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

A curated list of awesome cns frameworks, libraries, and software + First class pure python Tutorial Series for Spiking Neural Networks 🔥

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

personal repository

spiking-neural-networks

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.

High-performance Spiking Neural Networks Library Written From Scratch with C++ and Python Interfaces.

Repository for the master thesis titled "Local Unsupervised Learning of Multimodal Event-Based Data with Spiking Neural Networks", by Julian Lopez Gordillo (MSc in Artificial Intelligence, 2019-2021).

Nuerapse simulations for SNNs

Convolutional spiking neural network implementing STDP

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

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

Convolutional spiking neural network implementing voltage-dependent synaptic plasticity and single-spike integrate-and-fire neurons

Implementation of Spiking Neural Networks (SNNs) using SpykeTorch, featuring STDP and R-STDP training methods for efficient neural computation.

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.

STDP implementation for Nengo and single layer network

CPU-based spiking neural network framework for classification layers employing first-spike coding and supervised STDP training.

Convolutional Spiking Neural Network to recognize speech utterances using Spike-Timing-Dependent Plasticity

Malleable spiking neural network framework and training platform.

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

Probably the world most FC implementation of transformers with SNNs

A simulation of unsupervised (STDP) and reinforcement learning (Reward-based STDP) in human brain

Implementation of some of the basic neural models and simulation of interactions between neurons in a population and learning process from scratch in Python.

[TMLR] S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

A Python toolkit for advanced mathematics in AI and computational neuroscience, designed to support the development of the Fully Unified Model (FUM).

Biologically plausible cognitive architecture: structural memory, thought formation, and language without gradients or backpropagation

A simple experiment to compare Artificial and Spiking Neural Networks in Sequential and Few-Shot Learning.

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