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….
Nanyang Technological University
A user-friendly package providing easy access to advanced biosignal processing routines. Researchers and clinicians without extensive knowledge of programming or biomedical signal processing can analyze physiological data with only two lines of code….
Bodo Rueckauer and others
An open-source software platform for compiling and running deep spiking neural networks (SNNs) on the Intel Loihi neuromorphic hardware platform. The paper introduces the NxTF API, which provides a simple interface for defining and training SNNs using common deep learning frameworks, and the NxTF compiler, which translates trained SNN models into executable code for the Loihi chip….
Telluride Engineering Workshop Participants – Institute of Neuromorphic Engineering
A set of tools that allow users to decode brain signals into the signals that generated them. It can determine whether the signals come from visual or auditory stimuli, and whether they are measured with EEG, MEG, ECoG or any other neural response for decoding. This toolbox is provided as Matlab and Python code, along with documentation and some sample EEG and MEG data….