Simulation of spiking neural networks (SNNs) using PyTorch.
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Updated
May 23, 2024 - Python
Simulation of spiking neural networks (SNNs) using PyTorch.
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Deep and online learning with spiking neural networks in Python
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.
Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper
Training spiking networks with hybrid ann-snn conversion and spike-based backpropagation
Experiments with spiking neural networks (SNNs) in PyTorch. See https://github.com/BINDS-LAB-UMASS/bindsnet for the successor to this project.
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.
Spiking-DDPG trains an SNN for energy-efficient mapless navigation on Intel's Loihi neuromorphic processor.
Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network
A machine learning library for spiking neural networks. Supports training with both torch and jax pipelines, and deployment to neuromorphic hardware.
DRL with population coded spiking neural network for optimal and energy-efficient continuous control.
spiking-neural-networks
Quantization-aware training with spiking neural networks
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"
Implementation of a Spiking Neural Network in Tensorflow.
Nuerapse simulations for SNNs
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