neuromorphic-engineering
There are 3 repositories under neuromorphic-engineering topic.
- Neuromorphic-Computing-Guide
mikeroyal / Neuromorphic-Computing-Guide
Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
neuromorphic-computingneuromorphic-hardwareneuromorphic-engineeringneuromorphicneural-networksneural-engineneural-engineeringneural-networkneuronal-networkneural-machine-translationneuron-modelsneural-processesneural-simulatorsneuroscienceneural-renderingneocortexartificial-neural-networksbrain-imagingneuroimagingneural-radiance-fieldsLanguage:Python 282 prophesee-ai / openeb
Open source SDK to create applications leveraging event-based vision hardware equipment
Language:C++ 157Shikhargupta / Spiking-Restricted-Boltzmann-Machine
RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.
Language:Python 27duguyue100 / pyaer
Low-level Python APIs for Accessing Neuromorphic Devices.
Language:Python 26RTC-research-group / OpenNAS
OpenN@S: Open-source software to NAS automatic VHDL code generation
Language:C# 22SensorsINI / ddd20-utils
DDD20 End-to-End Event Camera Driving Dataset
Language:Jupyter Notebook 22- pyNAVIS
jpdominguez / pyNAVIS
An open-source cross-platform package to analyze and post-process spiking information obtained from neuromorphic cochleas
Language:Python 18 - Language:Verilog 14
A labeled dataset from a subset of the MVSEC dataset for car detection at night driving conditions.
Language:Python 13Neuromorphic Auditory Visualizer Tool
Language:C# 8cepdnaclk / e16-4yp-neuromorphic-architecture
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, as the first stage, we are implementing parallel multiple processing elements based on RISC-V architecture to represent biological neurons. Single neurons can be implemented as a single processor with local memory access or since the spike time of biological neurons is in the millisecond order multiple neurons can be virtualized to a single processor. At the second stage of the process, we are expecting to design encoders and decoders to benchmark the architecture by solving classical machine learning problems.
Language:VHDL 6Andreou-JHULabOrg / RetinoSim
Video to AER data synthesis model for robust data conversion and architecture exploration.
Language:MATLAB 4preetam25 / Robot-Navigation-using-SNNs
Bio-inspired navigation system for artificial agents using spiking neural networks
Language:MATLAB 4EladCh / Error-Signals-For-Adaptive-Neuro-Robotics
Error Signals For Adaptive Neuro-Robotics: preliminary experiment
Language:Jupyter Notebook 3janhohenheim / neuromorphic-engineering-one
Repository for all materials of the UZH/ETH course in Neuromorphic Engineering I
Language:Jupyter Notebook 1maxbortone / spiking-space-radio
Classification of radio signals on a neuromorphic chip in space
Language:Jupyter Notebook 1- Language:Jupyter Notebook 1
uni-courses / snncompare
Runs networkx graphs representing spiking neural networks of LIF-neurons on lava-nc or networkx.
Language:Python 1dancasmor / A-bio-inspired-implementation-of-a-sparse-learning-spike-based-hippocampus-memory-model
A fully-functional sparse-learning spike-based bio-inspired hippocampal memory model implemented with SNN technology in the SpiNNaker hardware
Language:Python 0Bioinspired memories in the CA3 region of the hippocampus implemented with SNN technology in the SpiNNaker hardware.
Language:Python 0- Language:HTML 0
