Simulation of spiking neural networks (SNNs) using PyTorch.

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

Auryn: A fast simulator for spiking neural networks with synaptic plasticity

spiking-neural-networks

Nuerapse simulations for SNNs

Code for the model presented in the paper "A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule"

A generalizable model of spike-timing dependent plasticity for the Neural Simulation Tool (NEST).

Spiking neural networks are biologically plausible CNNs which learn through a temporally dependent learning method known as Spike Time Dependant Plasticity (STDP)- an alternate to gradient descent. This repository contains layers built on top of Lasagne layers for spiking neural networks. This is the first implementation of spiking neural networks in any tensor based framework to the best of my knowledge. The various layers can be found in snn.py for dense layer and snn_conv.py for other layers. These layers are to be processed for each time step which is done using the Theano scan as a quick hack - in the snn class. The results can be found the ppt. Further details on how to use the code will be put up after later.

A Spiking Neural Network model for Digit Recognition using the N-MNIST dataset.

海月网络--通过人工设计来形成先天的连接，加上利用hebb学习机制来形成后天的连接

This is a repository with implementations of neuron models, synapses, and spiking neural networks (SNN). It's still in development and it has original content in terms of code.

Neuron++ is a library which wraps NEURON (http://www.neuron.yale.edu) with easy to use Python objects.

Spiking neural network simulator

SNN MNIST "Unsupervised learning of digit recognition using spike-timing-dependent plasticity"