Neuromorphic Computing Guide

A guide covering Neuromorphic Computing including the applications, libraries and tools that will make you better and more efficient with Neuromorphic Computing development.

Note: You can easily convert this markdown file to a PDF in VSCode using this handy extension Markdown PDF. Also, checkout the mdBook version Neuromorphic Computing Guide mdBook (Special thanks to jonathanwoollett-light).

Types of Neural Networks

Table of Contents

1. Getting Stat\rted with Neuromorphic Computing

   • Developer Resources
   • Online Training Courses
   • Books
   • YouTube videos

2. Neuromorphic Computing Tools, Libraries, and Frameworks

3. Algorithms

4. Machine Learning

5. Deep Learning Development

6. Reinforcement Learning Development

7. Computer Vision Development

8. Natural Language Processing (NLP) Development

9. Bioinformatics

10. Robotics Development

11. Electric charge, field, and potential

    • Charge and electric force (Coulomb's law): Electric charge, field, and potential
    • Electric field: Electric charge, field, and potential
    • Electric potential energy, electric potential, and voltage: Electric charge, field, and potential

12. Circuits

    • Ohm's law and circuits with resistors: Circuits
    • Circuits with capacitors: Circuits

13. Magnetic forces, magnetic fields, and Faraday's law

    • Magnets and Magnetic Force: Magnetic forces, magnetic fields, and Faraday's law
    • Magnetic field created by a current: Magnetic forces, magnetic fields, and Faraday's law
    • Electric motors: Magnetic forces, magnetic fields, and Faraday's law
    • Magnetic flux and Faraday's law

14. Electromagnetic waves and interference

    • Introduction to electromagnetic waves: Electromagnetic waves and interference
    • Interference of electromagnetic waves

15. CUDA Development

16. MATLAB Development

17. Python Development

18. C/C++ Development

Getting Started with Neuromorphic Computing

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Neuromorphic Computing is the use of very large scale integration (VLSI) systems containing electronic analog circuits to simulate the neuro-biological architectures present in the human brain ad nervous system.

Intel Loihi 2, its second-generation neuromorphic research chip.

The Akida Neuromorphic System-on-Chip (NSoC) developed by BrainChip.

Developer Resources

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• Neuromorphic Computing - Next Generation of AI | Intel

• Next-Level Neuromorphic Computing: Intel Lab's Loihi 2 Chip | Intel

• Neuromorphic Computing | NIST

• Programming and Usability of Neuromorphic Computing | ORNL

• Neuromorphic Computing | EBRAINS Research Infrastructure

• Neuromorphic devices & systems | IBM Research Zurich

• Light-Emitting Artificial Synapses for Neuromorphic Computing (Research Paper PDF)

Online Training Courses

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• Top Computational Neuroscience Courses Online | Coursera

• Computational Neuroscience Course Online | Coursera

• Computational Neuroscience: Neuronal Dynamics of Cognition Course Online | edX

• Fundamentals of Neuroscience, Part 1: The Electrical Properties of the Neuron | Harvard Online Learning

• Fundamentals of Neuroscience, Part 2: Neurons and Networks | Harvard Online Learning

• Fundamentals of Neuroscience, Part 3: The Brain | Harvard Online Learning

• Introduction to Computational Neuroscience | MIT OpenCourseWare

• Brain and Cognitive Sciences Online Course | MIT OpenCourseWare

• Getting Started with PyTorch

• Top Pytorch Courses Online | Coursera

• Top Pytorch Courses Online | Udemy

• Learn PyTorch with Online Courses and Classes | edX

• Intro to Deep Learning with PyTorch | Udacity

• PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure

• Deep Learning with PyTorch | Amazon Web Services (AWS)

• Getting started with PyTorch on Google Cloud

Books

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• Neuromorphic Computing Principles and Organization by Abderazek Ben Abdallah and Khanh N. Dang

• Neuromorphic Computing and Beyond: Parallel, Approximation, Near Memory, and Quantum by Khaled Salah Mohamed

• Neuromorphic Engineering: The Scientist's, Algorithm Designer's, and Computer Architect's Perspectives on Brain-Inspired Computing by Elishai Ezra Tsur

• Memristors for Neuromorphic Circuits and Artificial Intelligence Applications by Jordi Suñé

• Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by Nan Zheng, Pinaki Mazumder

• Neuromorphic Devices for Brain-inspired Computing: Artificial Intelligence, Perception, and Robotics by Qing Wan, Yi Shi

• Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning by Huajin Tang, Kaushik Roy, Lei Deng

• Photo-Electroactive Non-Volatile Memories for Data Storage and Neuromorphic Computing by Ye Zhou, Suting Han

• Neuromorphic Photonics by Bhavin J. Shastri, Paul R. Prucnal

YouTube videos

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• Neuromorphic Computing Explained | Jeffrey Shainline and Lex Fridman

• Brains Behind the Brains: Mike Davies and Neuromorphic Computing at Intel Labs | Intel

• How Neuromorphic Computing Uses the Human Brain as a Model | Intel Labs

• ESWEEK 2021 Education - Introduction to Neuromorphic Computing

• Stanford Seminar: Neuromorphic Chips: Addressing the Nanostransistor Challenge

• Brain-Like (Neuromorphic) Computing - Computerphile

• Photonic Neuromorphic Computing: The Future of AI? | ExplainingComputers

• Machine learning + neuroscience = biologically feasible computing | Benjamin Migliori | TEDxSanDiego

Neuromorphic Computing Tools, Libraries, and Frameworks

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Lava is an open-source software framework for developing neuro-inspired applications and mapping them to neuromorphic hardware. Lava provides developers with the tools and abstractions to develop applications that fully exploit the principles of neural computation. Constrained in this way, like the brain, Lava applications allow neuromorphic platforms to intelligently process, learn from, and respond to real-world data with great gains in energy efficiency and speed compared to conventional computer architectures.

Lava DL is an enhanced version of SLAYER. Some enhancements include support for recurrent network structures, a wider variety of neuron models and synaptic connections (complete list of features here). This version of SLAYER is built on top of the PyTorch deep learning framework, similar to its predecessor.

Lava Dynamic Neural Fields (DNF) are neural attractor networks that generate stabilized activity patterns in recurrently connected populations of neurons. These activity patterns form the basis of neural representations, decision making, working memory, and learning. DNFs are the fundamental building block of dynamic field theory, a mathematical and conceptual framework for modeling cognitive processes in a closed behavioral loop.

Neuromorphic Constraint Optimization is a library of solvers that