Stochastic Methods in Neuroscience

Hardcover | October 24, 2009

EditorCarlo Laing, Gabriel J Lord

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Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis. These techniques are leading to biophysically more realisticmodels. It has also become clear that both neuroscientists and mathematicians profit from collaborations in this exciting research area. Graduates and researchers in computational neuroscience and stochastic systems, and neuroscientists seeking to learn more about recent advances in the modelling and analysis of noisy neural systems, will benefit from this comprehensive overview. The series of self-contained chapters, each writtenby experts in their field, covers key topics such as: Markov chain models for ion channel release; stochastically forced single neurons and populations of neurons; statistical methods for parameter estimation; and the numerical approximation of these stochastic models. Each chapter gives an overview of a particular topic, including its history, important results in the area, and future challenges, and the text comes complete with a jargon-busting index of acronyms to allow readers to familiarize themselves with the language used.

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Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in computing power, the ability to record large amounts of neurophysiological data, and advances in stochastic analysis. These techniques are leading to biophysically more realisticmodels. It has also become clear that both neu...

Carlo Laing obtained his PhD in applied mathematics from the University of Cambridge. After post-doctoral positions in the UK, USA and Canada, he joined Massey University in Auckland, New Zealand, where he is currently a senior lecturer. His interests include nonlinear dynamical systems, particularly as applied in computational neuros...

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Stochastic Methods in Neuroscience
Stochastic Methods in Neuroscience

Kobo ebook|Sep 24 2009

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Format:HardcoverDimensions:400 pages, 9.21 × 6.14 × 1.06 inPublished:October 24, 2009Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0199235074

ISBN - 13:9780199235070

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Table of Contents

PrefaceCarlo Laing and Gabriel J Lord: Nomenclature1. Benjamin Lindner: A brief introduction to some basic stochastic processes2. Jeffrey R Groff, Hilary DeRemigio, and Gregory D Smith: Markov chain models of ion channels and calcium release sites3. Nils Berglund and Barbara Gentz: Stochastic dynamic bifurcations and excitability4. Andre Longtin: Neural coherence and stochastic resonance5. Bard Ermentrout: Noisy oscillators6. Brent Doiron: The role of variablity in populations of spiking neuons7. Daniel Tranchina: Population density methods in large-scale neural network modelling8. Marco A Huertas and Gregory D Smith: A population density model of the driven LGN/PGN9. Alin Destexhe and Michelle Rudolph-Lilith: Syanptic "noise": experiments, computatioal consequences and methods to analyze experimental data10. Liam Paninski, Emery N Brown, Satish Iyengar, and Robert E Kass: Statistical models of spike trains11. A Aldo Faisal: Stochastic simulations of neurons, axons, and action potentials12. Hasan Alzubaidi, Hagen Gilsing, Tony Shardlow: Numerical simulations of SDEs and SPDEs from neural systems using SDELAB