000			02071nam a22003138i 4500
001			CR9780511975899
003			UkCbUP
005			20200124160250.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101011s2011||||enk o ||1 0|eng|d
020			_a9780511975899 (ebook)
020			_z9780521877954 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	4	_aQP357.5 _b.S84 2011
082	0	0	_a612.801/13 _222
100	1		_aSterratt, David, _d1973- _eauthor.
245	1	0	_aPrinciples of computational modelling in neuroscience / _cDavid Sterratt [and three others].
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (xi, 390 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520			_aThe 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.
650		0	_aComputational neuroscience.
776	0	8	_iPrint version: _z9780521877954
856	4	0	_uhttps://doi.org/10.1017/CBO9780511975899
999			_c519405 _d519403