This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models.
It is now clear that the brain is unlikely to be understood without recourse to computational theories. The theme of An Introduction to Natural Computation is that ideas from diverse areas such as neuroscience, information theory, and optimization theory have recently been extended in ways that make them useful for describing the brains programs. This book provides a comprehensive introduction to the computational material that forms the underpinnings of the currently evolving set of brain models. It stresses the broad spectrum of learning models―ranging from neural network learning through reinforcement learning to genetic learning―and situates the various models in their appropriate neural context.
To write about models of the brain before the brain is fully understood is a delicate matter. Very detailed models of the neural circuitry risk losing track of the task the brain is trying to solve. At the other extreme, models that represent cognitive constructs can be so abstract that they lose all relationship to neurobiology. An Introduction to Natural Computation takes the middle ground and stresses the computational task while staying near the neurobiology.
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Dana H. Ballard is Professor in the Department of Computer Sciences at the University of Texas at Austin, where he has appointments in Psychology, the Institute for Neuroscience, and the Center for Perceptual Systems. He is the author of An Introduction to Natural Computation (MIT Press).
I am EXTREMELY enthusiastic about this work. This is the first such book I've seeb that comes even close to covering the topic. I'd love to use it for teahing.
―Alex (Sandy) Pentland, Academic Head, The Media Lab, M.I.T. Toshiba Professor of Media Arts and Sciences"About this title" may belong to another edition of this title.
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