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Guillermo Gallego and others
A collection of resources related to event-based vision, a type of computer vision that processes visual data as a stream of events, rather than traditional frame-based methods. The repository contains links to relevant papers, datasets, and tutorials to help further research and development in this field….
Shikhar Gupta
This is a Python implementation of a hardware efficient spiking neural network. It includes modified learning and prediction rules which could be realised on hardware in an energy efficient way. The aim is to develop a network which could be used for on-chip learning and prediction….
Neuromatch Academy
We have curated a curriculum that spans most areas of computational neuroscience (a hard task in an increasingly big field!). We will expose you to both theoretical modeling and more data-driven analyses. This section will overview the curriculum….
Tiffany Hwu and others
A complete neuromorphic solution to outdoor navigation and path planning. The proposed solution is based on a spiking neural network that mimics the biological nervous system of insects. The solution is tested in a real-world scenario and demonstrates promising results in terms of accuracy and efficiency….
Carlos Rios and others
A photonic platform that can combine integrated optics with collocated data storage and processing to enable all-photonic, in-memory computations. By employing nonvolatile photonic elements based on a special phase-change material, it can achieve direct scalar and matrix-vector multiplication, featuring a novel single-shot Write/Erase and a drift-free process….
Paul Merolla and others
A scalable and flexible non–von Neumann architecture that leverages contemporary silicon technology. It includes a 5.4-billion-transistor chip with 4096 neuro synaptic cores interconnected via an intra chip network that integrates 1 million programmable spiking neurons….
A tutorial collection that provides educational material on the topic of robotics. It includes a variety of resources such as articles, GitHub repos, interactive simulations, and hands-on projects to help learners understand the concepts and techniques used in robotics….
iniLabs
An asynchronous event-based silicon cochlea. The board takes stereo audio inputs; the custom chip asynchronously outputs a stream of address-events representing activity in different frequency ranges. As such it is a silicon model of the cochlea, the auditory inner ear….
Tasbolat Taunyazov
NeuTouch is a neuromorphic fingertip tactile sensor that scales well with the number of taxels. The proposed Visual-Tactile Spiking Neural Network (VT-SNN) also enables fast perception when coupled with event sensors. The proposed visual-tactile system (using the NeuTouch and Prophesee event camera) is evaluated on two robot tasks: container classification and rotational slip detection….
Debarun Sengupta and others
An approach entailing carbon nanofiber–polydimethylsiloxane composite-based piezoresistive sensors, coupled with spiking neural networks, to mimic skin-like sensing….
Luke Osborn and others
A multilayered electronic dermis (e-dermis) with properties based on the behavior of mechanoreceptors and nociceptors to provide neuromorphic tactile information for an amputee….
Sony and Prophesee
Metavision Sensing and Software offer all the resources to work with event-driven cameras. The sensational IMX636ES is the new event-driven sensor created by a collaboration between Sony and PROPHESEE. The Metavision software offers 95 algorithms, 67 code samples and 11 ready-to-use applications to be used with this new generation of cameras….
Inivation AG
A high-resolution event-based sensors that only output event streams. Compared to the DAVIS series, it can provide (640 x 480) resolution, but is not able to output greyscale images….
Inivation AG
A dynamic and active pixel vision sensor (DAVIS), which addresses the lack of greyscale output of the DVXplorer series by utilising asynchronous DVS events and synchronous global shutter frames concurrently. The active pixel sensor (APS) circuits and the DVS circuits within a pixel share a single photodiode….
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….
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….
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….
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….
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….
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….