Mathematical Foundations Of Neuroscience
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Mathematical Foundations of Neuroscience
Author: G. Bard Ermentrout
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-07-01
This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University.
From Computer to Brain
Author: William W. Lytton
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-05-08
Biology undergraduates, medical students and life-science graduate students often have limited mathematical skills. Similarly, physics, math and engineering students have little patience for the detailed facts that make up much of biological knowledge. Teaching computational neuroscience as an integrated discipline requires that both groups be brought forward onto common ground. This book does this by making ancillary material available in an appendix and providing basic explanations without becoming bogged down in unnecessary details. The book will be suitable for undergraduates and beginning graduate students taking a computational neuroscience course and also to anyone with an interest in the uses of the computer in modeling the nervous system.
Spikes, Decisions, and Actions
The nervous system of higher animals is very complex and highly nonlinear. among its many capabilities are making decisions and carrying out complex motor actions such as swimming. Nonlinear dynamical modelling can be used to understand and explain neural phenomena at many different levels, including - ion-currents and action potentials; short - and long - term memory; visual hallucinations; neural synchronization; motor control This book explores the mathematical principles by which brains generate spikes, make decisions, store memories, and control actions. It assumes a basic knowledge of calculus and develops the dynamical foundations of neuroscience using problem sets and computer simulations on the accompanying PC and Mac compatible MatLab disk.