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A self-contained introduction to fundamental theories and basic methods for computational neuroscientists
Keeps mathematical concepts as intuitive and simple as possible and provides routes of explanation accessible also to readers who are less familiar with mathematics
Represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience
Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in neuroscience with backgrounds in biology, psychology and medicine, takes its readers on a journey through three fundamental domains of computational neuroscience: membrane biophysics, systems theory and artificial neural networks. The required mathematical concepts are kept as intuitive and simple as possible throughout the book, making it fully accessible to readers who are less familiar with mathematics. Overall, Computational Neuroscience - A First Course represents an essential reference guide for all neuroscientists who use computational methods in their daily work, as well as for any theoretical scientist approaching the field of computational neuroscience.
Content Level »Research
Keywords »Artificial neural networks - Associative memory - Fourier Analysis for Neuroscientists - Membrane potentials - Neural Cods and Popuulation Codes - Nonlinearities in receptive fields - Receptive Fields - Retinotopic mapping - Self-organization and competitive learning