Neuroscience for Artificial Intelligence 1st Edition

Neuroscience for Artificial Intelligence, 1st Edition by Huijue Jia is a concise, research-driven guide that connects biological principles of the brain with cutting-edge AI techniques. Designed for curious students, engineers, and researchers, this book translates complex neuroscience into practical insights that can improve machine learning models and inspire new computational architectures.

Beginning with clear explanations of neural anatomy, synaptic plasticity, and neural coding, the text moves quickly into computational models—spiking neural networks, recurrent dynamics, and biologically inspired learning rules. Each chapter balances theory with application: mathematical foundations are made accessible, case studies highlight recent breakthroughs, and comparisons to conventional deep learning demonstrate where brain-inspired methods offer efficiency, robustness, or interpretability advantages.

What sets this 1st Edition apart is its practical orientation. Readers will learn how principles such as temporal coding, sparse representations, and homeostatic regulation can inform model design, optimize energy use, and enhance generalization. Whether you are developing neuromorphic hardware, refining architecture for edge devices, or exploring explainable AI, Jia provides the conceptual tools needed to translate neuroscience into engineering outcomes.

Internationally relevant and classroom-ready, this volume is well-suited for graduate courses and professional reference collections across North America, Europe, and Asia. Carefully curated references and a structured progression make it simple to integrate into curricula or self-study plans.

Add Neuroscience for Artificial Intelligence by Huijue Jia to your library for a rigorous, accessible bridge between brain science and artificial intelligence—an essential resource for anyone serious about next-generation AI.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.