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SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.

Simulation of spiking neural networks (SNNs) using PyTorch.

Deep learning with spiking neural networks (SNNs) in PyTorch.

PyTorch implementation of SLAYER for training Spiking Neural Networks

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values const... Read more

Efficient Spiking Neural Network framework, built on top of PyTorch for GPU acceleration

A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3(ultralytics/yolov3 & eriklindernoren/PyTorch-YOLOv3), with support for Spiking-YOLOv3-Tiny at present.

Energy-efficient Event-driven Spiking Neural Network accelerator for FPGA with PyTorch integration

A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021