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

OeSNN-AD implemention for Python

Metrics for spiking neural networks based on torchmetrics

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.

Evaluates the robustness of capsule networks in regard to novel viewpoints, occlusion and more

code demos for primitives of spiking neural networks

This repository hosts the code used in the paper "Collective control of modular soft robots via embodied Spiking Neural Cellular Automata"

VS2N (Visualization tool for Spiking Neural Networks) is an interactive web-based tool designed to analyze and visualize the collected activity from spiking neural networks.

This is a low-power, high-precision, trainable SNN based on the FS-STBP algorithm, researched by the author while participating in the “Brain-like AI Algorithms and Chips Collaborative Design Group” at UESTC.

Novel method to translate spatial anatomical data into spiking neural networks (SNNs).

Comparison of neural dynamics like LIF and ALIF for image classification on MNIST and Fashion-MNIST datasets.

A differentially private spiking neural network with temporal enhanced pooling

Develop SNN-aware Noise Addition Layers

Neuromorphic ASIC with 96 neurons on Tiny Tapeout 7

A python package based on PyTorch for simulating Spiking Neural Networks (SNNs)

A VHDL (and eventually Memristive) implementation of ESA's SNN4Space project

High Performance Simulation of Spiking Neural Network on GPGPUs

Resonate-and-fire neuron model enabled for backpropagation learning with PyTorch

This repository presents a comprehensive comparative study that delves into the weights and biases of both inbuilt and custom-built Sequential Neural Network (SNN) models. The primary objective is to gain a deeper understanding of the differences and nuances between these two approaches.