Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications: 9781609600211: Computer Science & IT Books

Publication Status: E-Book and Print Version Available for Purchase

ISBN13: 9781609600211

EISBN13: 9781609600235

DOI: 10.4018/978-1-60960-021-1

Description:

In recent years there has been increased interest in developing computational and mathematical models of learning and adaptation.

Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications captures the latest research in this area, providing a learning theorists with a mathematically sound framework within which evaluate their models. The significance of this book lies in its theoretical advances, which are grounded in an understanding of computational and biological learning. The approach taken moves the entire field closer to a watershed moment of learning models, through the interaction of computer science, psychology and neurobiology.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

Analysis of agent behavior
Artificial neural systems
Associative learning and memory
Computational modals of learning
Computational Modeling
Neural computation
Neural-symbolic processing
Pavlovian conditioning
Reinforcement learning in the brain
Temporary uncertainty

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Reviews

"This is a great reference for readers interested in advanced computational neuroscience and artificial intelligence. It is full of research findings, along with hundreds of references to follow up on. It contains both theoretical models and applications. The figures and tables are extremely helpful. The book is written at a very advanced level, but it will benefit readers with an extensive background in neuroscience."

– Gary B Kaniuk, Psy.D., Cermak Health Services, Doody's Book Review

This book is different from others in that we have not sought the collaboration of computational neuroscientists who dwell in middle ground, that is, of computer scientists who have never set foot in a lab or neuro-scientists who use but barely understand the complexities of computational models. But rather of specialists in neuro-science and computer science who for one reason or another are compelled to crisscross their areas of expertise.|

– Eduardo Alonso, City University, UK; and Esther Mondragón, University College London, UK

Eduardo Alonso is a Senior Lecturer at City University London. He is an expert on Artificial Intelligence in particular on the interdisciplinary bridges between machine learning and animal learning. He has published dozens of papers and contributions to Artificial Intelligence volumes (e.g., in The Cambridge Handbook of Artificial Intelligence, to appear in 2010, ISBN-10: 0521871425). His survey paper "AI and Agents: State of the Art", AI Magazine 23(3): Fall 2002, 25-30, is still recommended as a general reading at AAAI's AI Topics-Agents. He is the Public Understanding Officer of The Society for the Study of Artificial Intelligence and the Simulation of Behaviour, the eldest learned Artificial Intelligence society in Europe, and a member of the Society for Computational Modeling of Associative Learning. He is also a member of the EPSRC College.

Esther Mondragón held several research positions at the Department of Psychology at the University of York and at the Cognitive, Perceptual and Brain Sciences Unit at University College London. Her research focuses on Behavioural Neuroscience, specializing in the study of animal learning and cognition from the theoretical background of associative models of conditioning. She has published her work in, among others, Science, Learning and Behavior, and The Quarterly Journal of Experimental Psychology. She contributed to the book Occasion Setting (APA, 1998). Recently, she founded the Centre for Computational and Animal Learning Research that she co-chairs.

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