Parallelizable Leaky-Integrate-and-Fire (ParaLIF) Neuron

This repository contains code for simulating ParaLIF neuron to accelerate training of spiking neural networks (SNN). ParaLIF is presented in Yarga, S. Y. A., & Wood, S. U. (2024). Accelerating Spiking Neural Networks with Parallelizable Leaky Integrate-and-Fire Neurons. The ParaLIF neuron is compared to Leaky-Integrate-and-Fire (LIF) neuron on the Spiking Heidelberg Digits (SHD), Neuromorphic-TIDIGITS, Neuromorphic-MNIST, DVSGesture and Yin-Yang datasets. This repository consists of a few key components:

• datasets.py: This module provides a simple interface for loading and accessing training and test datasets.

• network.py: This module contains the implementation of the neural network itself, including code for training and evaluating the network.

• run.py: This is the main entry point for running the simulation. It provides a simple command-line interface for specifying various options.

• datasets directory: This directory contains training and test datasets.

• neurons directory: This directory contains implementations for the two neurons types, extending the base class in base.py. The available models are:

  • lif.py: The Leaky Integrate-and-Fire model
  • paralif.py: The Parallelizable Leaky-Integrate-and-Fire Neuron model. It can be simulated with various spiking functions.

• outputs directory: This directory contains outputs generated by the simulation.

Usage

The run.py script can be run using various arguments. The following are available:

• --seed: Random seed for reproducibility.
• --dataset: The dataset to use for training, options include heidelberg, ntidigits, nmnist, dvsgesture and yinyang .
• --neuron: The neuron model to use for training, options include LIF, ParaLIF-SB, ParaLIF-GS, ParaLIF-D, ParaLIF-T and other variations.
• --nb_epochs: The number of training epochs.
• --tau_mem: The neuron membrane time constant.
• --tau_syn: The neuron synaptic current time constant.
• --batch_size: The batch size for training.
• --hidden_size: The number of neurons in the hidden layer.
• --nb_layers: The number of hidden layers.
• --recurrent: Whether to use recurrent architecture or not.
• --reg_thr: The spiking frequency regularization threshold.
• --reg_thr_r: The spiking frequency regularization threshold for recurrent spikes.
• --loss_mode: The mode for computing the loss, options include last, max, and mean.
• --data_augmentation: Whether to use data augmentation during training.
• --shift: The random shift factor for data augmentation.
• --scale: The random scale factor for data augmentation.
• --window_size: Define the input time resolution.
• --dir: The directory to save the results.
• --save_model: Whether to save the trained model.
• --best_config: Select the best configuration for the given dataset.

Examples - Basic

To run the code in the basic mode, the following commands can be used.

python run.py --seed 0 --dataset 'heidelberg' --best_config
python run.py --seed 0 --neuron 'ParaLIF-GS' --dataset 'yinyang'
python run.py --seed 0 --neuron 'ParaLIF-T' --dataset 'ntidigits' --recurrent

Citation

If you find this work helpful, please consider citing it:

@article{yarga2024accelerating,
  title={Accelerating Spiking Neural Networks with Parallelizable Leaky Integrate-and-Fire Neurons},
  author={Yarga, Sidi Yaya Arnaud and Wood, Sean UN},
  year={2024}
}