About the Book

Neuromorphic computing is an interdisciplinary domain whose origin can be spotted in the late 1980s. It was developed to solve mathematical problems using computers and electronic circuits designed with inspirations from biological principles of the nervous system, and physical principles of electron spin. These systems involve very large-scale integration of analog electronic circuits to replicate the neural architecture of the human brain to solve complex problems. Neuromorphic devices have artificial neurons made of silicon. In terms of hardware, these systems can be synthesized by employing oxide-based memristors, spintronic memories along with traditional electronic components. The possibility of biology-inspired systems emerged because the biological nervous system involves analog chemical signals in the synapse for information transfer between neurons. Achieving precise biological neural structure is practically impossible as current systems work based on digital principles rather than analog. So analogously performing operations in a highly distributed way among small computing elements is the principal focus of neuromorphic computing. Some examples of neuromorphic systems are vision systems, auditory processors, robots, and head-eye systems. Legal and ethical concerns should be ignored because comparing manmade neuromorphic systems with the human brain is like comparing a paper boat with a huge ship.

This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium-scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to perform a network of spiking neurons communication, computing, and adaptive learning for emerging artificial intelligence applications.

The book is mainly devoted to recent results, critical aspects, and perspectives of ongoing research on relevant topics, all involving neuromorphic computing in diverse applications. This book shall help clarify understanding of certain key mechanisms and technologies helpful in realizing such systems.

The features of the book will be:

• Includes an overview of primary scientific concepts for the research topic of neuromorphic computing such as neurons as computational units, artificial intelligence, machine learning, and neuromorphic models.

• Discusses potential applications in computing.

• Presents current trends and models in neuromorphic computing and neural network hardware architectures.

• Development of novel devices and hardware to enable neuromorphic computing.

• Information about Computation and learning principles for neuromorphic systems.

• Presents information about Neuromorphic implementations of neurobiological learning algorithms.

• Biologically inspired neuromorphic systems and devices (including adaptive bio-interfacing and hybrid systems consisting of living matter and synthetic matter).

• Describes and discusses the fundamental design method and organization of neuromorphic architecture.

• Presents a systematic way to design applications algorithms and hardware to achieve the best performance.

Table of Contents (but not limited to)

Paper Submission Email: jyotirmoy.chatterjee.cse@gmail.com or harish.garg@thapar.edu

Important Dates

Book Editors