MARC Record from Library of Congress

LEADER: 03532cam a22003974a 4500
001 2011001055
003 DLC
005 20111109145337.0
008 110110s2011 enka b 001 0 eng
010 $a 2011001055
016 7 $a101569919$2DNLM
020 $a9780521877954 (hbk.)
035 $a(OCoLC)ocn690090171
040 $aDLC$cDLC$dYDX$dBTCTA$dYDXCP$dUKMGB$dBWX$dMIX$dNLM$dCDX$dDLC
042 $apcc
050 00 $aQP357.5$b.P75 2011
060 00 $a2011 J-499
060 10 $aWL 20
082 00 $a612.801/13$222
084 $aMED057000$2bisacsh
245 00 $aPrinciples of computational modelling in neuroscience /$cDavid Sterratt ... [et al.].
260 $aCambridge ;$aNew York :$bCambridge University Press,$c2011.
300 $axi, 390 p. :$bill. (some col.) ;$c26 cm.
520 $a"The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signalling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modelling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience"--$cProvided by publisher.
520 $a"This book is about how to construct and use computational models of specific parts of the nervous system, such as a neuron, a part of a neuron or a network of neurons. It is designed to be read by people from a wide range of backgrounds from the biological, physical and computational sciences. The word 'model' can mean different things in different disciplines, and even researchers in the same field may disagree on the nuances of its meaning. For example, to biologists, the term 'model' can mean 'animal model'; to physicists, the standard model is a step towards a complete theory of fundamental particles and interactions. We therefore start this chapter by attempting to clarify what we mean by computational models and modelling in the context of neuroscience. Before giving a brief chapter-by-chapter overview of the book, we also discuss what might be called the philosophy of modelling: general issues in computational modelling that recur throughout the book"--$cProvided by publisher.
504 $aIncludes bibliographical references (p. [351]- and index.
650 0 $aComputational neuroscience.
650 12 $aModels, Neurological.
650 22 $aComputer Simulation.
650 22 $aNeural Conduction.
650 22 $aSynaptic Transmission.
700 1 $aSterratt, David,$d1973-
856 42 $3Cover image$uhttp://assets.cambridge.org/97805218/77954/cover/9780521877954.jpg
856 42 $3Contributor biographical information$uhttp://catdir.loc.gov/catdir/enhancements/fy1106/2011001055-b.html
856 42 $3Publisher description$uhttp://catdir.loc.gov/catdir/enhancements/fy1106/2011001055-d.html
856 41 $3Table of contents only$uhttp://catdir.loc.gov/catdir/enhancements/fy1106/2011001055-t.html