Stochastic Biomathematical Models
with Applications to Neuronal Modeling
Published on 19. October 2012
XVI, 206 pages
978-3-642-32157-3 (ISBN)
Description
Stochastic 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.
More details
Series
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XVI, 206 p. 34 illus., 13 illus. in color.
File size
3,52 MB
ISBN-13
978-3-642-32157-3 (9783642321573)
DOI
10.1007/978-3-642-32157-3
Schweitzer Classification
Other editions
Additional editions
Mostafa Bachar | Jerry J. Batzel | Susanne Ditlevsen
Stochastic Biomathematical Models
with Applications to Neuronal Modeling
Book
10/2012
1st Edition
Springer
€85.59
Shipment within 7-9 days
Content
1 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
