Mathematical foundations of neuroscience
 Responsibility
 G. Bard Ermentrout, David H. Terman.
 Language
 English.
 Imprint
 New York : Springer, c2010.
 Physical description
 xv, 422 p. : ill. (some col.) ; 24 cm.
 Series
 Interdisciplinary applied mathematics v. 35.
Access
Available online
Math & Statistics Library
Stacks
Call number  Status 

QP357.5 .E76 2010  Ask at circulation desk 
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Creators/Contributors
 Author/Creator
 Ermentrout, Bard.
 Contributor
 Terman, David H. (David Hillel)
Contents/Summary
 Bibliography
 Includes bibliographical references (p. 407418) and index.
 Contents

 The HodgkinHuxley Equations. Dendrites. Dynamics. The Variety of Channels. Bursting Oscillations. Propagating Action Potentials. Synaptic Channels. Neural Oscillators: Weak Coupling. Neuronal Networks: Fast/Slow Analysis. Noise. Firing Rate Models. Spatially Distributed Networks.
 (source: Nielsen Book Data)9780387877075 20160605
 Publisher's Summary
 One cansay that the ?eld ofcomputationalneurosciencestarted with the 1952paper ofHodgkinandHuxleyin whichtheydescribe, throughnonlinearpartialdifferential equations, the genesis of the action potential in the giant axon of the squid. These equations and the methods that arose from this combination of modeling and  periments have since formed the basis for nearly every subsequent model for active cells.TheHodgkinHuxleymodelandahostofsimpli?edequationsthatared erived fromit haveinspiredthedevelopmentofnewandbeautifulmathematics.Dynamical systems and computational methods are now being used to study activity patterns in a variety of neuronal systems. It is becoming increasingly recognized, by both experimentalists and theoreticians, that issues raised in neuroscience and the ma ematical analysis of neuronal models provide unique interdisciplinary collaborative research and educational opportunities. This book is motivated by a perceived need for an overview of how dynamical systems and computational analysis have been used in understanding the types of models that come out of neuroscience. Our hope is that this will help to stimulate an increasing number of collaborations between mathematicians and other th reticians, looking for interesting and relevant problems in applied mathematics and dynamical systems, and neuroscientists, looking for new ways to think about the biological mechanisms underlying experimental data. The book arose out of several courses that the authors have taught. One of these is a graduate course in computational neuroscience that has students from the d ciplines of psychology, mathematics, computer science, physics, and neuroscience.
(source: Nielsen Book Data)9780387877075 20160605
Bibliographic information
 Publication date
 2010
 Series
 Interdisciplinary applied mathematics, 09396047 ; v. 35
 ISBN
 9780387877075
 038787707X
 9780387877082 (eISBN)
 0387877088 (eISBN)