SNN MNIST "Unsupervised learning of digit recognition using spike-timing-dependent plasticity"
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Updated
Aug 7, 2015 - Python
SNN MNIST "Unsupervised learning of digit recognition using spike-timing-dependent plasticity"
Graceful Degradation Under Noise on Brain Inspired Robot Controllers
Neurorobotic Simulations on the Degradation of Multiple Column Liquid State Machines
Step-by-Step Brian Simulation
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 networ…
This project is an easy tool to create and train neurals networks in C++. Fully modular, you will be able to create a personalized network with several layers and many types of perceptrons.
SNN (Shared Nearest Neighbors) plugin for Neo4j.
A Spiking Neural Network framework with SNNML parser written in C++
As was shown in Table 3, SNN achieved 98% accuracy with HTRU2 dataset. This implementation achieves the same result. Model description: Conic layer form 8 hidden layers starting with 512 units. AlphaDropout with rate = 0.05 inputs normalized to zero mean and unit variance
Experiments with spiking neural networks (SNNs) in PyTorch. See https://github.com/BINDS-LAB-UMASS/bindsnet for the successor to this project.
Implementation of a Spiking Neural Network in Tensorflow.
Basic SNN propogating spikes between LIF neurons
Nuerapse simulations for SNNs
Evaluates the robustness of capsule networks in regard to novel viewpoints, occlusion and more
This project will observe 4 different choice of hyperparameter on their effect to CNN with ReLU and SELU as activation function
Python library for easy creation and running of spiking neural networks in tensorflow.
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