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010   $a3-642-32157-7
024 7 $a10.1007/978-3-642-32157-3
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035   $a(DE-He213)978-3-642-32157-3
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100   $a20121026d2013      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aStochastic Biomathematical Models$b[electronic resource] $ewith Applications to Neuronal Modeling /$fedited by Mostafa Bachar, Jerry J. Batzel, Susanne Ditlevsen
205   $a1st ed. 2013.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013.
215   $a1 online resource (XVI, 206 p. 34 illus., 13 illus. in color.) 
225 1 $aMathematical Biosciences Subseries,$x2524-6771 ;$v2058
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-642-32156-9 
320   $aIncludes bibliographical references and index.
327   $a1 Introduction to stochastic models in biology -- 2 One-dimensional homogeneous diffusions -- 3 A brief introduction to large deviations theory -- 4 Some numerical methods for rare events simulation and analysis -- 5 Stochastic Integrate and Fire models: a review on mathematical methods and their applications -- 6 Stochastic partial differential equations in Neurobiology: linear and nonlinear models for spiking neurons -- 7 Deterministic and stochastic FitzHugh-Nagumo systems -- 8 Stochastic modeling of spreading cortical depression.
330   $aStochastic biomathematical models are becoming increasingly important as new light is shed on the role of noise in living systems. In certain biological systems, stochastic effects may even enhance a signal, thus providing a biological motivation for the noise observed in living systems. Recent advances in stochastic analysis and increasing computing power facilitate the analysis of more biophysically realistic models, and this book provides researchers in computational neuroscience and stochastic systems with an overview of recent developments. Key concepts are developed in chapters written by experts in their respective fields. Topics include: one-dimensional homogeneous diffusions and their boundary behavior, large deviation theory and its application in stochastic neurobiological models, a review of mathematical methods for stochastic neuronal integrate-and-fire models, stochastic partial differential equation models in neurobiology, and stochastic modeling of spreading cortical depression.
410  0$aMathematical Biosciences Subseries,$x2524-6771 ;$v2058
606   $aProbabilities
606   $aMathematical models
606   $aStatistics 
606   $aNeurobiology
606   $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004
606   $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068
606   $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030
606   $aNeurobiology$3https://scigraph.springernature.com/ontologies/product-market-codes/L25066
615  0$aProbabilities.
615  0$aMathematical models.
615  0$aStatistics .
615  0$aNeurobiology.
615 14$aProbability Theory and Stochastic Processes.
615 24$aMathematical Modeling and Industrial Mathematics.
615 24$aStatistics for Life Sciences, Medicine, Health Sciences.
615 24$aNeurobiology.
676   $a612.8/1046015118
702   $aBachar$b Mostafa$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aBatzel$b Jerry J$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aDitlevsen$b Susanne$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a996466481003316
996   $aStochastic biomathematical models$9258663
997   $aUNISA