Meta-Dynamic Neurons improved Spatio-temporal Generalization of Spiking Neural Networks

Code for paper: AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.

code for "FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models".

Code for the model presented in the paper "LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition."

A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

Iris segmentation using feature channel optimization for noisy environments

End-to-End Speech Processing Toolkit

The PyTorch-based audio source separation toolkit for researchers

Event-Based Angular Velocity Regression with Spiking Networks

🌊 Numerically solving and backpropagating through the wave equation

Code from our paper: SuperSpike: Supervised learning in multi-layer spiking neural networks.

Simulation of spiking neural networks (SNNs) using PyTorch.

Code for the model presented in the paper "A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule"

Spiking neural network controler for tracking a trajectory

Long short-term memory Spiking Neural Networks

RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.

A spiking neural network based on the LIF neuron model to detect genre of given song

One-Shot Object Appearance Learning using Spiking Neural Networks

Liquid State Machines in Python and NEST

Basic SNN propogating spikes between LIF neurons

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…

Toolbox for converting analog to spiking neural networks (ANN to SNN), and running them in a spiking neuron simulator.

Pure python implementation of SNN

Hao's sym-STDP-SNN model

Bio-inspired spiking-neural-network framework on an autonomous robot car.

Implementation of the paper Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints by Habenschuss et al.