000			03542nam a22004695i 4500
001			978-0-387-87708-2
003			DE-He213
005			20251006084427.0
007			cr nn 008mamaa
008			100702s2010 xxu| s |||| 0|eng d
020			_a9780387877082
020			_a99780387877082
024	7		_a10.1007/978-0-387-87708-2 _2doi
082	0	4	_a570.285 _223
100	1		_aErmentrout, G. Bard. _eauthor.
245	1	0	_aMathematical Foundations of Neuroscience _h[electronic resource] / _cby G. Bard Ermentrout, David H. Terman.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2010.
300			_aXV, 422p. 38 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInterdisciplinary Applied Mathematics, _x0939-6047 ; _v35
505	0		_aThe Hodgkin-Huxley 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.
520			_aThis 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.
650		0	_aMATHEMATICS.
650		0	_aNEUROSCIENCES.
650		0	_aNEUROBIOLOGY.
650	1	4	_aMATHEMATICS.
650	2	4	_aMATHEMATICAL AND COMPUTATIONAL BIOLOGY.
650	2	4	_aNEUROBIOLOGY.
650	2	4	_aNEUROSCIENCES.
700	1		_aTerman, David H. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387877075
830		0	_aInterdisciplinary Applied Mathematics, _x0939-6047 ; _v35
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-87708-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c59336 _d59336