This repository is a comprehensive guide to Neuromorphic Computing, targeting engineers, researchers, and students interested in brain-inspired computing architectures. It provides a structured overview of applications, developer resources, training materials, books, and tools relevant to the field, aiming to enhance efficiency and knowledge in neuromorphic development.

How It Works

Neuromorphic computing aims to mimic the neuro-biological architectures of the human brain using Very Large Scale Integration (VLSI) systems with analog circuits. This approach seeks to achieve significant gains in energy efficiency and speed compared to conventional computing architectures by processing information in a manner analogous to biological neurons and synapses.

Quick Start & Requirements

This repository is a curated collection of information and does not involve direct installation or execution. It serves as a knowledge base.

Highlighted Details

• Extensive lists of online courses, books, and YouTube videos for learning neuromorphic computing and related fields like computational neuroscience and PyTorch.
• Detailed sections on various algorithms (Fuzzy Logic, SVM, Neural Networks, CNNs, RNNs, MLPs, Random Forests, Decision Trees, Naive Bayes) and their applications.
• A broad catalog of tools, libraries, and frameworks for Machine Learning, Deep Learning, Reinforcement Learning, Computer Vision, NLP, Bioinformatics, and Robotics, with a strong emphasis on Python-based ecosystems.
• Coverage of hardware-specific development, including CUDA, MATLAB, and C/C++ development, with links to relevant toolkits and libraries.

Maintenance & Community

The guide is maintained by mikeroyal, with contributions encouraged via Pull Requests. Specific community links or active maintenance signals are not detailed in the README.

Licensing & Compatibility

The content is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) Public License, allowing for broad use and adaptation.

Limitations & Caveats

This guide is a curated resource and does not provide executable code or direct support for any specific neuromorphic hardware or software. Its value lies in its comprehensive listing of learning materials and tools.