snn

Simulation of spiking neural networks (SNNs) using 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.

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

🔥🔥🔥 A collection of some awesome YOLO object detection series projects.

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

Basic SNN propogating spikes between LIF neurons

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

The Spiking Reservoir (Liquid State Machine - LSM) Simulator

spiking-neural-networks

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

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

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

Spiking neural network system

Implementation of a Spiking Neural Network in Tensorflow.

Nuerapse simulations for SNNs

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

Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"

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

Spiking neural network library based on PyTorch.

A tiny Spiking neural network library implemented by BindsNet.

Encoders for turning real-valued data into spike trains.

High Performance Simulation of Spiking Neural Network on GPGPUs

Code for DCT_SNN, an input encoding scheme for SNNs using DCT

code for generating data set ES-ImageNet with corresponding training code

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.

This repository contains the models and training scripts used in the papers: "Quantizing Spiking Neural Networks with Integers" (ICONS 2020) and "Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators" (ISCAS 2020).