8 Best-Selling Neuromorphic Computing Books Millions Trust

After more than two decades of hands-on research in event-based sensors and asynchronous circuits, Shih-Chii Liu and her co-authors developed this book to fill a critical gap in neuromorphic engineering literature. You’ll learn how the brain’s data-driven communication inspires the design of efficient electronic sensors and processors, with detailed chapters on vision and auditory systems, neuronal processing, and learning circuits. The text walks you through building scalable multi-chip systems and handling real-world operational challenges, making it especially useful if you’re involved in advanced electrical engineering or computational neuroscience. If you want a rigorous yet approachable guide that ties historical developments to current architectures, this book delivers without fluff.

During his tenure at the University of Manchester, Steve Furber developed a neuromorphic computing platform that rethinks how artificial neural networks emulate biological brains. You’ll explore the SpiNNaker machine’s architecture, which leverages over a million ARM processors to simulate spiking neural networks in real time, matching the scale of a mouse brain. The book walks you through the machine’s conception, software innovations, and real-world applications like the "Talk" robotic exhibit and stochastic problem-solving. If you’re involved in neural engineering, AI research, or advanced computing systems, this text offers a detailed technical narrative about building and deploying one of the largest neuromorphic projects worldwide.

This tailored book explores battle-tested neuromorphic computing methods designed to address complex system design challenges. By focusing on your individual interests and background, it covers core principles such as spiking neural networks, event-driven architectures, and energy-efficient hardware implementations. The book examines how these methods integrate to solve real-world problems, revealing nuanced approaches that align with your specific goals. This personalized guide offers a unique combination of widely trusted knowledge and a custom exploration of reader-validated insights, enabling you to deepen your understanding and apply neuromorphic concepts effectively within your projects or research.

What started as an effort to bridge photonic device physics with neural network models became a detailed exploration of neuromorphic photonics. Paul R. Prucnal and Bhavin J. Shastri guide you through the evolution from fiber-optic neurons to cutting-edge integrated laser neurons, offering insights into device architectures and learning functionalities at the intersection of photonics and neural computation. You’ll develop a clear understanding of how physical photonic components can mimic neural behaviors and how this field is shaping next-generation computing. This book suits graduate students diving into neuromorphic research and professionals seeking a thorough technical reference without unnecessary complexity.

What if everything you knew about building neural network hardware was incomplete? Nan Zheng and Pinaki Mazumder explore how tightly coupling algorithm design with hardware architecture can dramatically improve energy efficiency in neuromorphic computing. You’ll learn about rate-based and spiking neural networks, hardware accelerators ranging from digital to analog, and emerging nanotechnologies like memristors. The book balances foundational theory with concrete design examples, such as adaptive dynamic programming accelerators, making it suited for engineers and researchers aiming to reduce power consumption without compromising learning capability.

Drawing from detailed hardware design expertise, Sanjay Kumar Boddhu presents a thorough exploration of implementing neuromorphic computing through a reconfigurable continuous time recurrent neural network. You’ll gain insight into how off-the-shelf components can be assembled to replicate neural network dynamics accurately, with tested configurations that align hardware behavior closely to theoretical models. The book carefully explains the underlying differential equations and their translation into physical circuitry, making it accessible even if you’re not an electrical engineering expert. This work benefits researchers and engineers looking for practical hardware frameworks to experiment with neuromorphic systems rather than purely theoretical treatments.

This personalized book explores the focused journey of accelerating neuromorphic coding skills within 30 days, tailored to your unique background and goals. It covers fundamental concepts such as spiking neural networks and hardware architectures, then advances through hands-on coding exercises and real-world applications. By combining widely validated knowledge with your specific interests, the book reveals a step-by-step pathway to deepen your understanding and enhance practical coding abilities. This tailored approach ensures you spend time only on the most relevant topics, maximizing learning efficiency and engagement. Readers gain an immersive experience that bridges theory and practice in neuromorphic programming.

What if the way you think about analog circuit design was turned on its head? Oliver Landolt, blending deep knowledge in neurobiology and electronic engineering, explores how the brain's place coding concept can inspire new analog VLSI circuits. You gain insight into representing information spatially within integrated circuits, including how networks of links can implement complex functions with remarkable noise tolerance and energy efficiency. For example, the book details three integrated circuits ranging from 80 to 1800 links, showcasing practical applications like active vision systems. This book suits engineers and researchers keen on bio-inspired hardware design and those curious about bridging neuroscience with circuit technology.

Dr. Elishai Ezra Tsur, with his extensive interdisciplinary expertise spanning life sciences, computer science, and computational neuroscience, offers a nuanced exploration of brain-inspired computing. You’ll gain insight into how neuromorphic engineering challenges the traditional digital computer model by presenting alternative architectures inspired by neural processes, covering neuronal modeling, neuromorphic circuits, and event-based communication. This book suits scientists, algorithm designers, and computer architects seeking a deeper understanding of cognitive hardware and software ecosystems that could redefine computing performance beyond von Neumann architectures. Its multi-perspective approach enables you to appreciate the varied dimensions of building machines with cognitive capabilities.

Drawing from over 25 years in cognitive neuroscience and machine learning, Daniel M. Rice presents a detailed exploration of logistic regression adapted to mimic neural processing. You’ll discover how the reduced error logistic regression (RELR) method addresses challenges like high dimensionality and cognitive bias, which often complicate predictive modeling in human behavior. The book delves into parallels between machine learning and explicit and implicit brain functions, offering insights grounded in both neuroscience and statistics. Particularly, chapters on RELR’s application beyond traditional logistic regression reveal its potential to improve both explanation and prediction without introducing subjective bias. This book suits data scientists and cognitive researchers interested in integrating biological principles into analytic models.