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.

The book presents neuromorphic engineering from three perspectives: the scientist, the computer architect, and the algorithm designer. It zooms in and out of the different disciplines, allowing readers with diverse backgrounds to understand and appreciate the field. Overall, the book covers the basics of neuronal modeling, neuromorphic circuits, neural architectures, event-based communication, and the neural engineering framework. 

section I|58 pages

Introduction and Overview

chapter 2Chapter 1|22 pages

Introducing the perspective of the scientist

chapter Chapter 2|24 pages

Introducing the perspective of the computer architect

chapter Chapter 3|10 pages

Introducing the perspective of the algorithm designer

section II|78 pages

The Scientist's Perspective

chapter 60Chapter 4|10 pages

Biological description of neuronal dynamics

chapter Chapter 5|28 pages

Models of point neuronal dynamic

chapter Chapter 6|16 pages

Models of morphologically detailed neurons

chapter Chapter 7|22 pages

Models of network dynamic and learning

section III|48 pages

The Computer Architect's Perspective

chapter 138Chapter 8|28 pages

Neuromorphic hardware

chapter Chapter 9|10 pages

Communication and hybrid circuit design

chapter Chapter 10|8 pages

In-memory computing with memristors

section IV|72 pages

The Algorithms Designer's Perspective

chapter 186Chapter 11|10 pages

Introduction to neuromorphic programming

chapter Chapter 12|44 pages

The Neural Engineering Framework (NEF)

chapter Chapter 13|16 pages

Learning spiking neural networks