Riccardo Massa and others
An efficient spiking neural network for recognizing gestures with a DVS Camera on the Loihi neuromorphic processor. It can be used for analyzing the DNN-to-SNN conversion through SNNToolbox, and the codes for pre-processing the DvsGesture dataset to make it possible to train in the DNN domain….
Intel
An open-source software framework for developing neuro-inspired applications and mapping them to neuromorphic hardware. Includes various useful packages including a Neuromorphic Constrained Optimization Library….
Applied Brain Research
A Python library for building and simulating large-scale neural models. Nengo can create sophisticated spiking and non-spiking neural simulations with sensible defaults in a few lines of code. In addition, Nengo is highly versatile. You can define your own neuron types and learning rules, drive robots, and even simulate your model on a completely different neural simulator or neuromorphic hardware….
Applied Brain Research
A simulator for Nengo models. That means it takes a Nengo network as input and allows the user to simulate that network using some underlying computational framework (in this case, TensorFlow). In practice, this means that the code for constructing a Nengo model is exactly the same as it would be for the standard Nengo simulator….
Jason Eshraghian
A Python package for performing gradient-based learning with spiking neural networks. It extends the capabilities of PyTorch, taking advantage of its GPU accelerated tensor computation and applying it to networks of spiking neurons….
SpiNNaker – University of Manchester
SpiNNaker is a novel computer architecture inspired by the working of the human brain. A SpiNNaker machine is a massively parallel computing platform, targeted toward three main areas of research: Neuroscience, Robotics, and Computer Vision. This package provides standard code for PyNN implementations for SpiNNaker….
Friedemann Zenke
An open-source project that provides a PyTorch-based library for deep learning with spiking neural networks (SNNs). The library aims to make it easy to train and evaluate SNNs. This library provides a range of tools and functionality to help researchers and practitioners work with SNNs in a flexible and intuitive way, including support for common SNN models and training algorithms….