Название: Neuromorphic Engineering; The Scientist’s, Algorithm Designer’s, and Computer Architect’s Perspectives on Brain-Inspired Computing
Автор: Elishai Ezra Tsur
Издательство: CRC Press
Год: 2022
Страниц: 330
Язык: английский
Формат: pdf (true)
Размер: 72.9 MB

The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness traveling in cyberspace. However, studying the brain's core computation architecture can inspire scientists, computer architects, and algorithm designers to think fundamentally differently about their craft.

Neuromorphic engineers have the ultimate goal of realizing machines with some aspects of cognitive intelligence. They aspire to design computing architectures that could surpass existing digital von Neumann-based computing architectures' performance. In that sense, brain research bears the promise of a new computing paradigm. As part of a complete cognitive hardware and software ecosystem, neuromorphic engineering opens new frontiers for neuro-robotics, artificial intelligence, and supercomputing applications.

This book will present neuromorphic engineering from three perspectives: the scientist, the computer architect, and the algorithm designer. We will zoom in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field. Overall, the book will cover the basics of neuronal modeling, neuromorphic circuits, neural architectures, event-based communication, and the neural engineering framework. Readers will have the opportunity to understand the different views over the inherently multidisciplinary field of neuromorphic engineering.

Table of Contents:
Part 1. Introduction and overview. 1. The Scientist Perspective. 2. Introducing the perspective of the computer architect. 3. Introducing the perspective of the algorithm designer. Part 2. The Scientist's Perspective. 4. Biological description of neuronal dynamic. 5. Models of point neuronal dynamic. 6. Models of morphologically detailed neurons. 7. Models of network dynamic and learning. Part 3. The Computer Architect's Perspective. 8. Neuromorphic hardware. 9. Communication and hybrid circuit design. 10. In-memory computing with memristors. Part 4. The Algorithms Designer's Perspective. 11. Introduction to neuromorphic programming. 12. The Neural Engineering Framework (NEF). 13. Learning spiking neural networks.