Showcases of Spiking Neural Network, which have Synaptic Plasticity

Network model of Rosenbaum et al. (2017) reimplemented in Brian 2. Developed as a course project during OCNC2017.

This repository contains all codes necessary to reproduce figures and results reported in Stein, Barbosa et al. (Nature Communications, 2020) from the raw data acquired in human behavioral experiments (data included in the repository), and from the relevant model simulations.

A demo for robotic control loop with Intel's Loihi neuromorphic chip used in our papers

A few Colab Notebooks that can be used to get started with pyNN and test virtual sPyNNaker without many issues.

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.

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

code demos for primitives of spiking neural networks

Demo: Spiking Neural Network (SNN) using Generalised Linear Model (GLM)

Bio-inspired spiking-neural-network framework on an autonomous robot car.

SSTDP is a efficient spiking neural network training framework, which is contributed by Fangxin Liu and Wenbo Zhao.

A supervised learning algorithm of SNN is proposed by using spike sequences with complex spatio-temporal information. We explore an error back-propagation method of SNN based on gradient descent. The chain rule proved mathematically that it is sufficient to update the SNN’s synaptic weights by directly using an optimizer. Utilizing the TensorFlo…

Python and ROS implementation of an SNN on Intel's Loihi neuromorphic processor mimicking the oculomotor system controlling a biomimetic robotic head

PyTorch and Loihi implementation of the Spiking Neural Network for decoding EEG on Neuromorphic Hardware

Spiking Neural Network implementation in pure C++ with minimal dependencies

Hao's sym-STDP-SNN model

Repo of the bachelor thesis 'Dynamic memory traces for sequence learning in spiking networks'

A neuroscientific sequence learning model on spiking neural networks with winner-take-all circuits and lateral inhibition. Written using the NEST neural simulator and custom neuron/synapse models.

Mapping Spike Activities with Multiplicity, Adaptability, and Plasticity into Bio-Plausible Spiking Neural Networks