Synopsis
"This is a wonderful book that brings together in one place the modern view of computation as found in nature. It is well written and has something for everyone from the undergraduate to the advanced researcher." -- Terrence J. Sejnowski, Howard Hughes Medical Institute at The Salk Institute for Biological Studies, La Jolla, California 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 brain's 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. The material is accessible to advanced undergraduates as well as beginning graduate students.
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About the Author
Dana Ballard is Professor in the Department of Computer Science and the Department of Brain and Cognitive Sciences at the University of Rochester.
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An Introduction to Natural Computation (Complex Adaptive Systems)
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