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awesome-snn

🔥🔥🔥 A collection of some awesome public SNN(Spiking Neural Network) projects.

brain-cog

Brain-inspired Cognitive Intelligence Engine (BrainCog) is a brain-inspired spiking neural network based platform for simulating the cognitive brains of different animal species at multiple scales and realizing brain-inspired Artificial Intelligence. The long term goal of BrainCog is to provide a comprehensive theory and system to decode the mechanisms and principles of human intelligence and its evolution, and develop artificial brains for brain-inspired conscious living machines in future human-machine society.

carlsim4

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail.

carlsim6

CARLsim is an efficient, easy-to-use, GPU-accelerated software framework for simulating large-scale spiking neural network (SNN) models with a high degree of biological detail.

cifar-10

cnn

hsuantienlin_machinelearning

memtorch

A Simulation Framework for Memristive Deep Learning Systems

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

nest-simulator

The NEST simulator

pubgit

to use

py

Repository to store sample python programs for python learning

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

sinabs

Spiking neural network library based on PyTorch.

snn

snn-

snntorch

Deep and online learning with spiking neural networks in Python

spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp

What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.