Authors: Mrs. B. Jyothi, Dr. Kuraku Nirmala, Mrs. G. Sailaja, Mrs. B. Ammanni

ISBN: 978-81-69857-50-5

DOI: https://doi.org/10.59646/797

Date of Publication: August 21, 2026

Cite this book: B. Jyothi, Kuraku N, G. Sailaja, B. Ammanni, (2026), Neuromorphic Computing: Brain-Inspired Architectures and Systems, San International Scientific Publications, ISBN: 978-81-69857-50-5, DOI: https://doi.org/10.59646/797

Preface

Neuromorphic Computing: Brain-Inspired Architectures and Systems presents a comprehensive exploration of computing paradigms inspired by the structure, dynamics, and information-processing principles of the biological brain. Conventional computing architectures rely predominantly on sequential, clock-driven processing and physically separated memory and computation, whereas neuromorphic computing seeks to emulate the highly parallel, adaptive, event-driven, and energy-efficient characteristics of biological neural systems. The rapid development of artificial intelligence, edge computing, robotics, intelligent sensing, and autonomous systems has created an increasing demand for computational architectures capable of processing complex information with low latency and significantly reduced energy consumption. Neuromorphic computing addresses these requirements by combining neuroscience principles, neural computation models, specialized hardware, learning mechanisms, and event-based information processing.

The book begins with the neuroscience foundations for computing, establishing the biological concepts required to understand brain-inspired computational systems. The structure and function of neurons, synaptic transmission, plasticity, neural coding, biological learning, brain regions, spiking behavior, and neural connectivity are examined to demonstrate how biological systems encode, transmit, and transform information. These foundations provide the conceptual basis for understanding how principles observed in biological neural networks can be translated into computational models and hardware architectures.

The subsequent unit introduces artificial neural networks and spiking models, with particular emphasis on Spiking Neural Networks (SNNs) as a computational framework for representing temporal and event-driven information. Traditional Artificial Neural Networks are compared with SNNs to clarify differences in representation, computation, learning, temporal dynamics, and energy efficiency. Fundamental neuron models, including the Leaky Integrate-and-Fire and Hodgkin–Huxley models, are discussed together with temporal coding, spike-based processing, and training algorithms for SNNs. This provides a mathematical and computational foundation for designing intelligent systems based on discrete neural events.

A major focus of the book is neuromorphic hardware architecture, where biological principles are translated into physical computing platforms. Neuromorphic chips and processors, analog and digital implementations, event-driven architectures, memory–computation integration, low-power circuit design, FPGA and ASIC implementations, and brain-inspired hardware platforms are explored. Particular attention is given to architectures that minimize unnecessary computation and data movement, thereby improving energy efficiency and enabling real-time intelligent processing at the edge.

The book further examines learning mechanisms and neural plasticity, including Hebbian learning, Spike-Timing Dependent Plasticity (STDP), unsupervised learning, reinforcement learning, online adaptation, and hardware-oriented learning rules. These mechanisms are essential for developing neuromorphic systems that can learn continuously from their environment rather than depending exclusively on centralized offline training. The discussion is extended to system design and integration, covering sensor and actuator interfaces, event-based data processing, communication protocols, real-time computation, and embedded neuromorphic systems.

To support practical development and experimentation, the book also addresses modeling, simulation, and software tools used in neuromorphic computing. Simulation frameworks, software platforms, hardware–software co-simulation, data-driven modeling, neural activity visualization, debugging, and testing techniques are introduced to bridge theoretical models with implementable systems. These concepts enable readers to evaluate neural dynamics, optimize architectures, and investigate system behavior before deployment on specialized hardware.

Finally, the book explores diverse applications of neuromorphic computing, including pattern recognition, classification, robotics, autonomous systems, Edge AI, Internet of Things devices, brain–computer interfaces, speech and vision processing, smart sensing, signal processing, and healthcare and biomedical technologies. By integrating neuroscience, artificial intelligence, computational modeling, electronic hardware, embedded systems, and intelligent applications, this book provides a unified perspective of neuromorphic computing as an emerging technological paradigm. It is intended to serve students, researchers, engineers, academicians, and technology professionals seeking a strong conceptual and technical foundation for understanding, designing, and applying brain-inspired computing systems.