Computational Neuroscience
Computational Neuroscience by Eric L. Schwartz, published by MIT Press in August 1993, is a revised edition comprising 441 pages. This book presents a comprehensive exploration of computational neuroscience, defining the intersection of computer science and neuroscience through thirty original contributions. It offers historical and philosophical overviews alongside summaries of recent findings, addressing the structure of the brain and the computational challenges involved in understanding it at various levels.
Readers will find discussions on synaptic-level structures, including dendritic branches and synapses, and their relation to biophysics and neural network simulations. The book also delves into network-level phenomena such as preattentive perception and learning models, while map-level structures are examined through examples like bat echolocation and primate visual cortex. System-level analyses cover topics such as VLSI models of vision and neuropsychological studies, providing a thorough understanding of the complexities within the field of computational neuroscience.
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The thirty original contributions in this book provide a working definition of”computational neuroscience” as the area in which problems lie simultaneously within computerscience and neuroscience. They review this emerging field in historical and philosophical overviewsand in stimulating summaries of recent results. Leading researchers address the structure of thebrain and the computational problems associated with describing and understanding this structure atthe synaptic, neural, map, and system levels.The overview chapters discuss the early days of thefield, provide a philosophical analysis of the problems associated with confusion between brainmetaphor and brain theory, and take up the scope and structure of computationalneuroscience.Synaptic-level structure is addressed in chapters that relate the properties ofdendritic branches, spines, and synapses to the biophysics of computation and provide a connectionbetween real neuron architectures and neural network simulations.The network-level chapters take upthe preattentive perception of 3-D forms, oscillation in neural networks, the neurobiologicalsignificance of new learning models, and the analysis of neural assemblies and local learningrides.Map-level structure is explored in chapters on the bat echolocation system, cat orientationmaps, primate stereo vision cortical cognitive maps, dynamic remapping in primate visual cortex, andcomputer-aided reconstruction of topographic and columnar maps in primates.The system-level chaptersfocus on the oculomotor system VLSI models of early vision, schemas for high-level vision,goal-directed movements, modular learning, effects of applied electric current fields on corticalneural activity neuropsychological studies of brain and mind, and an information-theoretic view ofanalog representation in striate cortex.Eric L. Schwartz is Professor of Brain Research and ResearchProfessor of Computer Science, Courant Institute of Mathematical Sciences, New York UniversityMedical Center. Computational Neuroscience is included in the System Development FoundationBenchmark Series.
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