Awesome
Event-SNN-Resources
Code and Examples
The repository provides a collection of Jupyter notebooks that demonstrate the implementation of various techniques and methods related to Event-based Spiking Neural Networks. Below are links to the key notebooks and their descriptions:
Basic Examples
- Basic Event Processing: This notebook covers the fundamental steps for processing event-based data.
Advanced Examples
- Build Feedforward-SNN: A tutorial on building Feed-Forward Spiking Neural Networks using PyTorch.
- Build S-CNN: A tutorial on building Spiking Convolutional Neural Networks using PyTorch.
Techniques and Methods
- Image to Poisson Spikes: This repository includes code to convert images into Poisson spikes.
- Image to TTFS Spikes: Instructions and code to convert images into Time-to-First-Spikes activations.
Datasets
The repository utilizes a variety of datasets. Besides classic RGB datasets, event-based datasets are provided by Tonic, a library for event-based data, which simplifies the handling and processing of event-based datasets. Below are some of the event datasets provided by Tonic:
For more information, refer to the Tonic Documentation and the list of available Tonic Datasets.
SNN Libraries
- NEST: NEST Website
- NEURON: NEURON Website
- Brian: Brian2 GitHub
- Nengo: Nengo Website
- SNNToolbox: SNNToolbox GitHub
- BindsNET: BindsNET GitHub
- snnTorch: snnTorch GitHub
- SpikingJelly: SpikingJelly GitHub
- NxTF: NxTF GitHub
How to Use This Repository
- Clone the Repository:
git clone https://github.com/radlab-sketch/Event-SNN-Resources.git