Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

Open source SDK to create applications leveraging event-based vision hardware equipment

DDD20 End-to-End Event Camera Driving Dataset

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

Low-level Python APIs for Accessing Neuromorphic Devices.

OpenN@S: Open-source software to NAS automatic VHDL code generation

Sends event camera data from A to B. Supports live cameras and dead recordings

This repository aims to bridge the gap between artificial neural networks and biological brain mechanisms, fostering interdisciplinary research in cognitive computing, neuromorphic engineering, and biologically-inspired AI.

Brilliantly Radical Artificially Intelligent Neural Machine

An open-source cross-platform package to analyze and post-process spiking information obtained from neuromorphic cochleas

A labeled dataset from a subset of the MVSEC dataset for car detection at night driving conditions.

Neuromorphic architectures are hardware architectures that use the biologically inspired neural functions as the basis of operation. Information processing based on spiking neuron architectures have caught considerable attention in recent years due to its low power consumption compared to traditional artificial neural networks. In this project, …

Neuromorphic Bird Classifier Desktop App (NeuroBCDA) bundled with Live Event Camera Simulator

Neuromorphic Auditory Visualizer Tool

This repository aims to provide a curated collection of resources for researchers and practitioners interested in neuromorphic navigation, including datasets, hardware platforms, and software tools.

Bio-inspired navigation system for artificial agents using spiking neural networks

Video to AER data synthesis model for robust data conversion and architecture exploration.

Error Signals For Adaptive Neuro-Robotics: preliminary experiment

Emerging Threats and Countermeasures in Neuromorphic Systems: A Survey. By designing and analyzing Memristor Devices for Neuromorphic Computing, Spiking Neural Networks (SNNs), Physically Unclonable Functions (PUFs), True Random Number Generators (TRNGs), we are investigating their hardware and software security (attacks and defenses).

Runs networkx graphs representing spiking neural networks of LIF-neurons on lava-nc or networkx.