This repository contains all code and data to create the figures in the paper Energy-efficient network activity from disparate circuit parameters by Deistler, Macke*, Goncalves* (2022). If you only want to use the machine learning tools developed in this work, see the sbi repository and the corresponding tutorial. If you only need the pyloric simulator, see this repo.
First, create a conda environment:
conda env create --file environment.yml
conda activate stg-energy
Then clone and install this package:
git clone git@github.com:mackelab/STG_energy.git
cd STG_energy
pip install -e .
Please note that all neural networks were trained on sbi v0.14.0. In v0.15.0, the training routine of sbi changed (z-score only using train data). Thus, training on a newer version give slightly different results.
Roughly, the workflow for this work can be divided into three sections: (1) Running the pyloric simulator for many parameter sets, (2) Training the neural density estimator to approximate the posterior and (3) Generating plots.
(1) is implemented in stg_energy/generate_data/simulate... and was run on a compute cluster with SLURM.
(2) is implemented in stg_energy/generate_data/train...
(3) is implemented in paper/
cd stg_energy/generate_data/simulate_11deg
python simulate_11deg.py
python 01_merge_simulated_data.py
python train_classifier.py
cd stg_energy/generate_data/simulate_11deg_R2
python simulate_11deg.py
python 01_merge_simulated_data.py
python train_classifier_R2.py
cd stg_energy/generate_data/simulate_11deg_R3
python simulate_11deg.py
python 01_merge_simulated_data.py
python train_flow_R3.py
To store the data files, we use Git LFS.
@article{deistler2022energy,
title={Energy efficient network activity from disparate circuit parameters},
author={Deistler, Michael and Macke, Jakob H and Gon{\c{c}}alves, Pedro J},
journal={bioRxiv},
pages={2021--07},
year={2022},
publisher={Cold Spring Harbor Laboratory}
}
If you have any questions regarding the code, please contact michael.deistler@uni-tuebingen.de