Peter Blouw and others
A power benchmarking code for running a simple two-layer, 256 neuron per layer neural network keyword spotter on both neuromorphic and conventional hardware devices. On conventional devices a Tensorflow version of the keyword spotter is used, while on neuromorphic devices (Loihi), an architecturally identical Nengo version is used….
BindsNET
A spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab….
brainpy
A flexible, efficient, and extensible framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top of JAX, Numba, and other JIT compilers)….
brain-team
A free, open source simulator for spiking neural networks. It is written in the Python programming language and is available on almost all platforms….
TU-Delft
cuSNN is a C++ library that enables GPU-accelerated simulations of large-scale Spiking Neural Networks (SNNs)….
Zhexiong Wan and others
Learning Dense and Continuous Optical Flow from an Event Camera…
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….
Perception and Robotics Group, UMD
A project that explores the use of event cameras in dynamic obstacle dodging tasks. Unlike traditional cameras. The project uses deep learning techniques to train a neural network to predict the trajectory of moving obstacles and generate control signals for a drone to avoid collisions in real-time….
IBM
An open source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. The toolkit is fully GPU accelerated and can be used to conveniently estimate the impact of material properties and non-idealities of future analog technology on the accuracy for arbitrary ANNs….
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….
Lava
A library of deep learning tools within Lava that support offline training, online training and inference methods for various Deep Event-Based Networks….
Guillaume Bellec and others
A tensorflow 1.12 library and a tutorial to train a recurrent spiking neural networks. This model uses a method of network rewiring to keep a sparse connectivity during training, which is called DEEP-R….
Guillaume Bellec and others
A tensorflow 1.12 library and a tutorial to train a Long short-term memory Spiking Neural Network. The proposed model uses a method of network rewiring to keep a sparse connectivity during training, which is called DEEP R….
Prophesee
A C++ and Python SDK for event-based data processing. It includes algorithms available via APIs, code samples and documentation allowing you to develop event-based applications ready to go to production. The algorithms are written in C++ for performance reasons, but are also provided through a Python API through bindings of the C++ code. Some modules (e.g. the Core and Machine Learning modules) also provide features in pure Python modules….
Zhenhua Zhu and others
A behavior-level modeling tool for designing and simulating memristor-based neuromorphic computing systems. MNSIM 2.0 provides a high-level model of the memristive devices and their circuits, allowing for efficient and accurate simulation of large-scale systems. The tool supports various neural network architectures and can aid in optimizing the performance of memristor-based neuromorphic systems….
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….
Laboratory for Emerging Devices and Circuits, Georgia Institute of Technology
An integrated framework to emulate the deep neural networks (DNN) inference performance or on-chip training performance on the hardware accelerator based on near-memory computing or in-memory computing architectures….
Norse
Norse expands PyTorch with primitives for bio-inspired neural components, bringing two advantages: a modern and proven infrastructure based on PyTorch and deep learning-compatible spiking neural network components….
Prophesee
An open source project associated with Metavision SDK. It enables anyone to get a better understanding of event-based vision, directly interact with events and build their own applications or plugins….
SynSense
An open-source Python package for simulating, training and deploying spiking neural network applications. Includes simulations and deployment to SynSense hardware….
Andrzej Kucik and Gabriele Meoni
A package that deploy spiking neural networks (SNN) in land cover and land use classification tasks. In particular, a VGG-16 -based artificial neural network (ANN) classifier is converted into an SNN using KerasSpiking. After fine-tuning of the converted model, the accuracy depreciation as well as a potential improvement in the energy consumption are compared on selected hardware platforms….
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….
Institute of Neuromorphic Engineering
A tool to facilitate the download, manipulation and loading of event-based/spike-based data. It's like PyTorch Vision but for neuromorphic data!…