Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

A new model for quickly training and simulating adaptive leaky integrate-and-fire spiking neural networks.

Installing dependencies

Install all required dependencies and activate the blocks environment using conda.

conda env create -f environment.yml
conda activate blocks

Getting started tutorial

See the notebooks/getting_started.ipynb notebook for getting started with our model.

Reproducing paper results

All the paper results can be reproduced using the scripts available in the scripts folder.

Running benchmark experiments

The python run_benchmarks.py script will benchmark the time of the forward and backward passes of the blocks and the standard SNN model for different numbers of neurons and simulation steps.

Training models

Ensure that the computer has a CUDA capable GPU with CUDA 11.7 installed.

1. Downloading and processing datasets

Machine learning datasets:

The content of the Neuromorphic-MNIST dataset can be downloaded and unzipped into the data/N-MNIST directory. Thereafter, the python convert_nmnist2h5.py script (adapted from Perez-Nieves et al., 2021) needs to be run which processes the raw dataset. The Spiking Heidelberg Digits (SHD) dataset can be downloaded and unzipped into the data/SHD directory.

E-phys dataset:

Running the scripts/ephys/build_data.py script will download and process the necessary data from the Allen Institute.

2. Train model

You can train the blocks and standard SNN on the different datasets using the train.py scripts in the scripts/ephys and scripts/supervised folders respectively. See respective folders for different experiment run scripts.

Building result figures

Speedup plots can be built using: notebooks/results/speed_benchmarks.ipynb

Machine learning benchmark plots can be built using: notebooks/results/supervised_benchmarks.ipynb

Neural-fitting plots can be built using: notebooks/results/ephys.ipynb

Machine learning benchmark results

Neural-fitting results