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guillaume-chevalier / 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.

Home Page:https://guillaume-chevalier.com/spiking-neural-network-snn-with-pytorch-where-backpropagation-engenders-stdp-hebbian-learning/

Repository from Github https://github.comguillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDPRepository from Github https://github.comguillaume-chevalier/Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP

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