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Spiking Neural Network Snn With Pytorch Where Backpropagation Engenders Stdp ⭐ 192

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.

This repository will host models, modules, algorithms and applications developed by the INRC Community to run on the Intel Loihi Platform.

Spyx: Spiking Neural Networks in JAX

Spiking Neural Network ⭐ 30

Basic SNN propogating spikes between LIF neurons

Snn Experiments ⭐ 25

Some of my initial studies using spiking neural networks and Python

Dendrify ⭐ 23

Introducing dendrites to spiking neural networks. Designed for the Brian 2 simulator.

Spikeflow ⭐ 20

Python library for easy creation and running of spiking neural networks in tensorflow.

Ijcnn2016 ⭐ 13

Diverse, Noisy and Parallel: a New Spiking Neural Network Approach for Humanoid Robot Control

Snn From Scratch With Python ⭐ 13

NeuroMorphic Predictive Model with Spiking Neural Networks (SNN) using Pytorch

Snn Adversarial Attacks ⭐ 9

Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters

Hybrid Stereo Matching ⭐ 8

Framework for event- and frame-based stereo matching using SNNs and MRFs

Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".

PyTorch implementation of IJCAI 2020 paper Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network

Neuromatchacademy ⭐ 6

Repo containing computational neuroscience notebooks and a project detecting latent states in neural activity using Hidden Markov Models on spiking data, made during the Neuromatch Academy 2020.

Event Based Velocity Prediction Snn ⭐ 5

Neuromorphic computing uses very-large-scale integration (VLSI) systems with the goal of replicating neurobiological structures and signal conductance mechanisms. Neuromorphic processors can run spiking neural networks (SNNs) that mimic how biological neurons function, particularly by emulating the emission of electrical spikes. A key benefit of using SNNs and neuromorphic technology is the ability to optimize the size, weight, and power consumed in a system. SNNs can be trained and employed i

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