mirror of
https://github.com/rndlabsoy/fly-brain-full.git
synced 2026-09-10 11:20:43 +02:00
Enrique Manuel Rojas Aliaga
27cec28d5d
Replace bioRxiv placeholder DOI with published Zenodo preprint. Update citation badge, paper reference, and BibTeX entry.
394 lines
17 KiB
Markdown
394 lines
17 KiB
Markdown
# Emergent Individuality in Whole-Brain Connectome Simulations of *Drosophila melanogaster*
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[](https://www.python.org/downloads/)
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[](LICENSE)
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[](https://developer.nvidia.com/cuda-toolkit)
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[](https://neuromechfly.org/)
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[](https://flywire.ai/)
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[](https://doi.org/10.5281/zenodo.19152238)
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> **Enrique Manuel Rojas Aliaga** · Facultad de Ingenieria y Arquitectura, Universidad de San Martin de Porres · Lima, Peru · enrique_rojas1@usmp.pe
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<div align="center">
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<img src="demo_preview.gif" alt="Embodied Drosophila simulation" width="640">
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<br>
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<em>The fly sees, walks, grooms, and escapes — driven entirely by 138,639 spiking neurons from the FlyWire connectome.</em>
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<br>
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<a href="demo.mp4"><strong>Watch full demo video</strong></a>
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</div>
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---
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## Paper
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This repository accompanies the preprint:
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> **Rojas Aliaga, E. M.** (2026). *Emergent Individuality and Neural Integration in Whole-Brain Connectome Simulations of Drosophila melanogaster.* Zenodo. doi: [10.5281/zenodo.19152238](https://doi.org/10.5281/zenodo.19152238)
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Pre-built PDFs are included in this repository:
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| Document | File | Pages |
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| **English** | [`paper_emergent_individuality.pdf`](paper_emergent_individuality.pdf) | 14 |
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| **Spanish** | [`paper_individualidad_emergente_ES.pdf`](paper_individualidad_emergente_ES.pdf) | 14 |
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Both papers contain 10 publication-quality figures (300 DPI) generated from the included experimental data.
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---
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## Abstract
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We present the first embodied whole-brain spiking simulation of *Drosophila melanogaster* that combines the complete FlyWire v783 connectome (138,639 neurons; 15,091,983 directed weighted synapses) with biomechanical embodiment, multi-modal sensory processing, Hebbian synaptic plasticity, and multi-theory neural integration metrics. Two flies initialized with identical connectomes develop distinct behavioral profiles (81% vs. 47% escape), different neural integration signatures (CI = 0.221 vs. 0.198), and 76,034 divergent synapses after 24 hours of independent embodied experience — demonstrating that connectome architecture + embodied experience + Hebbian plasticity is sufficient to generate computational individuality from identical initial conditions.
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---
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ BRAIN (GPU / PyTorch) │
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│ 138,639 LIF neurons · 15M synapses · 5 kHz timestep │
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│ │
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│ Visual ─── T1→T2→T3→T4→T5 ─── LC4 ─── GF ──→ DNs │
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│ cortex (motion detection) (loom) (escape) │
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│ │
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│ Olfactory ─── ORN→PN→KC→MBON ─── DN turn commands │
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│ Gustatory ─── GRN→SEZ→MN ─── feeding/avoidance │
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│ Somatosensory ─── mechanoreceptors→IN→MN │
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│ │
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│ Hebbian Plasticity: dW = eta*(r_i*r_j) - alpha*W │
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└──────────────────────┬──────────────────────────────────────┘
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│ DN spike rates (~1,100 descending neurons)
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┌────────▼────────┐
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│ Brain-Body │
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│ Bridge │
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│ DN→drive rates │
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│ mode selection │
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│ (walk/escape/ │
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│ groom/feed/ │
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│ flight) │
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└────────┬────────┘
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│ joint torques
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┌──────────────────────▼──────────────────────────────────────┐
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│ BODY (NeuroMechFly v2 / MuJoCo) │
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│ 87 joints · 6 legs · 2 wings · head · abdomen │
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│ Compound eyes (721 ommatidia) · Contact sensors │
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│ Arena: terrain, sky, sunlight, odors, threats │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## Quick Start
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### 1. Clone
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```bash
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git clone https://github.com/erojasoficial-byte/fly-brain.git
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cd fly-brain
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git lfs pull # downloads large data files (~270 MB)
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```
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### 2. Install Dependencies
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```bash
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# PyTorch with CUDA 12.1 (adjust for your CUDA version)
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pip install torch --index-url https://download.pytorch.org/whl/cu121
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# Core dependencies
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pip install flygym mujoco numpy scipy pandas matplotlib fpdf2 pygame pyarrow
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```
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Or use conda:
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```bash
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conda env create -f environment.yml
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conda activate brain-fly
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pip install flygym mujoco fpdf2 pygame
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```
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### 3. Run
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```bash
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# Single fly — visual mode with all sensory systems
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python fly_embodied.py --visual --monitor --flight --olfactory --gustatory --somatosensory
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# Two-fly experiment with neural integration metrics
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python two_flies.py --visual
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# Headless mode (faster, no display)
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python fly_embodied.py --steps 10000
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python two_flies.py --steps 500000
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```
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---
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## Reproducing the Paper
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All figures and statistics in the paper are generated programmatically from the experimental data included in this repository. No manual editing or cherry-picking was performed.
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### Step 1: Verify Data Integrity
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The repository includes all raw data from 20 measurement sessions (8 paired two-fly sessions + 3 single-fly baseline sessions + 1 early single-fly session):
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```bash
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# Check consciousness session data
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ls consciousness_history/
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# Expected: 20 session directories (3 single + 8 paired × 2 flies + 1 early)
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# Check plasticity weight snapshots
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ls data/plastic_weights*.pt
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# Expected: 3 files (~60 MB each)
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```
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### Step 2: Regenerate All Figures and the Paper
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```bash
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# Generate English paper with 10 figures (300 DPI)
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python generate_paper.py
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# Output: paper_emergent_individuality.pdf + paper_figures/*.png
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# Generate Spanish paper (reuses same figures)
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python generate_paper_es.py
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# Output: paper_individualidad_emergente_ES.pdf
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```
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This regenerates every figure from raw data:
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| Figure | Script section | Source data |
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| Fig. 1 — Architecture | `generate_paper.py` | Schematic (programmatic) |
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| Fig. 2 — CI timelines | `generate_paper.py` | `consciousness_history/session_*_fly{0,1}/` |
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| Fig. 3 — CI by behavioral mode | `generate_paper.py` | Same sessions |
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| Fig. 4 — Integration components | `generate_paper.py` | Same sessions |
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| Fig. 5 — Cross-session evolution | `generate_paper.py` | All 8 paired sessions |
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| Fig. 6 — CI distributions | `generate_paper.py` | Same sessions |
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| Fig. 7 — Behavior montage | `generate_paper.py` | Rendered frames |
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| Fig. 8 — Plasticity analysis | `generate_paper.py` | `data/plastic_weights_{fly0,fly1}.pt` |
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| Fig. 9 — Cross-correlation | `generate_paper.py` | Paired session data |
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| Fig. 10 — Compound eyes | `generate_paper.py` | `data/eye_{L,R}_{0,20}.png` |
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### Step 3: Run Your Own Experiment
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```bash
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# Run a new two-fly experiment (headless, ~30 min for 100k steps)
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python two_flies.py --steps 100000
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# Results saved to consciousness_history/session_YYYYMMDD_HHMMSS_fly{0,1}/
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# Plasticity weights saved to data/plastic_weights_fly{0,1}.pt
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```
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### Step 4: Independent Analysis Scripts
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```bash
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# Analyze plasticity divergence between two flies
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python analyze_plasticity_divergence.py
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# Compare overnight plasticity evolution
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python analyze_overnight.py
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# Direct weight comparison
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python compare_plasticity.py
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```
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---
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## Project Structure
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```
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fly-brain/
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├── paper_emergent_individuality.pdf # Paper (English, 14 pages)
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├── paper_individualidad_emergente_ES.pdf # Paper (Spanish, 14 pages)
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├── paper_figures/ # 10 publication figures (300 DPI)
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│
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├── fly_embodied.py # Main closed-loop brain-body simulation (1,035 lines)
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├── brain_body_bridge.py # DN decoding, motor commands, behavior ( 747 lines)
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├── consciousness.py # Multi-theory neural integration metrics ( 957 lines)
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├── two_flies.py # Two-fly experiment + plasticity tracking (1,140 lines)
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├── code/run_pytorch.py # GPU LIF neuron simulation engine ( 514 lines)
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│
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├── visual_system.py # Compound eye → motion detection pipeline ( 422 lines)
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├── olfactory.py # Bilateral ORN chemotaxis ( 335 lines)
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├── gustatory.py # Tarsal taste detection (sugar/bitter) ( 197 lines)
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├── somatosensory.py # Touch, vibration, Johnston's organ ( 349 lines)
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│
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├── brain_monitor.py # Real-time neural activity visualization (1,253 lines)
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├── looming_arena.py # Natural environment with threats ( 255 lines)
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├── procedural_arena.py # Procedural world generation (Minecraft-like)( 410 lines)
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├── flight.py # Virtual flight state machine ( 200 lines)
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├── vocalization.py # Wing courtship song generation ( 191 lines)
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├── fly_alive.py # Autonomous neural-driven demo ( 260 lines)
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│
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├── generate_paper.py # Paper + figure generation (English) (1,333 lines)
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├── generate_paper_es.py # Paper + figure generation (Spanish) ( 674 lines)
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├── analyze_plasticity_divergence.py # Plasticity divergence analysis ( 778 lines)
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├── analyze_overnight.py # Overnight session analysis ( 251 lines)
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├── compare_plasticity.py # Direct weight comparison tool ( 254 lines)
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│
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├── data/
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│ ├── 2025_Completeness_783.csv # FlyWire v783 neuron table (138,639 neurons)
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│ ├── 2025_Connectivity_783.parquet # Synaptic connectivity (15,091,983 edges)
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│ ├── flywire_annotations.tsv # Neuron type annotations
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│ ├── sez_neurons.pickle # SEZ neuron IDs (for gustation)
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│ ├── plastic_weights.pt # Baseline plasticity snapshot
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│ ├── plastic_weights_fly0.pt # Fly 0 final weights (after 24h)
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│ ├── plastic_weights_fly1.pt # Fly 1 final weights (after 24h)
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│ ├── eye_{L,R}_{0,20}.png # Compound eye renders
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│ └── benchmark-results.csv # GPU benchmark data
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│
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├── consciousness_history/ # 20 experimental sessions
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│ ├── session_20260311_132935/ # Single-fly baseline sessions
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│ ├── session_20260311_133411/
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│ ├── session_20260311_134345/
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│ ├── session_20260311_210436/ # Extended single-fly session
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│ ├── session_20260311_225504_fly0/ # ┐ Paired session 1
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│ ├── session_20260311_225556_fly1/ # ┘
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│ ├── session_20260311_230255_fly0/ # ┐ Paired session 2
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│ ├── session_20260311_230347_fly1/ # ┘
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│ ├── session_20260311_230853_fly0/ # ┐ Paired session 3
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│ ├── session_20260311_230945_fly1/ # ┘
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│ ├── session_20260311_233655_fly0/ # ┐ Paired session 4
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│ ├── session_20260311_233746_fly1/ # ┘
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│ ├── session_20260312_071403_fly0/ # ┐ Paired session 5
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│ ├── session_20260312_071508_fly1/ # ┘
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│ ├── session_20260312_074258_fly0/ # ┐ Paired session 6
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│ ├── session_20260312_074353_fly1/ # ┘
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│ ├── session_20260312_083414_fly0/ # ┐ Paired session 7
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│ ├── session_20260312_083508_fly1/ # ┘
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│ ├── session_20260312_094510_fly0/ # ┐ Paired session 8
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│ └── session_20260312_094601_fly1/ # ┘
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│
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├── code/ # GPU simulation backends
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│ ├── run_pytorch.py # Primary: PyTorch sparse LIF engine
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│ ├── run_brian2_cuda.py # Alternative: Brian2CUDA backend
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│ ├── run_nestgpu.py # Alternative: NEST GPU backend
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│ └── benchmark.py # Backend comparison tool
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│
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├── scripts/
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│ └── setup_WSL_CUDA.sh # WSL2 + CUDA setup guide
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│
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├── docs/index.html # Project webpage
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├── environment.yml # Conda environment specification
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├── LICENSE # MIT License
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└── demo.mp4 / demo_preview.gif # Demo video
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```
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**Total**: ~11,500 lines of Python across 20 modules.
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---
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## Neural Model
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The brain implements a **Leaky Integrate-and-Fire (LIF)** network from the [FlyWire connectome](https://flywire.ai/) v783 (Dorkenwald et al., 2024):
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| Parameter | Value |
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| Neurons | 138,639 |
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| Synapses | 15,091,983 (directed, weighted) |
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| Timestep | 0.2 ms (5 kHz) |
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| Membrane time constant (tau_m) | 10 ms |
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| Synaptic time constant (tau_s) | 5 ms |
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| Resting potential (V_rest) | -65 mV |
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| Threshold (V_th) | -50 mV |
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| Reset potential (V_reset) | -65 mV |
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| Refractory period (t_ref) | 2 ms |
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Neurotransmitter identity determines synapse sign:
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- **Excitatory**: acetylcholine, glutamate
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- **Inhibitory**: GABA, glycine
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### Hebbian Plasticity
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All 15,091,983 synapses undergo continuous modification:
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```
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dW_ij = eta * (r_i * r_j) - alpha * W_ij
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```
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- `eta = 1e-4` — learning rate
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- `alpha = 1e-7` — weight decay (homeostatic depression bias)
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- Operates on GPU alongside neural dynamics (no additional memory overhead)
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## Sensory Systems
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| Modality | Neurons | Pathway | Reference |
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| **Vision** | 721 ommatidia/eye | T1→T2→T3→T4/T5→LC4→GF→DN | Reichardt-like motion detection |
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| **Olfaction** | ~2,600 ORNs | ORN→PN→KC→MBON→DN | Bilateral chemotaxis |
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| **Gustation** | ~200 GRNs | GRN→SEZ→MN | Tarsal sugar/bitter |
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| **Mechanosensation** | JO + leg sensors | Mechanoreceptor→IN→MN | Vibration + proprioception |
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## Neural Integration Metrics
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Four proxy metrics computed every 500 ms (see paper Section 2.5 for mathematical definitions):
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| Metric | Theory | Range | Mean value |
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| Phi | Integrated Information Theory (Tononi) | [0, 1] | ~0.15 |
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| Broadcast | Global Workspace Theory (Baars, Dehaene) | [0, 1] | ~0.60 |
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| Self-Model | Self-Model Theory (Metzinger) | [0, 1] | ~0.04 |
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| Complexity | Perturbation Complexity (Koch) | [0, 1] | ~0.08 |
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**Composite Index**: CI = 0.3 × Phi + 0.3 × Broadcast + 0.2 × Self-Model + 0.2 × Complexity
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---
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## Key Results
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From 8 paired two-fly sessions over 24 hours of simulated time:
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| Metric | Fly 0 | Fly 1 | Interpretation |
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| Mean CI | 0.221 | 0.198 | 12% asymmetry |
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| Escape behavior | 81.4% | 46.6% | Distinct motor profiles |
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| Grooming behavior | 4.7% | 37.2% | Stable individual preferences |
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| Divergent synapses | — | 76,034 (0.50%) | Experience-dependent plasticity |
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| Cross-individual r | — | 0.19 | Near-independent dynamics |
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See Figures 2–9 in the paper for detailed visualizations.
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---
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## System Requirements
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| Component | Minimum | Tested |
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| **GPU** | NVIDIA with CUDA 12.x, 6 GB VRAM | RTX 5090 Laptop (24 GB) |
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| **RAM** | 32 GB | 64 GB DDR5 5600 MHz |
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| **CPU** | Intel i5 / AMD Ryzen 5 | Intel Core Ultra 9 285HX |
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| **OS** | Windows 10 / Ubuntu 20.04 | Windows 11 Home |
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| **Python** | 3.10+ | 3.10.16 |
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| **PyTorch** | 2.0+ (CUDA) | 2.5.1+cu128 |
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| **MuJoCo** | 3.0+ | 3.2.7 |
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---
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## Citation
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```bibtex
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@article{rojas_aliaga_2026,
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author = {Rojas Aliaga, Enrique Manuel},
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title = {Emergent Individuality and Neural Integration in Whole-Brain
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Connectome Simulations of {Drosophila melanogaster}},
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year = {2026},
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journal = {Zenodo},
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doi = {10.5281/zenodo.19152238},
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url = {https://doi.org/10.5281/zenodo.19152238}
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}
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```
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## Acknowledgments
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- [FlyWire Consortium](https://flywire.ai/) — Complete adult *Drosophila* connectome (Dorkenwald et al., 2024; Schlegel et al., 2024)
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- [NeuroMechFly v2](https://neuromechfly.org/) — Biomechanical fly model (Lobato-Rios et al., 2024)
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- [MuJoCo](https://mujoco.org/) — Physics engine (DeepMind)
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- [Shiu et al. (2024)](https://doi.org/10.1038/s41586-024-07763-9) — LIF connectome model reference
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## License
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[MIT License](LICENSE) — free to use, modify, and distribute.
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---
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**Enrique Manuel Rojas Aliaga** · enrique_rojas1@usmp.pe · Facultad de Ingenieria y Arquitectura, Universidad de San Martin de Porres, Lima, Peru
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