Drosophila Whole Brain Fitting

This project implements a whole-brain spiking neural network model to simulate and predict the dynamics of the Drosophila (fruit fly) brain activity.

Data

This project requires two main datasets:

• connectome data from the FlyWire project (version 630/783): https://codex.flywire.ai/
• neural activity recordings from the Drosophila brain: https://doi.org/10.6084/m9.figshare.13349282

We have also provided preprocessed data files in the data/ directory for convenience.

Please download the datasets (https://drive.google.com/file/d/1YeespJpoRfwS_kkH-VVuuFSJNgERcUK_/view?usp=drive_link) and place them in the appropriate directories (data/) as specified in the code.

Key Features

• Loads and processes Drosophila brain connectome data
• Simulates neural activity with biologically plausible dynamics
• Predicts firing rates across brain regions (neuropils)
• Evaluates prediction accuracy using bin classification and MSE metrics
• Visualizes simulated vs. experimental neural activity

Usage

Run the training and prediction pipeline:

python drosophila_whole_brain_fitting.py --flywire_version 630 --neural_activity_id 2017-10-30_1 --devices 0 --split 0.5 --epoc 50

Command Line Arguments

• --flywire_version: Version of the FlyWire connectome data
• --neural_activity_id: ID of the neural activity recording dataset
• --devices: GPU device ID to use
• --split: Train/test split ratio
• --epoch: Number of epochs for training

Evaluation

The model evaluates performance using:

• Bin accuracy: Percentage of correctly predicted firing rate bins
• MSE loss: Mean squared error between predicted and actual firing rates

Visualization

The model generates visualizations comparing:

• Simulated neuropil firing rates
• Experimental neuropil firing rates

Figures are saved in the output directory.

Citation

If you use this code or data, please cite:

@article {Wang2024.09.24.614728,
	author = {Wang, Chaoming and Dong, Xingsi and Ji, Zilong and Jiang, Jiedong and Liu, Xiao and Wu, Si},
	title = {BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks},
	elocation-id = {2024.09.24.614728},
	year = {2025},
	doi = {10.1101/2024.09.24.614728},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/07/27/2024.09.24.614728},
	eprint = {https://www.biorxiv.org/content/early/2025/07/27/2024.09.24.614728.full.pdf},
	journal = {bioRxiv}
}