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simpleitk-notebooks

Jupyter notebooks for learning how to use SimpleITK

snn-encoder-tools

Data encoders

snn-for-pattern-recognition

A 3-layer SNN code for performing MNIST handwritten digit recognition using a supervised spike based learning rule

snn-image-segmentation

This is project for image segmentation based on Spiking Neural Network

snn-minibatch

Repository for code, data, and other artifacts for "Minibatch Processing in Spiking Neural Networks"

snn-mnist

spiking neural network

snn.tutorial

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

snn_benchmark

snn_cv_applications_resources

Paper list for SNN based computer vision tasks.

snn_model_pytorch_project

snn_toolbox

Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.

snns-rnns

The code associated with Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.

snntorch

Deep and online learning with spiking neural networks in Python

spike-element-wise-resnet

Deep Residual Learning in Spiking Neural Networks

spikems

SpikeMS: Deep Spiking Neural Network for Motion Segmentation

spiking-neural-network

Pure python implementation of SNN

spiking-neural-network-1

Learning with spiking neural networks using STDP synapses. Python, PyNN, NEST-simulator, numpy

spiking_neural_network_thesis

spikingjelly

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

spinnet

SpiNNaker network testbed

spyketorch

High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.

st-rsbp

stable-diffusion

stbp-for-training-spikingnn

Spatio-temporal BP for SNNs

stdp-mnist

stdp-nmnist

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).

supervised-snn-with-gd

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 TensorFlow framework, a bilayer supervised learning SNN is constructed from scratch. We take the lead in the application of SAR image classification and conduct experiments on the MSTAR dataset.

sym-stdp-snn

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

tempotron-wisconsin

Spiking neuro model Tempotron with Wisconsin Dataset

tensorsandbox

My tensorflow sandbox