hdufang Goto Github PK
Type: User
Company: Hangzhou Dianzi University
Bio: major in CS, now interested in deep learning, spiking neural networks, bioinfomatics, etc.
Location: Hangzhou, China
Type: User
Company: Hangzhou Dianzi University
Bio: major in CS, now interested in deep learning, spiking neural networks, bioinfomatics, etc.
Location: Hangzhou, China
A Spiking Convolutional Neural Network implemented using brian2 package.
Pure python implementation of SNN
A spiking neural network based on the LIF neuron model to detect genre of given song
Spiking neural networks are biologically plausible CNNs which learn through a temporally dependent learning method known as Spike Time Dependant Plasticity (STDP)- an alternate to gradient descent. This repository contains layers built on top of Lasagne layers for spiking neural networks. This is the first implementation of spiking neural networks in any tensor based framework to the best of my knowledge. The various layers can be found in snn.py for dense layer and snn_conv.py for other layers. These layers are to be processed for each time step which is done using the Theano scan as a quick hack - in the snn class. The results can be found the ppt. Further details on how to use the code will be put up after later.
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.
Working on improving spiking recurrent neural networks
A python implementation of a spiking neural network with STDP and Reinforcement learning methods
High-speed simulator of convolutional spiking neural networks with at most one spike per neuron.
Tutorial for surrogate gradient learning in spiking neural networks
Solving arbitrary size Sudoku puzzles by Spiking Neural Network
Supervised learning in spiking neural networks for precise temporal encoding
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