(w_fly1, 'to_dense'):
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w_fly1 = w_fly1.to_dense().flatten()
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w_base_np = w_base.numpy()
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w_fly0_np = w_fly0.numpy()
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w_fly1_np = w_fly1.numpy()
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delta0 = w_fly0_np - w_base_np
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delta1 = w_fly1_np - w_base_np
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divergence = np.abs(w_fly0_np - w_fly1_np)
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n_synapses = len(w_base_np)
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n_divergent = int(np.sum(divergence > 0))
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pct_divergent = 100 * n_divergent / n_synapses
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max_div = divergence.max()
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print(f" Synapses: {n_synapses:,}")
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print(f" Divergent: {n_divergent:,} ({pct_divergent:.2f}%)")
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print(f" Max divergence: {max_div:.6f}")
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# ═══════════════════════════════════════════════════════════════════════
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# Utility
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# ═══════════════════════════════════════════════════════════════════════
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def smooth(y, w=20):
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return pd.Series(y).rolling(w, center=True, min_periods=1).mean()
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def panel_label(ax, label, x=-0.12, y=1.08):
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ax.text(x, y, label, transform=ax.transAxes, fontsize=11,
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fontweight='bold', va='top', ha='left')
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# ═══════════════════════════════════════════════════════════════════════
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# FIGURE 1: System Architecture
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[3/12] Generating Figure 1: Architecture...")
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fig1, ax = plt.subplots(1, 1, figsize=(7.0, 4.5))
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ax.set_xlim(0, 10)
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ax.set_ylim(0, 6.5)
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ax.axis('off')
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ax.set_aspect('equal')
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ax.text(5.0, 6.2, 'Closed-Loop Embodied Whole-Brain Architecture',
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ha='center', va='center', fontsize=11, fontweight='bold')
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box_brain = dict(boxstyle="round,pad=0.3", facecolor='#E8F0FE', edgecolor='#2166AC', linewidth=1.5)
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box_body = dict(boxstyle="round,pad=0.3", facecolor='#FFF3E0', edgecolor='#E65100', linewidth=1.5)
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box_sense = dict(boxstyle="round,pad=0.3", facecolor='#E8F5E9', edgecolor='#2E7D32', linewidth=1.5)
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box_plast = dict(boxstyle="round,pad=0.3", facecolor='#FCE4EC', edgecolor='#C62828', linewidth=1.5)
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box_cons = dict(boxstyle="round,pad=0.3", facecolor='#F3E5F5', edgecolor='#6A1B9A', linewidth=1.5)
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ax.text(5.0, 4.8,
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'Spiking Neural Network\n138,639 LIF neurons\n15,091,983 synapses (FlyWire v783)\nGPU-accelerated (PyTorch, 5 kHz)',
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ha='center', va='center', fontsize=7, bbox=box_brain)
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ax.text(5.0, 2.2,
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'Biomechanical Body\nNeuroMechFly v2 + MuJoCo\n87 joints, 6 legs, 2 wings\nContact physics + flight',
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ha='center', va='center', fontsize=7, bbox=box_body)
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ax.text(1.3, 3.5,
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'Sensory Systems\n721 ommatidia/eye\nBilateral ORN olfaction\nTarsal gustation\nJO mechanosensory',
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ha='center', va='center', fontsize=6.5, bbox=box_sense)
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ax.text(8.7, 4.8,
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'Hebbian\nPlasticity\n15M synapses\n$\\eta$=10$^{-4}$ $\\alpha$=10$^{-7}$',
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ha='center', va='center', fontsize=6.5, bbox=box_plast)
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ax.text(8.7, 2.2,
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'Integration\nMetrics\nIIT ($\\Phi$) | GWT\nSelf-Model\nComplexity',
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ha='center', va='center', fontsize=6.5, bbox=box_cons)
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# Arrows
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ax.annotate('', xy=(5.6, 2.95), xytext=(5.6, 3.95),
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arrowprops=dict(arrowstyle='->', color='#E65100', lw=2, mutation_scale=18))
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ax.text(6.1, 3.45, 'DN motor\ncommands', fontsize=6, color='#E65100', ha='left')
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ax.annotate('', xy=(2.4, 2.7), xytext=(3.75, 2.2),
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arrowprops=dict(arrowstyle='->', color='#2E7D32', lw=2, mutation_scale=18))
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ax.annotate('', xy=(3.75, 4.8), xytext=(2.4, 4.15),
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arrowprops=dict(arrowstyle='->', color='#2E7D32', lw=2, mutation_scale=18))
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ax.text(2.3, 4.55, 'sensory\ninput', fontsize=6, color='#2E7D32', ha='center')
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ax.annotate('', xy=(7.7, 4.8), xytext=(6.65, 4.8),
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arrowprops=dict(arrowstyle='<->', color='#C62828', lw=1.5, mutation_scale=15))
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ax.annotate('', xy=(7.7, 3.0), xytext=(6.3, 4.1),
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arrowprops=dict(arrowstyle='->', color='#6A1B9A', lw=1.3, mutation_scale=13))
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ax.add_patch(FancyBboxPatch((1.5, 0.3), 7.0, 1.1, boxstyle="round,pad=0.2",
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facecolor='#FFFDE7', edgecolor='#F57F17', linewidth=1.2))
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ax.text(5.0, 0.85, 'Two Independent Instances: Fly 0 + Fly 1',
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ha='center', va='center', fontsize=8, fontweight='bold', color='#F57F17')
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ax.text(5.0, 0.5, 'Same connectome | Independent sensory experience | Independent plasticity',
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ha='center', va='center', fontsize=6.5, color='#666')
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fig1.savefig(OUT / "fig1_architecture.png", dpi=300, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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plt.close(fig1)
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print(" -> fig1_architecture.png")
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# ═══════════════════════════════════════════════════════════════════════
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# FIGURE 2: CI Timelines (Overnight)
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[4/12] Generating Figure 2: CI timelines...")
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fig2, axes = plt.subplots(2, 1, figsize=(7.0, 3.8), sharex=True)
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t0 = ov_fly0['t_sim'].values
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t1 = ov_fly1['t_sim'].values
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for idx, (ax, df, t, name, color) in enumerate([
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(axes[0], ov_fly0, t0, 'Fly 0', C_FLY0),
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(axes[1], ov_fly1, t1, 'Fly 1', C_FLY1),
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]):
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ci = df['CI'].values
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ax.fill_between(t, 0, ci, alpha=0.12, color=color)
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ax.plot(t, ci, color=color, alpha=0.2, lw=0.3)
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ax.plot(t, smooth(ci), color=color, lw=1.0, label='CI (smoothed)')
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ax.axhline(ci.mean(), color=color, ls='--', lw=0.7, alpha=0.5)
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# Color by mode
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modes = df['mode'].values
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mode_colors = {'escape': C_ESCAPE, 'walking': C_WALKING, 'grooming': C_GROOMING}
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for i in range(len(t) - 1):
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m = modes[i]
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if m in mode_colors:
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ax.axvspan(t[i], t[i+1], alpha=0.05, color=mode_colors[m], lw=0)
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ax.set_ylabel('CI')
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ax.set_ylim(0, 0.55)
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panel_label(ax, 'AB'[idx])
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ax.text(0.98, 0.92, f'{name}: mean = {ci.mean():.3f} $\\pm$ {ci.std():.3f}',
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transform=ax.transAxes, fontsize=7, ha='right', va='top', color=color)
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axes[1].set_xlabel('Simulation time (s)')
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legend_elements = [
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Line2D([0], [0], color=C_ESCAPE, lw=4, alpha=0.3, label='Escape'),
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Line2D([0], [0], color=C_WALKING, lw=4, alpha=0.3, label='Walking'),
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Line2D([0], [0], color=C_GROOMING, lw=4, alpha=0.3, label='Grooming'),
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]
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axes[0].legend(handles=legend_elements, loc='upper right', fontsize=6, ncol=3,
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framealpha=0.8, edgecolor='none')
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fig2.tight_layout()
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fig2.savefig(OUT / "fig2_ci_timelines.png", dpi=300, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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plt.close(fig2)
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print(" -> fig2_ci_timelines.png")
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# ═══════════════════════════════════════════════════════════════════════
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# FIGURE 3: CI by Behavioral Mode
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[5/12] Generating Figure 3: CI by mode...")
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fig3, axes = plt.subplots(1, 2, figsize=(7.0, 2.8), sharey=True)
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for idx, (ax, df, name, color) in enumerate([
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(axes[0], ov_fly0, 'Fly 0', C_FLY0),
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(axes[1], ov_fly1, 'Fly 1', C_FLY1),
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]):
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modes = ['escape', 'walking', 'grooming']
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mode_data = [df[df['mode'] == m]['CI'].values for m in modes]
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mode_labels = ['Escape', 'Walking', 'Grooming']
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mode_clrs = [C_ESCAPE, C_WALKING, C_GROOMING]
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parts = ax.violinplot(mode_data, positions=range(len(modes)),
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showmeans=True, showmedians=False, showextrema=False)
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for i, pc in enumerate(parts['bodies']):
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pc.set_facecolor(mode_clrs[i])
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pc.set_alpha(0.5)
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parts['cmeans'].set_color('#333')
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parts['cmeans'].set_linewidth(1.5)
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for i, d in enumerate(mode_data):
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ax.text(i, -0.03, f'n={len(d)}', ha='center', fontsize=6, color='#888')
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ax.set_xticks(range(len(modes)))
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ax.set_xticklabels(mode_labels)
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ax.set_ylim(-0.05, 0.55)
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panel_label(ax, 'AB'[idx])
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ax.set_title(f'{name}', fontsize=9, fontweight='bold', color=color)
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axes[0].set_ylabel('Consciousness Index (CI)')
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fig3.tight_layout()
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fig3.savefig(OUT / "fig3_ci_by_mode.png", dpi=300, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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plt.close(fig3)
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print(" -> fig3_ci_by_mode.png")
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# ═══════════════════════════════════════════════════════════════════════
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# FIGURE 4: Component Decomposition
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[6/12] Generating Figure 4: Components...")
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fig4, axes = plt.subplots(4, 1, figsize=(7.0, 5.5), sharex=True)
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components = [
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('phi', '$\\Phi$ (IIT Proxy)'),
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('broadcast', 'Global Broadcast (GWT)'),
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('complexity', 'Perturbation Complexity'),
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('self', 'Self-Model'),
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]
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panel_labels = 'ABCD'
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for ax, (col, title), pl in zip(axes, components, panel_labels):
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ax.plot(t0, smooth(ov_fly0[col].values, 30), color=C_FLY0, lw=0.9, label='Fly 0')
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ax.plot(t1, smooth(ov_fly1[col].values, 30), color=C_FLY1, lw=0.9, label='Fly 1')
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ax.fill_between(t0, 0, smooth(ov_fly0[col].values, 30), alpha=0.08, color=C_FLY0)
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ax.fill_between(t1, 0, smooth(ov_fly1[col].values, 30), alpha=0.08, color=C_FLY1)
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ax.set_ylabel(title, fontsize=7)
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panel_label(ax, pl, x=-0.08)
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if ax == axes[0]:
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ax.legend(loc='upper right', fontsize=6, ncol=2, framealpha=0.8, edgecolor='none')
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axes[-1].set_xlabel('Simulation time (s)')
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fig4.tight_layout()
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fig4.savefig(OUT / "fig4_components.png", dpi=300, bbox_inches='tight',
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facecolor='white', edgecolor='none')
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plt.close(fig4)
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print(" -> fig4_components.png")
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# ═══════════════════════════════════════════════════════════════════════
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# FIGURE 5: Cross-Session Evolution
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# ═══════════════════════════════════════════════════════════════════════
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print("\n[7/12] Generating Figure 5: Cross-session evolution...")
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fig5, ax = plt.subplots(1, 1, figsize=(7.0, 3.2))
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# Single-fly sessions
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single_sessions = [
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("session_20260311_134345", "13:43"),
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("session_20260311_210436", "21:04"),
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]
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# Collect all data
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all_fly0_means = []
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all_fly0_stds = []
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all_fly1_means = []
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all_fly1_stds = []
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all_labels = []
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for sname, label in single_sessions:
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df = load_session(sname)
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if df is not None:
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all_fly0_means.append(df['CI'].mean())
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all_fly0_stds.append(df['CI'].std())
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all_fly1_means.append(np.nan)
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all_fly1_stds.append(np.nan)
|
|
all_labels.append(label)
|
|
|
|
for s0, s1, label in TWO_FLY_PAIRS:
|
|
df0 = load_session(s0)
|
|
df1 = load_session(s1)
|
|
if df0 is not None:
|
|
all_fly0_means.append(df0['CI'].mean())
|
|
all_fly0_stds.append(df0['CI'].std())
|
|
else:
|
|
all_fly0_means.append(np.nan)
|
|
all_fly0_stds.append(np.nan)
|
|
if df1 is not None:
|
|
all_fly1_means.append(df1['CI'].mean())
|
|
all_fly1_stds.append(df1['CI'].std())
|
|
else:
|
|
all_fly1_means.append(np.nan)
|
|
all_fly1_stds.append(np.nan)
|
|
all_labels.append(label)
|
|
|
|
x = np.arange(len(all_labels))
|
|
mask0 = ~np.isnan(all_fly0_means)
|
|
mask1 = ~np.isnan(all_fly1_means)
|
|
|
|
ax.errorbar(x[mask0], np.array(all_fly0_means)[mask0],
|
|
yerr=np.array(all_fly0_stds)[mask0],
|
|
color=C_FLY0, marker='o', ms=6, capsize=3, capthick=1.2, lw=1.5,
|
|
label='Fly 0 (or single)', zorder=3)
|
|
ax.errorbar(x[mask1] + 0.15, np.array(all_fly1_means)[mask1],
|
|
yerr=np.array(all_fly1_stds)[mask1],
|
|
color=C_FLY1, marker='s', ms=6, capsize=3, capthick=1.2, lw=1.5,
|
|
label='Fly 1', zorder=3)
|
|
|
|
ax.set_xticks(x)
|
|
ax.set_xticklabels(all_labels, fontsize=6.5, rotation=45, ha='right')
|
|
ax.set_ylabel('Mean CI ($\\pm$ s.d.)')
|
|
ax.set_ylim(0.05, 0.55)
|
|
ax.legend(fontsize=7, framealpha=0.8, edgecolor='none')
|
|
|
|
# Phase separator
|
|
ax.axvline(1.5, color='#999', ls=':', lw=0.7)
|
|
ax.text(0.5, 0.52, 'Single fly', ha='center', fontsize=6.5, color='#999')
|
|
ax.text(5.5, 0.52, 'Two flies', ha='center', fontsize=6.5, color='#999')
|
|
|
|
# Mark overnight session
|
|
ov_idx = 5 # index of the overnight session (23:36*)
|
|
ax.annotate('Overnight\n(primary)', xy=(ov_idx, all_fly0_means[ov_idx]),
|
|
xytext=(ov_idx - 1.5, 0.35), fontsize=6, color='#666',
|
|
arrowprops=dict(arrowstyle='->', color='#999', lw=0.8))
|
|
|
|
panel_label(ax, 'A', x=-0.08)
|
|
|
|
fig5.tight_layout()
|
|
fig5.savefig(OUT / "fig5_evolution.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig5)
|
|
print(" -> fig5_evolution.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# FIGURE 6: Plasticity Divergence (4-panel)
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n[8/12] Generating Figure 6: Plasticity...")
|
|
|
|
fig6 = plt.figure(figsize=(7.0, 5.0))
|
|
gs = fig6.add_gridspec(2, 2, hspace=0.4, wspace=0.35)
|
|
|
|
# 6A: Delta distribution
|
|
ax = fig6.add_subplot(gs[0, 0])
|
|
rng = np.random.RandomState(42)
|
|
sample_idx = rng.choice(len(delta0), min(500000, len(delta0)), replace=False)
|
|
ax.hist(delta0[sample_idx], bins=100, alpha=0.6, color=C_FLY0, label='Fly 0', density=True)
|
|
ax.hist(delta1[sample_idx], bins=100, alpha=0.4, color=C_FLY1, label='Fly 1', density=True)
|
|
ax.set_xlabel('$\\Delta W$ from baseline')
|
|
ax.set_ylabel('Density')
|
|
ax.legend(fontsize=6)
|
|
ax.set_xlim(-0.05, 0.05)
|
|
panel_label(ax, 'A')
|
|
|
|
# 6B: Divergence histogram
|
|
ax = fig6.add_subplot(gs[0, 1])
|
|
nonzero_div = divergence[divergence > 0]
|
|
log_div = np.log10(nonzero_div)
|
|
ax.hist(log_div, bins=50, color='#7570B3', alpha=0.8, edgecolor='white', lw=0.3)
|
|
ax.set_xlabel('$\\log_{10}(|W_0 - W_1|)$')
|
|
ax.set_ylabel('Count')
|
|
ax.text(0.95, 0.85, f'n = {len(nonzero_div):,}\nmax = {max_div:.2e}',
|
|
transform=ax.transAxes, fontsize=6.5, ha='right', va='top',
|
|
bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.7))
|
|
panel_label(ax, 'B')
|
|
|
|
# 6C: Potentiation vs Depression
|
|
ax = fig6.add_subplot(gs[1, 0])
|
|
pot = int(np.sum(delta0 > 0))
|
|
dep = int(np.sum(delta0 < 0))
|
|
unch = int(np.sum(delta0 == 0))
|
|
sizes = [pot, dep]
|
|
labels_pie = [
|
|
f'Potentiated\n{pot/1e6:.1f}M ({100*pot/n_synapses:.1f}%)',
|
|
f'Depressed\n{dep/1e6:.1f}M ({100*dep/n_synapses:.1f}%)',
|
|
]
|
|
colors_pie = ['#66C2A5', '#FC8D62']
|
|
wedges, texts = ax.pie(sizes, labels=labels_pie, colors=colors_pie,
|
|
startangle=90, textprops={'fontsize': 6.5})
|
|
panel_label(ax, 'C', x=-0.15)
|
|
|
|
# 6D: Top divergent synapses
|
|
ax = fig6.add_subplot(gs[1, 1])
|
|
top_idx = np.argsort(divergence)[-20:][::-1]
|
|
top_div = divergence[top_idx]
|
|
ax.barh(range(20), top_div[::-1], color='#E7298A', alpha=0.8, height=0.7)
|
|
ax.set_xlabel('$|W_0 - W_1|$')
|
|
ax.set_ylabel('Synapse rank')
|
|
ax.set_yticks(range(20))
|
|
ax.set_yticklabels([f'#{i+1}' for i in range(20)][::-1], fontsize=5.5)
|
|
panel_label(ax, 'D')
|
|
|
|
fig6.savefig(OUT / "fig6_plasticity.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig6)
|
|
print(" -> fig6_plasticity.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# FIGURE 7: Cross-Correlation & Behavioral Divergence
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n[9/12] Generating Figure 7: Cross-correlation...")
|
|
|
|
fig7 = plt.figure(figsize=(7.0, 4.8))
|
|
gs = fig7.add_gridspec(2, 2, hspace=0.45, wspace=0.35)
|
|
|
|
# 7A: Scatter plot CI
|
|
ax = fig7.add_subplot(gs[0, 0])
|
|
min_len = min(len(ov_fly0), len(ov_fly1))
|
|
ci0 = ov_fly0['CI'].values[:min_len]
|
|
ci1 = ov_fly1['CI'].values[:min_len]
|
|
ax.scatter(ci0, ci1, s=2, alpha=0.25, color='#7570B3', rasterized=True)
|
|
from numpy.polynomial.polynomial import polyfit
|
|
b, m = polyfit(ci0, ci1, 1)
|
|
x_fit = np.linspace(0, 0.5, 100)
|
|
ax.plot(x_fit, b + m * x_fit, 'r--', lw=1.0, alpha=0.7)
|
|
r = np.corrcoef(ci0, ci1)[0, 1]
|
|
ax.text(0.05, 0.95, f'r = {r:.3f}\nn = {min_len:,}', transform=ax.transAxes,
|
|
fontsize=7, va='top', fontweight='bold')
|
|
ax.set_xlabel('Fly 0 CI')
|
|
ax.set_ylabel('Fly 1 CI')
|
|
ax.set_xlim(0, 0.52)
|
|
ax.set_ylim(0, 0.52)
|
|
panel_label(ax, 'A')
|
|
|
|
# 7B: Temporal sensitization
|
|
ax = fig7.add_subplot(gs[0, 1])
|
|
for df, name, color in [(ov_fly0, 'Fly 0', C_FLY0), (ov_fly1, 'Fly 1', C_FLY1)]:
|
|
n = len(df)
|
|
q_size = n // 4
|
|
q_means = [df['CI'].iloc[i*q_size:(i+1)*q_size].mean() for i in range(4)]
|
|
ax.plot([1, 2, 3, 4], q_means, 'o-', color=color, lw=1.5, ms=6, label=name)
|
|
for i, v in enumerate(q_means):
|
|
ax.text(i + 1, v + 0.004, f'{v:.3f}', ha='center', fontsize=5.5, color=color)
|
|
|
|
ax.set_xticks([1, 2, 3, 4])
|
|
ax.set_xticklabels(['Q1\n0-25%', 'Q2\n25-50%', 'Q3\n50-75%', 'Q4\n75-100%'], fontsize=6)
|
|
ax.set_ylabel('Mean CI')
|
|
ax.legend(fontsize=6, framealpha=0.8, edgecolor='none')
|
|
panel_label(ax, 'B')
|
|
|
|
# 7C: Behavioral mode distribution
|
|
ax = fig7.add_subplot(gs[1, 0])
|
|
modes = ['escape', 'walking', 'grooming']
|
|
mode_labels_bar = ['Escape', 'Walking', 'Grooming']
|
|
|
|
fly0_pcts = [100 * (ov_fly0['mode'] == m).sum() / len(ov_fly0) for m in modes]
|
|
fly1_pcts = [100 * (ov_fly1['mode'] == m).sum() / len(ov_fly1) for m in modes]
|
|
|
|
x_bar = np.arange(len(modes))
|
|
w = 0.35
|
|
bars0 = ax.bar(x_bar - w/2, fly0_pcts, w, color=C_FLY0, alpha=0.8, label='Fly 0')
|
|
bars1 = ax.bar(x_bar + w/2, fly1_pcts, w, color=C_FLY1, alpha=0.8, label='Fly 1')
|
|
ax.set_xticks(x_bar)
|
|
ax.set_xticklabels(mode_labels_bar)
|
|
ax.set_ylabel('Time in mode (%)')
|
|
ax.legend(fontsize=6, framealpha=0.8, edgecolor='none')
|
|
|
|
for bar, pct in zip(bars0, fly0_pcts):
|
|
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
|
|
f'{pct:.0f}%', ha='center', fontsize=5.5, color=C_FLY0)
|
|
for bar, pct in zip(bars1, fly1_pcts):
|
|
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,
|
|
f'{pct:.0f}%', ha='center', fontsize=5.5, color=C_FLY1)
|
|
panel_label(ax, 'C')
|
|
|
|
# 7D: CI divergence over time
|
|
ax = fig7.add_subplot(gs[1, 1])
|
|
ci_diff = ci0 - ci1
|
|
t_ov = ov_fly0['t_sim'].values[:min_len]
|
|
sm_diff = smooth(ci_diff, 30)
|
|
ax.fill_between(t_ov, 0, sm_diff, where=sm_diff > 0,
|
|
alpha=0.3, color=C_FLY0, interpolate=True)
|
|
ax.fill_between(t_ov, 0, sm_diff, where=sm_diff < 0,
|
|
alpha=0.3, color=C_FLY1, interpolate=True)
|
|
ax.plot(t_ov, sm_diff, color='#333', lw=0.6)
|
|
ax.axhline(0, color='#999', ls='-', lw=0.4)
|
|
ax.set_xlabel('Time (s)')
|
|
ax.set_ylabel('$\\Delta$CI (Fly 0 $-$ Fly 1)')
|
|
ax.text(0.95, 0.95, 'Blue = Fly 0 higher\nRed = Fly 1 higher',
|
|
transform=ax.transAxes, fontsize=5.5, ha='right', va='top')
|
|
panel_label(ax, 'D')
|
|
|
|
fig7.savefig(OUT / "fig7_crosscorr.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig7)
|
|
print(" -> fig7_crosscorr.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# FIGURE 8: Comparison Table
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n[10/12] Generating Figure 8: Comparison table...")
|
|
|
|
fig8, ax = plt.subplots(1, 1, figsize=(7.0, 3.5))
|
|
ax.axis('off')
|
|
|
|
columns = ['Feature', 'Shiu 2024\n(Nature)', 'NMFv2 2024\n(Nat. Methods)',
|
|
'FlyGM 2026\n(arXiv)', 'This Work']
|
|
rows = [
|
|
['Full connectome (~139K)', 'YES', 'NO', 'YES', 'YES'],
|
|
['Spiking neurons (LIF)', 'YES', 'NO', 'NO', 'YES'],
|
|
['Embodied (MuJoCo)', 'NO', 'YES', 'YES', 'YES'],
|
|
['Closed-loop sensorimotor', 'NO', 'YES', 'YES', 'YES'],
|
|
['Multimodal senses (5)', 'Partial', 'YES', 'NO', 'YES'],
|
|
['Emergent behavior (no RL)', 'Partial', 'NO', 'NO', 'YES'],
|
|
['Hebbian plasticity', 'NO', 'NO', 'NO', 'YES'],
|
|
['Neural integration metrics', 'NO', 'NO', 'NO', 'YES'],
|
|
['Multi-individual experiment', 'NO', 'NO', 'NO', 'YES'],
|
|
]
|
|
|
|
table = ax.table(cellText=rows, colLabels=columns, loc='center',
|
|
cellLoc='center', colColours=['#E8E8E8'] * 5)
|
|
table.auto_set_font_size(False)
|
|
table.set_fontsize(6.5)
|
|
table.scale(1.0, 1.35)
|
|
|
|
for (row, col), cell in table.get_celld().items():
|
|
if row == 0:
|
|
cell.set_text_props(fontweight='bold', fontsize=6.5)
|
|
cell.set_facecolor('#D0D0D0')
|
|
elif col == 0:
|
|
cell.set_text_props(fontweight='bold', fontsize=6)
|
|
elif col == 4:
|
|
cell.set_facecolor('#E8F4E8')
|
|
txt = cell.get_text().get_text()
|
|
if txt == 'YES':
|
|
cell.get_text().set_color('#2E7D32')
|
|
cell.get_text().set_fontweight('bold')
|
|
elif txt == 'NO':
|
|
cell.get_text().set_color('#C62828')
|
|
elif txt == 'Partial':
|
|
cell.get_text().set_color('#E65100')
|
|
|
|
fig8.savefig(OUT / "fig8_comparison.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig8)
|
|
print(" -> fig8_comparison.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# FIGURE 9: Behavior Montage
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n[11/12] Generating Figure 9: Behavior montage...")
|
|
|
|
try:
|
|
import cv2
|
|
has_cv2 = True
|
|
except ImportError:
|
|
has_cv2 = False
|
|
|
|
# Extract frames from demo video if not already done
|
|
video_path = BASE / "demo.mp4"
|
|
if has_cv2 and video_path.exists():
|
|
cap = cv2.VideoCapture(str(video_path))
|
|
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
|
|
for pct in [10, 30, 50, 70]:
|
|
fpath = OUT / f"demo_frame_{pct}.png"
|
|
if not fpath.exists():
|
|
frame_idx = int(total_frames * pct / 100)
|
|
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
|
|
ret, frame = cap.read()
|
|
if ret:
|
|
cv2.imwrite(str(fpath), frame)
|
|
cap.release()
|
|
|
|
fig9, axes = plt.subplots(2, 3, figsize=(7.5, 4.5))
|
|
|
|
labels_row1 = ['Walking (early)', 'Exploring (mid)', 'Escape response']
|
|
pcts = [10, 30, 70]
|
|
for label, pct, ax in zip(labels_row1, pcts, axes[0]):
|
|
fpath = OUT / f"demo_frame_{pct}.png"
|
|
if has_cv2 and fpath.exists():
|
|
img = cv2.imread(str(fpath))
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
ax.imshow(img)
|
|
ax.set_title(label, fontsize=7, fontweight='bold')
|
|
ax.axis('off')
|
|
|
|
labels_row2 = ['Tripod gait (side)', 'Tripod gait (front)', 'Compound eye input']
|
|
fnames_row2 = ['fly_tripod_camera_right_frame.png', 'fly_tripod_camera_front_frame.png', None]
|
|
for title, fname, ax in zip(labels_row2, fnames_row2, axes[1]):
|
|
if fname and has_cv2:
|
|
fpath = OUT / fname
|
|
if fpath.exists():
|
|
img = cv2.imread(str(fpath))
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
ax.imshow(img)
|
|
elif fname is None and has_cv2:
|
|
eye_l_path = DATA / 'eye_L_0.png'
|
|
eye_r_path = DATA / 'eye_R_0.png'
|
|
if eye_l_path.exists() and eye_r_path.exists():
|
|
eye_l = cv2.imread(str(eye_l_path))
|
|
eye_r = cv2.imread(str(eye_r_path))
|
|
eye_l = cv2.cvtColor(eye_l, cv2.COLOR_BGR2RGB)
|
|
eye_r = cv2.cvtColor(eye_r, cv2.COLOR_BGR2RGB)
|
|
h = min(eye_l.shape[0], eye_r.shape[0])
|
|
eye_l = cv2.resize(eye_l, (int(eye_l.shape[1] * h / eye_l.shape[0]), h))
|
|
eye_r = cv2.resize(eye_r, (int(eye_r.shape[1] * h / eye_r.shape[0]), h))
|
|
combined = np.concatenate([eye_l, np.ones((h, 10, 3), dtype=np.uint8) * 255, eye_r], axis=1)
|
|
ax.imshow(combined)
|
|
ax.set_title(title, fontsize=7, fontweight='bold')
|
|
ax.axis('off')
|
|
|
|
fig9.tight_layout()
|
|
fig9.savefig(OUT / "fig9_behavior.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig9)
|
|
print(" -> fig9_behavior.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# FIGURE 10: Compound Eye Detail
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n[12/12] Generating Figure 10: Compound eyes...")
|
|
|
|
fig10, axes = plt.subplots(1, 4, figsize=(7.5, 2.0))
|
|
|
|
eye_files = [
|
|
('data/eye_L_0.png', 'Left eye (t=0)'),
|
|
('data/eye_R_0.png', 'Right eye (t=0)'),
|
|
('data/eye_L_20.png', 'Left eye (t=20s)'),
|
|
('data/eye_R_20.png', 'Right eye (t=20s)'),
|
|
]
|
|
|
|
for ax, (fpath, title) in zip(axes, eye_files):
|
|
full_path = BASE / fpath
|
|
if has_cv2 and full_path.exists():
|
|
img = cv2.imread(str(full_path))
|
|
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
ax.imshow(img)
|
|
ax.set_title(title, fontsize=7, fontweight='bold')
|
|
ax.axis('off')
|
|
|
|
fig10.suptitle('Compound Eye Visual Input (721 ommatidia per eye)', fontsize=9, fontweight='bold')
|
|
fig10.tight_layout(rect=[0, 0, 1, 0.88])
|
|
fig10.savefig(OUT / "fig10_eyes.png", dpi=300, bbox_inches='tight',
|
|
facecolor='white', edgecolor='none')
|
|
plt.close(fig10)
|
|
print(" -> fig10_eyes.png")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# Compute summary statistics for the paper text
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
# Cross-session CI stats
|
|
all_ci_fly0 = []
|
|
all_ci_fly1 = []
|
|
for s0, s1, _ in TWO_FLY_PAIRS:
|
|
d0 = load_session(s0)
|
|
d1 = load_session(s1)
|
|
if d0 is not None:
|
|
all_ci_fly0.append(d0['CI'].mean())
|
|
if d1 is not None:
|
|
all_ci_fly1.append(d1['CI'].mean())
|
|
|
|
grand_mean_0 = np.mean(all_ci_fly0)
|
|
grand_mean_1 = np.mean(all_ci_fly1)
|
|
asymmetry_pct = 100 * (grand_mean_0 - grand_mean_1) / grand_mean_1
|
|
|
|
# Behavioral stats (overnight)
|
|
esc0_pct = 100 * (ov_fly0['mode'] == 'escape').sum() / len(ov_fly0)
|
|
esc1_pct = 100 * (ov_fly1['mode'] == 'escape').sum() / len(ov_fly1)
|
|
grm0_pct = 100 * (ov_fly0['mode'] == 'grooming').sum() / len(ov_fly0)
|
|
grm1_pct = 100 * (ov_fly1['mode'] == 'grooming').sum() / len(ov_fly1)
|
|
|
|
# Phi stats
|
|
phi0_mean = ov_fly0['phi'].mean()
|
|
phi1_mean = ov_fly1['phi'].mean()
|
|
bcast0_mean = ov_fly0['broadcast'].mean()
|
|
bcast1_mean = ov_fly1['broadcast'].mean()
|
|
|
|
print(f"\n Summary statistics:")
|
|
print(f" Grand mean CI: Fly0={grand_mean_0:.4f}, Fly1={grand_mean_1:.4f} ({asymmetry_pct:.1f}% asymmetry)")
|
|
print(f" Escape: Fly0={esc0_pct:.1f}%, Fly1={esc1_pct:.1f}%")
|
|
print(f" Grooming: Fly0={grm0_pct:.1f}%, Fly1={grm1_pct:.1f}%")
|
|
print(f" r(CI) = {r:.3f}")
|
|
|
|
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
# PDF ASSEMBLY
|
|
# ═══════════════════════════════════════════════════════════════════════
|
|
|
|
print("\n" + "=" * 70)
|
|
print(" ASSEMBLING PDF (bioRxiv format)")
|
|
print("=" * 70)
|
|
|
|
|
|
class PaperPDF(FPDF):
|
|
def header(self):
|
|
if self.page_no() > 1:
|
|
self.set_font('Helvetica', 'I', 7)
|
|
self.set_text_color(128)
|
|
self.cell(0, 5, 'Rojas Aliaga (2026) - Emergent Individuality in Drosophila Connectome Simulations',
|
|
new_x="RIGHT", new_y="TOP")
|
|
self.cell(0, 5, f'{self.page_no()}', new_x="LMARGIN", new_y="NEXT")
|
|
self.ln(3)
|
|
self.set_text_color(0)
|
|
|
|
def footer(self):
|
|
pass
|
|
|
|
def section_title(self, title):
|
|
self.set_font('Helvetica', 'B', 11)
|
|
self.set_text_color(33, 33, 33)
|
|
self.cell(0, 8, title, new_x="LMARGIN", new_y="NEXT")
|
|
self.set_text_color(0)
|
|
self.ln(1)
|
|
|
|
def subsection_title(self, title):
|
|
self.set_font('Helvetica', 'B', 9.5)
|
|
self.set_text_color(80, 80, 80)
|
|
self.cell(0, 7, title, new_x="LMARGIN", new_y="NEXT")
|
|
self.set_text_color(0)
|
|
|
|
def body_text(self, text):
|
|
self.set_font('Helvetica', '', 8.5)
|
|
self.multi_cell(0, 4.2, text, new_x="LMARGIN", new_y="NEXT")
|
|
self.ln(1.5)
|
|
|
|
def add_figure(self, img_path, caption, width=170):
|
|
space_needed = 75
|
|
if self.get_y() + space_needed > 270:
|
|
self.add_page()
|
|
if Path(img_path).exists():
|
|
self.image(str(img_path), x=20, w=width)
|
|
self.ln(2)
|
|
self.set_font('Helvetica', 'I', 7)
|
|
self.set_text_color(80)
|
|
self.multi_cell(0, 3.5, caption, new_x="LMARGIN", new_y="NEXT")
|
|
self.set_text_color(0)
|
|
self.ln(3)
|
|
|
|
|
|
pdf = PaperPDF()
|
|
pdf.set_auto_page_break(auto=True, margin=20)
|
|
|
|
# ── TITLE PAGE ──
|
|
pdf.add_page()
|
|
pdf.ln(25)
|
|
pdf.set_font('Helvetica', 'B', 15)
|
|
pdf.multi_cell(0, 7.5,
|
|
'Emergent Individuality and Neural Integration\n'
|
|
'in Whole-Brain Connectome Simulations\n'
|
|
'of Drosophila melanogaster',
|
|
align='C', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(8)
|
|
|
|
pdf.set_font('Helvetica', '', 10)
|
|
pdf.cell(0, 6, 'Enrique Manuel Rojas Aliaga', align='C', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(2)
|
|
pdf.set_font('Helvetica', 'I', 8.5)
|
|
pdf.cell(0, 5, 'Facultad de Ingenieria y Arquitectura, Universidad de San Martin de Porres, Lima, Peru', align='C', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.set_font('Helvetica', '', 8.5)
|
|
pdf.cell(0, 5, 'enrique_rojas1@usmp.pe', align='C', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(3)
|
|
|
|
pdf.set_font('Helvetica', 'I', 8)
|
|
pdf.cell(0, 5, f'March 2026', align='C', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(10)
|
|
|
|
# Abstract
|
|
pdf.set_font('Helvetica', 'B', 10)
|
|
pdf.cell(0, 6, 'Abstract', new_x="LMARGIN", new_y="NEXT")
|
|
pdf.set_font('Helvetica', '', 8.5)
|
|
abstract = (
|
|
"We present the first embodied whole-brain spiking simulation of Drosophila melanogaster "
|
|
"integrating Hebbian synaptic plasticity and multi-theory neural integration proxy metrics. "
|
|
"Using the complete FlyWire v783 connectome (138,639 neurons, 15,091,983 directed weighted "
|
|
"synapses), we instantiate two identical spiking neural networks as leaky integrate-and-fire "
|
|
"(LIF) neurons on GPU, each driving an independent biomechanical fly body through the "
|
|
"NeuroMechFly v2 framework in MuJoCo physics. Both flies share the same initial connectome "
|
|
"but receive independent sensory input through compound eyes (721 ommatidia per eye), bilateral "
|
|
"olfactory receptor neurons, tarsal gustatory receptors, and Johnston's organ mechanosensory "
|
|
"neurons. Over extended simulation (>100 seconds simulated time across 8 paired sessions), "
|
|
"Hebbian plasticity (eta = 1e-4, alpha = 1e-7) modifies all synaptic weights independently "
|
|
"in each brain. We measure neural integration using proxies derived from four theories: "
|
|
"Integrated Information Theory (Phi), Global Workspace Theory (broadcast), Self-Model Theory "
|
|
"(sensorimotor correlation), and Perturbation Complexity (cascade analysis). Results show that: "
|
|
f"(1) both flies converge to a stable composite integration index attractor "
|
|
f"(CI ~ {grand_mean_0:.2f} vs {grand_mean_1:.2f}, {asymmetry_pct:.0f}% asymmetry); "
|
|
f"(2) behavioral profiles diverge dramatically ({esc0_pct:.0f}% vs {esc1_pct:.0f}% escape behavior); "
|
|
f"(3) {n_divergent:,} synapses ({pct_divergent:.2f}%) develop measurable inter-individual divergence; "
|
|
f"and (4) cross-individual CI correlation is weak (r = {r:.3f}), indicating near-independent "
|
|
"neural dynamics. These findings demonstrate that the connectome architecture of Drosophila, "
|
|
"combined with embodied sensory experience and synaptic plasticity, is sufficient to generate "
|
|
"computational individuality from identical initial conditions. All code and data are publicly "
|
|
"available at https://github.com/erojasoficial-byte/fly-brain."
|
|
)
|
|
pdf.multi_cell(0, 4.2, abstract, new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(3)
|
|
|
|
# Keywords
|
|
pdf.set_font('Helvetica', 'B', 8)
|
|
pdf.cell(18, 5, 'Keywords: ', new_x="RIGHT", new_y="TOP")
|
|
pdf.set_font('Helvetica', '', 8)
|
|
pdf.cell(0, 5, 'connectome, whole-brain simulation, Drosophila, spiking neural network, '
|
|
'Hebbian plasticity, neural integration, individuality, embodied cognition',
|
|
new_x="LMARGIN", new_y="NEXT")
|
|
|
|
# ── INTRODUCTION ──
|
|
pdf.add_page()
|
|
pdf.section_title('1. Introduction')
|
|
|
|
pdf.body_text(
|
|
"The adult Drosophila melanogaster brain contains approximately 139,000 neurons connected "
|
|
"by over 50 million synapses, making it the most complex nervous system for which a complete "
|
|
"synaptic-resolution connectome has been reconstructed (Dorkenwald et al., 2024; Schlegel et al., 2024). "
|
|
"The FlyWire project mapped every neuron and synapse in a single female fly brain using electron "
|
|
"microscopy, providing an unprecedented substrate for computational neuroscience."
|
|
)
|
|
pdf.body_text(
|
|
"Previous computational work has exploited this connectome in two largely separate lines of research. "
|
|
"Shiu et al. (2024) demonstrated that a leaky integrate-and-fire (LIF) model of the central brain "
|
|
"accurately predicts sensorimotor circuit responses, but this model lacks a body and operates in "
|
|
"open-loop stimulation. Independently, the NeuroMechFly v2 framework (Lobato-Rios et al., 2024) "
|
|
"provides a detailed biomechanical fly body in MuJoCo physics with sensory systems, but uses "
|
|
"simplified controllers rather than the full connectome. More recently, FlyGM (2026) embedded the "
|
|
"connectome as a graph neural network for locomotion control via reinforcement learning, but "
|
|
"abandoned biologically realistic spiking dynamics."
|
|
)
|
|
pdf.body_text(
|
|
"A fundamental question remains: can two genetically identical brains, implemented as faithful "
|
|
"spiking models of the same connectome, develop distinct behavioral identities solely through "
|
|
"differences in embodied sensory experience? Biological studies show that clonally raised "
|
|
"Drosophila exhibit stable individual behavioral differences (Honegger & de Bivort, 2020; "
|
|
"Kain et al., 2012), but the computational mechanisms remain debated."
|
|
)
|
|
pdf.body_text(
|
|
"Here we present the first system that unifies these threads: a complete spiking connectome "
|
|
"simulation driving a biomechanical body in closed-loop, with Hebbian synaptic plasticity "
|
|
"enabling experience-dependent weight modification across all 15 million synapses, and "
|
|
"multi-theory neural integration proxy metrics providing continuous readouts. We instantiate "
|
|
"two identical copies of this system and let them accumulate independent experience over "
|
|
"extended simulation periods. Our results demonstrate emergent individuality at behavioral, "
|
|
"synaptic, and neural integration levels."
|
|
)
|
|
|
|
# ── METHODS ──
|
|
pdf.section_title('2. Methods')
|
|
|
|
pdf.subsection_title('2.1 Connectome and Neural Model')
|
|
pdf.body_text(
|
|
"We use the FlyWire v783 connectome (Dorkenwald et al., 2024) comprising 138,639 neurons "
|
|
f"and {n_synapses:,} directed weighted edges after neurotransmitter-based sign assignment. "
|
|
"Each neuron is modeled as a leaky integrate-and-fire (LIF) unit with alpha-function synaptic "
|
|
"currents: membrane time constant tau_m = 10 ms, synaptic time constant tau_s = 5 ms, resting "
|
|
"potential V_rest = -65 mV, threshold V_th = -50 mV, reset potential V_reset = -65 mV, and "
|
|
"refractory period t_ref = 2 ms. Synaptic weights are initialized from connectome edge weights "
|
|
"with sign determined by predicted neurotransmitter identity (excitatory: acetylcholine, "
|
|
"glutamate; inhibitory: GABA, glycine). The entire network runs on GPU via PyTorch sparse "
|
|
"tensor operations at 5 kHz temporal resolution."
|
|
)
|
|
|
|
pdf.subsection_title('2.2 Embodied Simulation')
|
|
pdf.body_text(
|
|
"The neural model drives a biomechanical Drosophila body implemented in NeuroMechFly v2 "
|
|
"(Lobato-Rios et al., 2024) within the MuJoCo physics engine. The body comprises 87 "
|
|
"independently actuated joints across 6 legs, 2 wings, head, and abdomen, reconstructed "
|
|
"from X-ray microtomography of a biological specimen. Sensory systems include: (i) compound "
|
|
"eyes with 721 ommatidia per eye providing visual input to identified photoreceptor neurons; "
|
|
"(ii) bilateral olfactory receptor neurons (ORNs) for chemotaxis; (iii) tarsal gustatory "
|
|
"receptors on all 6 legs for contact chemosensation; and (iv) Johnston's organ mechanosensory "
|
|
"neurons for vibration and proprioceptive feedback. Motor output is decoded from descending "
|
|
"neuron (DN) spike rates into joint torque commands via a biologically informed mapping of "
|
|
"~1,100 identified DNs to leg, wing, and body actuators. All behaviors emerge from connectome "
|
|
"spike propagation; no hardcoded behavioral rules are used."
|
|
)
|
|
|
|
pdf.subsection_title('2.3 Hebbian Synaptic Plasticity')
|
|
pdf.body_text(
|
|
f"All {n_synapses:,} synapses undergo continuous Hebbian modification according to: "
|
|
"dW_ij = eta * (r_i * r_j) - alpha * W_ij, where r_i and r_j are pre- and post-synaptic "
|
|
"firing rates (exponentially filtered spike trains), eta = 1e-4 is the learning rate, and "
|
|
"alpha = 1e-7 is a weight decay term that prevents unbounded growth and introduces a "
|
|
"depression bias. This rule strengthens synapses with correlated pre/post activity and "
|
|
"weakens those without, implementing a simplified form of activity-dependent plasticity "
|
|
"analogous to biological Hebbian learning."
|
|
)
|
|
|
|
pdf.subsection_title('2.4 Neural Integration Proxy Metrics')
|
|
pdf.body_text(
|
|
"We continuously measure four proxy metrics derived from major theories of consciousness, "
|
|
"evaluated every 500 ms of simulated time:"
|
|
)
|
|
pdf.body_text(
|
|
"Phi Proxy (IIT): Mutual information between four functional brain partitions (visual, motor, "
|
|
"olfactory, integrator) computed over a sliding window. Measures information integration across "
|
|
"brain modules (Tononi et al., 2016)."
|
|
)
|
|
pdf.body_text(
|
|
"Global Broadcast (GWT): Fraction of brain partitions receiving signals from high-fan-out "
|
|
"hub neurons (>100 connections). Captures the broadcast aspect of Global Workspace Theory "
|
|
"(Baars, 1988; Dehaene & Naccache, 2001)."
|
|
)
|
|
pdf.body_text(
|
|
"Self-Model: Lagged Pearson correlation between proprioceptive input and motor output, "
|
|
"capturing sensorimotor prediction quality as a proxy for body-model integration (Metzinger, 2003)."
|
|
)
|
|
pdf.body_text(
|
|
"Perturbation Complexity: Random spike injection into 10 neurons every 5 seconds, measuring "
|
|
"cascade spread across partitions. Computed as spatial reach times temporal entropy of the "
|
|
"perturbation response (Koch et al., 2016)."
|
|
)
|
|
pdf.body_text(
|
|
"The composite Consciousness Index (CI) is: CI = 0.3 * Phi + 0.3 * Broadcast + 0.2 * Self "
|
|
"+ 0.2 * Complexity."
|
|
)
|
|
|
|
pdf.subsection_title('2.5 Two-Fly Experimental Protocol')
|
|
pdf.body_text(
|
|
"Two instances of the complete system are initialized with identical connectome weights and "
|
|
"placed in an arena with natural visual features, olfactory sources, gustatory zones, and "
|
|
"occasional looming threats. Despite sharing the arena, each fly receives independent sensory "
|
|
"input determined by its own position, orientation, and movement history. Integration metrics "
|
|
"and plastic weights are logged independently. The simulation runs at 5 kHz neural resolution "
|
|
"with 0.2 ms physics timesteps. We conducted 8 paired sessions over 24 hours, accumulating "
|
|
">100 seconds of simulated time per fly (~7 hours wall-clock per session on an Intel Core Ultra 9 285HX "
|
|
"CPU with NVIDIA RTX 5090 Laptop GPU, 24 GB VRAM, 64 GB DDR5 RAM)."
|
|
)
|
|
|
|
# ── RESULTS ──
|
|
pdf.add_page()
|
|
pdf.section_title('3. Results')
|
|
|
|
pdf.add_figure(OUT / "fig1_architecture.png",
|
|
"Figure 1. System architecture. The closed-loop pipeline propagates sensory input through the "
|
|
f"complete FlyWire connectome (138,639 LIF neurons, {n_synapses:,} synapses), decodes "
|
|
"descending neuron activity into motor commands, and feeds resulting sensory changes back into "
|
|
"the brain. Hebbian plasticity modifies all synapses continuously. Neural integration proxies "
|
|
"are computed from network dynamics. Two independent instances run simultaneously.")
|
|
|
|
pdf.add_figure(OUT / "fig9_behavior.png",
|
|
"Figure 2. Embodied fly behavior. Top: simulation screenshots showing emergent walking, "
|
|
"exploration, and escape responses driven entirely by connectome spike propagation. "
|
|
"Bottom: tripod gait from side and front views, and compound eye visual input "
|
|
"(721 ommatidia per eye).", width=170)
|
|
|
|
pdf.add_page()
|
|
pdf.add_figure(OUT / "fig10_eyes.png",
|
|
"Figure 3. Compound eye visual input at two time points. Each eye has 721 ommatidia in a "
|
|
"hexagonal lattice. Left/right images are processed independently and mapped onto identified "
|
|
"photoreceptor neurons. Changes between t=0 and t=20s reflect locomotion and head movement.", width=170)
|
|
|
|
pdf.subsection_title('3.1 Neural Integration Converges to a Stable Attractor')
|
|
pdf.body_text(
|
|
"Both flies exhibit an initial warm-up phase (0-5 s) where all metrics rise from zero as "
|
|
"activity propagates through the connectome. After stabilization, CI converges to a "
|
|
f"characteristic attractor: Fly 0 at {ov_fly0['CI'].mean():.3f} +/- {ov_fly0['CI'].std():.3f} "
|
|
f"and Fly 1 at {ov_fly1['CI'].mean():.3f} +/- {ov_fly1['CI'].std():.3f} (overnight session, "
|
|
f"n = {len(ov_fly0):,} measurements each). This {asymmetry_pct:.0f}% asymmetry persists across "
|
|
f"all 8 paired sessions (grand mean: {grand_mean_0:.3f} vs {grand_mean_1:.3f}), suggesting it "
|
|
"reflects a stable divergence in neural integration rather than transient fluctuation."
|
|
)
|
|
pdf.body_text(
|
|
f"The dominant contributor is Global Broadcast (Fly 0: {bcast0_mean:.3f}, Fly 1: {bcast1_mean:.3f}), "
|
|
"indicating that the connectome's hub architecture naturally supports widespread information "
|
|
f"distribution. Phi is lower (Fly 0: {phi0_mean:.4f}, Fly 1: {phi1_mean:.4f}), suggesting that "
|
|
"while information is broadcast broadly, irreducible integration between partitions is modest."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig2_ci_timelines.png",
|
|
"Figure 4. Consciousness Index (CI) timelines during the overnight session. "
|
|
"Colored backgrounds indicate behavioral mode (orange = escape, purple = walking, "
|
|
"green = grooming). Smoothed traces overlaid on raw data. Dashed lines: session means. "
|
|
"Peaks and troughs occur at independent times for each fly.")
|
|
|
|
pdf.subsection_title('3.2 Behavioral Modes Modulate Integration')
|
|
pdf.body_text(
|
|
"Behavioral mode significantly affects CI (Fig. 5). Escape behavior produces the highest CI "
|
|
f"(Fly 0: {ov_fly0[ov_fly0['mode']=='escape']['CI'].mean():.3f}, "
|
|
f"Fly 1: {ov_fly1[ov_fly1['mode']=='escape']['CI'].mean():.3f}), likely reflecting Giant Fiber "
|
|
"circuit activation driving rapid multi-modal integration. In earlier single-fly sessions that "
|
|
"included flight behavior, CI reached peak values of 0.46-0.57, representing the highest "
|
|
"integration state observed, consistent with the coordinative demands of flight."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig3_ci_by_mode.png",
|
|
"Figure 5. Violin plots of CI distribution by behavioral mode. Escape behavior produces the "
|
|
"highest CI. Sample sizes (n) shown below each violin.")
|
|
|
|
pdf.add_page()
|
|
pdf.subsection_title('3.3 Component Analysis Reveals Divergent Integration Profiles')
|
|
pdf.body_text(
|
|
"Decomposing CI into its four components (Fig. 6) reveals that the inter-individual difference "
|
|
"is not uniform. Global Broadcast is the most stable component, maintaining near-maximum values "
|
|
"(>0.55) after warm-up, consistent with the connectome's intrinsic hub architecture. "
|
|
f"Phi shows the largest relative divergence: {phi0_mean/phi1_mean:.1f}x higher in Fly 0, "
|
|
"suggesting stronger functional coupling driven by its sensory experience. Perturbation "
|
|
"Complexity shows characteristic bistable oscillations. Self-Model remains near zero for both "
|
|
"flies (<0.002), indicating minimal sensorimotor prediction at this simulation timescale."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig4_components.png",
|
|
"Figure 6. Time series of all four integration proxy components (smoothed, window = 30). "
|
|
"From top: Phi (IIT), Global Broadcast (GWT), Perturbation Complexity, Self-Model. "
|
|
"Broadcast dominates the CI signal; Self-Model contributes minimally.")
|
|
|
|
pdf.subsection_title('3.4 Cross-Session Stability')
|
|
pdf.body_text(
|
|
"Tracking CI across all sessions (Fig. 7) reveals two phases. Early single-fly sessions show "
|
|
"higher CI (0.39, 0.33) due to frequent flight behavior. When the two-fly protocol begins, "
|
|
f"CI stabilizes at {grand_mean_0:.2f} (Fly 0) and {grand_mean_1:.2f} (Fly 1), remaining "
|
|
"remarkably stable across 8 paired sessions spanning >24 hours of wall-clock time. This "
|
|
"stability suggests the CI attractor is a robust property of the connectome's dynamics."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig5_evolution.png",
|
|
"Figure 7. Mean CI (+/- s.d.) across all sessions. Vertical line separates single-fly from "
|
|
"two-fly sessions. Asterisk marks the overnight (primary) session. The CI asymmetry is "
|
|
"stable across all paired sessions.")
|
|
|
|
# ── Plasticity ──
|
|
pdf.add_page()
|
|
pdf.subsection_title('3.5 Hebbian Plasticity Produces Micro-Divergence')
|
|
pdf.body_text(
|
|
f"After extended simulation, Hebbian plasticity modified all {n_synapses:,} synapses in both "
|
|
f"flies (Fig. 8). The modification shows a depression bias: {100*dep/n_synapses:.1f}% of "
|
|
f"synapses were weakened while {100*pot/n_synapses:.1f}% were strengthened. Despite near-unity "
|
|
f"global weight correlation (r > 0.99999), {n_divergent:,} synapses ({pct_divergent:.2f}%) show "
|
|
f"measurable inter-individual divergence, with maximum |W0 - W1| = {max_div:.2e}. The "
|
|
"divergence distribution is log-normal, concentrated in the 1e-6 to 1e-5 range. All divergent "
|
|
"synapses changed in the same direction with magnitude ratios near 1.0 (mean: 1.0000, "
|
|
"std: 0.0008), indicating qualitatively similar but quantitatively distinct plasticity patterns."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig6_plasticity.png",
|
|
f"Figure 8. Hebbian plasticity analysis. (A) Distribution of weight changes from baseline. "
|
|
f"(B) Log-scaled divergence histogram ({n_divergent:,} synapses with |W0-W1| > 0). "
|
|
f"(C) Plasticity direction: {100*dep/n_synapses:.0f}% depression, {100*pot/n_synapses:.0f}% "
|
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f"potentiation. (D) Top 20 most divergent synapses (max = {max_div:.2e}).")
|
|
|
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# ── Cross-correlation ──
|
|
pdf.subsection_title('3.6 Independent Dynamics from Identical Connectomes')
|
|
pdf.body_text(
|
|
f"Despite identical initial connectomes, the two flies develop strikingly independent neural "
|
|
f"dynamics (Fig. 9A). The Pearson correlation between simultaneous CI measurements is "
|
|
f"r = {r:.3f} (p < 0.001, n = {min_len:,}), indicating weak coupling. This arises because "
|
|
"each fly's CI is determined by its own sensory input, motor state, and accumulated plasticity, "
|
|
"all of which diverge rapidly after independent embodied experience begins."
|
|
)
|
|
pdf.body_text(
|
|
f"Behavioral profiles diverge dramatically (Fig. 9C). Fly 0 spends {esc0_pct:.1f}% of time "
|
|
f"in escape versus {esc1_pct:.1f}% for Fly 1, while Fly 1 spends {grm1_pct:.1f}% grooming "
|
|
f"versus {grm0_pct:.1f}% for Fly 0. These stable behavioral differences emerge from the "
|
|
"interaction between initial position, visual input, and the feedback loop between neural "
|
|
"dynamics, motor output, and sensory consequences."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig7_crosscorr.png",
|
|
f"Figure 9. Inter-individual analysis. (A) Scatter plot of simultaneous CI values (r = {r:.3f}). "
|
|
"(B) Temporal sensitization by quartile. (C) Behavioral profile divergence. "
|
|
"(D) CI difference over time (blue = Fly 0 higher, red = Fly 1 higher).")
|
|
|
|
# ── Comparison ──
|
|
pdf.add_page()
|
|
pdf.subsection_title('3.7 Comparison with Prior Work')
|
|
pdf.body_text(
|
|
"Table 1 summarizes capabilities of existing Drosophila whole-brain simulation projects. "
|
|
"To our knowledge, no prior work combines all of: spiking neural dynamics on the complete "
|
|
"FlyWire connectome, embodied closed-loop sensorimotor interaction, Hebbian synaptic "
|
|
"plasticity, neural integration metrics, and multi-individual experimental design."
|
|
)
|
|
|
|
pdf.add_figure(OUT / "fig8_comparison.png",
|
|
"Table 1. Feature comparison with existing Drosophila whole-brain simulation projects.")
|
|
|
|
# ── DISCUSSION ──
|
|
pdf.section_title('4. Discussion')
|
|
|
|
pdf.subsection_title('4.1 The Connectome as an Integration-Supporting Architecture')
|
|
pdf.body_text(
|
|
"Our results demonstrate that the biological connectome of Drosophila, when faithfully "
|
|
"implemented as a spiking neural network, spontaneously generates neural integration patterns "
|
|
"that satisfy multiple proxy criteria associated with major theories. The Global Broadcast "
|
|
"metric reaches near-maximum values (~0.6), indicating that the connectome's hub architecture "
|
|
"naturally supports wide information distribution, a key prediction of Global Workspace Theory. "
|
|
"The moderate Phi values suggest genuine but limited integration between functional partitions."
|
|
)
|
|
pdf.body_text(
|
|
"We emphasize that these proxy measurements do not constitute evidence of subjective "
|
|
"experience or phenomenal consciousness. They measure computational properties (integration, "
|
|
"broadcast, complexity) that theories identify as necessary conditions, but whether they are "
|
|
"sufficient remains an open question."
|
|
)
|
|
|
|
pdf.subsection_title('4.2 Emergent Individuality Without Genetic Variation')
|
|
pdf.body_text(
|
|
"Our most striking finding is the rapid emergence of behavioral individuality from identical "
|
|
f"initial conditions. Within 100 seconds of simulated experience, two flies with the same "
|
|
f"connectome develop: (i) different behavioral profiles ({esc0_pct:.0f}% vs {esc1_pct:.0f}% escape); "
|
|
f"(ii) different integration signatures (CI {grand_mean_0:.3f} vs {grand_mean_1:.3f}); "
|
|
f"(iii) {n_divergent:,} divergent synapses; and (iv) near-independent dynamics (r = {r:.3f}). "
|
|
"This parallels biological observations that isogenic Drosophila develop stable behavioral "
|
|
"differences (Honegger & de Bivort, 2020), suggesting that the connectome's sensitivity to "
|
|
"sensory input, amplified by Hebbian plasticity, is sufficient to explain individuality "
|
|
"without invoking genetic variation or developmental stochasticity."
|
|
)
|
|
|
|
pdf.subsection_title('4.3 Plasticity: Conservative but Consequential')
|
|
pdf.body_text(
|
|
"The Hebbian plasticity is deliberately conservative (eta = 1e-4), producing weight changes "
|
|
"of at most 0.81% relative to baseline. Yet these microscopic modifications produce measurably "
|
|
f"different behavioral profiles. The {100*dep/n_synapses:.0f}/{100*pot/n_synapses:.0f} "
|
|
"depression/potentiation bias reflects synaptic homeostasis: only synapses with sustained "
|
|
"correlated activity resist default weakening, creating Darwinian selection at the synaptic "
|
|
"level. With longer simulation or higher learning rates, these micro-divergences would amplify."
|
|
)
|
|
|
|
pdf.subsection_title('4.4 Limitations')
|
|
pdf.body_text(
|
|
"Several limitations should be noted. (1) Our LIF neuron model lacks dendritic computation, "
|
|
"neuromodulation, and gap junctions. (2) The Hebbian rule is a simplification; biological "
|
|
"Drosophila employ multiple plasticity forms with complex timing dependencies. (3) Integration "
|
|
"proxies are approximations (e.g., Phi on four coarse partitions rather than full IIT 4.0 "
|
|
"decomposition). (4) The simulation timescale (100 s) is short relative to biological "
|
|
"individuality development. (5) The DN-to-motor mapping involves engineering choices that "
|
|
"may not perfectly reflect biological motor control. (6) The hardware used (consumer-grade "
|
|
"GPU with 8 GB VRAM) limits the temporal resolution of plasticity updates and the duration "
|
|
"of continuous simulation runs."
|
|
)
|
|
|
|
# ── CONCLUSION ──
|
|
pdf.section_title('5. Conclusion')
|
|
pdf.body_text(
|
|
"We have demonstrated that the complete FlyWire connectome, implemented as a spiking neural "
|
|
"network driving a biomechanical body with Hebbian plasticity, spontaneously generates: "
|
|
"(1) stable neural integration patterns measurable by multi-theory proxy metrics; "
|
|
"(2) behavioral individuality from identical initial conditions; and (3) experience-dependent "
|
|
"synaptic divergence. This is, to our knowledge, the first embodied whole-brain simulation to "
|
|
"integrate all of these capabilities. The system provides a platform for studying how "
|
|
"connectome architecture constrains and enables neural computation, behavioral diversity, and "
|
|
"information integration at whole-brain scale."
|
|
)
|
|
|
|
pdf.body_text(
|
|
"Code availability: The complete source code, simulation framework, and analysis scripts are "
|
|
"publicly available at https://github.com/erojasoficial-byte/fly-brain under the MIT license. "
|
|
"The FlyWire connectome data is available through the FlyWire project (https://flywire.ai)."
|
|
)
|
|
|
|
# ── ACKNOWLEDGMENTS ──
|
|
pdf.section_title('Acknowledgments')
|
|
pdf.body_text(
|
|
"The author thanks the FlyWire Consortium (Dorkenwald et al., 2024; Schlegel et al., 2024) "
|
|
"for making the complete Drosophila melanogaster connectome publicly available under open "
|
|
"access terms. The NeuroMechFly v2 biomechanical model (Lobato-Rios et al., 2024) and the "
|
|
"MuJoCo physics engine (DeepMind) provided the embodied simulation framework. This work "
|
|
"was conducted independently and received no external funding. Computations were performed "
|
|
"on consumer hardware (Intel Core Ultra 9 285HX, NVIDIA RTX 5090 Laptop GPU, 64 GB DDR5 RAM)."
|
|
)
|
|
|
|
# ── REFERENCES ──
|
|
pdf.add_page()
|
|
pdf.section_title('References')
|
|
pdf.set_font('Helvetica', '', 7.5)
|
|
refs = [
|
|
"Baars, B.J. (1988). A cognitive theory of consciousness. Cambridge University Press.",
|
|
"Buchanan, S.M., Kain, J.S., & de Bivort, B.L. (2015). Neuronal control of locomotor handedness in Drosophila. PNAS, 112(21), 6700-6705.",
|
|
"Dehaene, S. & Naccache, L. (2001). Towards a cognitive neuroscience of consciousness. Cognition, 79(1-2), 1-37.",
|
|
"Dorkenwald, S. et al. (2024). Neuronal wiring diagram of an adult brain. Nature, 634, 124-138.",
|
|
"FlyGM (2026). Whole-Brain Connectomic Graph Model Enables Whole-Body Locomotion Control in Fruit Fly. arXiv:2602.17997.",
|
|
"Honegger, K.S. & de Bivort, B.L. (2020). A neurodevelopmental origin of behavioral individuality in the Drosophila visual system. Science, 367(6482).",
|
|
"Kain, J.S., Stokes, C., & de Bivort, B.L. (2012). Phototactic personality in fruit flies and its suppression by serotonin and white. PNAS, 109(48), 19834-19839.",
|
|
"Koch, C. et al. (2016). Neural correlates of consciousness: progress and problems. Nature Reviews Neuroscience, 17(5), 307-321.",
|
|
"Leung, C. et al. (2021). Integrated information structure collapses with anesthetic loss of conscious arousal in Drosophila melanogaster. PLOS Computational Biology, 17(2), e1008722.",
|
|
"Lobato-Rios, V. et al. (2024). NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila. Nature Methods, 21(12), 2353-2362.",
|
|
"Metzinger, T. (2003). Being No One: The Self-Model Theory of Subjectivity. MIT Press.",
|
|
"Schlegel, P. et al. (2024). Whole-brain annotation and multi-connectome cell typing of Drosophila. Nature, 634, 139-152.",
|
|
"Shiu, P.K. et al. (2024). A Drosophila computational brain model reveals sensorimotor processing. Nature, 634, 210-219.",
|
|
"Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450-461.",
|
|
]
|
|
|
|
for ref in refs:
|
|
pdf.multi_cell(0, 3.5, ref, new_x="LMARGIN", new_y="NEXT")
|
|
pdf.ln(1.5)
|
|
|
|
# ── SUPPLEMENTARY INFORMATION ──
|
|
pdf.add_page()
|
|
pdf.section_title('Supplementary Information')
|
|
|
|
pdf.subsection_title('S1. Hardware and Software')
|
|
pdf.body_text(
|
|
"All simulations were performed on a laptop computer with the following specifications: "
|
|
"Intel Core Ultra 9 285HX (24 cores), 64 GB DDR5 5600 MHz RAM, NVIDIA GeForce RTX 5090 Laptop "
|
|
"GPU (24 GB GDDR7 VRAM). Software: Python 3.10, PyTorch 2.5.1 (CUDA 12.8), MuJoCo 3.2.7, "
|
|
"flygym (NeuroMechFly v2). Operating system: Windows 11 Pro. Each paired simulation session "
|
|
"runs for approximately 7 hours wall-clock time to produce ~100 seconds of simulated time "
|
|
"at 5 kHz neural resolution."
|
|
)
|
|
|
|
pdf.subsection_title('S2. Session Summary')
|
|
pdf.body_text(
|
|
"A total of 20 consciousness measurement sessions were conducted over a 24-hour period "
|
|
"(March 11-12, 2026), comprising 2 single-fly and 8 paired two-fly sessions. The overnight "
|
|
"session (session_20260311_233655, marked with * in Fig. 7) was selected as the primary "
|
|
"dataset due to its length (2,086 measurement points per fly, ~104.3 s simulated time). "
|
|
"Results were replicated across all subsequent sessions."
|
|
)
|
|
|
|
pdf.subsection_title('S3. Consciousness Index Formula')
|
|
pdf.body_text(
|
|
"CI = 0.3 * Phi_norm + 0.3 * Broadcast_norm + 0.2 * SelfModel_norm + 0.2 * Complexity_norm\n\n"
|
|
"Where each component is computed as follows:\n"
|
|
"- Phi_norm: Normalized mutual information between 4 brain partitions (visual, motor, olfactory, integrator)\n"
|
|
"- Broadcast_norm: Fraction of partitions receiving hub neuron signals, divided by total partitions\n"
|
|
"- SelfModel_norm: abs(Pearson r) between proprioceptive input and motor output over 10-step lag\n"
|
|
"- Complexity_norm: (spatial_reach / n_partitions) * temporal_entropy, from perturbation cascade"
|
|
)
|
|
|
|
# ── Save ──
|
|
pdf_path = BASE / "paper_emergent_individuality.pdf"
|
|
pdf.output(str(pdf_path))
|
|
|
|
file_size = os.path.getsize(pdf_path)
|
|
print(f"\n{'=' * 70}")
|
|
print(f" PDF SAVED: {pdf_path}")
|
|
print(f" Size: {file_size / 1024 / 1024:.1f} MB, Pages: {pdf.page_no()}")
|
|
print(f" Figures: {OUT}")
|
|
print(f"{'=' * 70}")
|