Simulation of spiking neural networks (SNNs) using PyTorch.

Experiments with spiking neural networks (SNNs) in PyTorch. See https://github.com/BINDS-LAB-UMASS/bindsnet for the successor to this project.

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

Basic SNN propogating spikes between LIF neurons

The Spiking Reservoir (Liquid State Machine - LSM) Simulator

Code for "Convolutional spiking neural networks (SNN) for spatio-temporal feature extraction" paper

Python library for easy creation and running of spiking neural networks in tensorflow.

Spiking neural network system

spiking-neural-networks

Implementation of a Spiking Neural Network in Tensorflow.

Nuerapse simulations for SNNs

Training spiking networks with hybrid ann-snn conversion and spike-based backpropagation

Diverse, Noisy and Parallel: a New Spiking Neural Network Approach for Humanoid Robot Control

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

A tiny Spiking neural network library implemented by BindsNet.

Spiking-DDPG trains an SNN for energy-efficient mapless navigation on Intel's Loihi neuromorphic processor.

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.

Encoders for turning real-valued data into spike trains.

The repository offer the simulation scripts of three different single-layer SNN learning algorihtms proposed by Querlioz, Diehl and this author.

Hao's sym-STDP-SNN model

Neurorobotic Simulations on the Degradation of Multiple Column Liquid State Machines

Collection of RNN GAN SNN CNN in Tensorflow

This project is an easy tool to create and train neurals networks in C++. Fully modular, you will be able to create a personalized network with several layers and many types of perceptrons.

Module for NEST simulator developed in the Project CerebNEST, aiming to recreate and simulate in NEST realistic cerebellar-inspired models.

Step-by-Step Brian Simulation

Graceful Degradation Under Noise on Brain Inspired Robot Controllers

DRL with population coded spiking neural network for optimal and energy-efficient continuous control.

Positioning control on a collaborative robot by sensor fusion with liquid state machines

spiking neural network