Introduction to Neuromorphic Computing by Shriram Ramanathan (Hardback)

£54.99
Available
Product Ref. (e.g. ISBN, SKU)
9781009564342
Published:
30 Nov 2025
Format:
Hardback
Number of pages:
350 pages
Weight:
0.000kgs
This book provides an accessible introduction to neuromorphic computing, covering fundamental principles and emerging challenges. It's an excellent resource for students and researchers in engineering, physics, AI and computer science, as well as technologists that are interested in this dynamic interdisciplinary field.
Artificial intelligence is transforming industries and society, but its high energy demands challenge global sustainability goals. Biological intelligence, in contrast, offers both good performance and exceptional energy efficiency. Neuromorphic computing, a growing field inspired by the structure and function of the brain, aims to create energy-efficient algorithms and hardware by integrating insights from biology, physics, computer science, and electrical engineering. This concise and accessible book delves into the principles, mechanisms, and properties of neuromorphic systems. It opens with a primer on biological intelligence, describing learning mechanisms in both simple and complex organisms, then turns to the application of these principles and mechanisms in the development of artificial synapses and neurons, circuits, and architectures. The text also delves into neuromorphic algorithm design, and the unique challenges faced by algorithmic researchers working in this area. The book concludes with a selection of practice problems, with solutions available to instructors online.

About the Author: Shriram Ramanathan is Rodkin-Weintraub Chair in Engineering at Rutgers University's College of Engineering. He previously held faculty positions at Purdue University and Harvard University, and was a research staff member at Components Research, Intel. Shriram's research focuses on adaptive semiconductors for neuromorphic computing and artificial intelligence, driving innovation at the intersection of materials science and next-generation AI technologies. He currently teaches the pioneering course 'Semiconductors for AI' at Rutgers.

More Information
Number of pages 350

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