LEADER 02248cam a22002774a 4500 001 991003599359707536 008 190129s2011 enka b 001 0 eng d 020 $a9780521877954 (hbk.) 035 $ab14358001-39ule_inst 040 $aBibl. Dip.le Aggr. Matematica e Fisica - Sez. Fisica$beng 082 04$a612.801/13$222 084 $aLC QP357.5 245 00$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 504 $aIncludes bibliographical references (p. [351]- and index 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 650 4$aComputational neuroscience 700 1 $aSterratt, David$eauthor$4http://id.loc.gov/vocabulary/relators/aut$0785812 907 $a.b14358001$b26-11-19$c29-01-19 912 $a991003599359707536 945 $aLE006 617.7 STE$g1$i2006000104050$lle006$on$pE57.35$q-$rl$s- $t0$u0$v0$w0$x0$y.i15881854$z07-03-19 945 $aLE006 617.7 STE$g1$i2006000187121$lle006$op$pE55.77$q-$rl$s- $t0$u1$v0$w1$x0$y.i15908574$z26-11-19 996 $aPrinciples of computational modelling in neuroscience$91749544 997 $aUNISALENTO 998 $ale006$b29-01-19$cm$da $e $feng$genk$h0$i0