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Stochastic Methods in Neuroscience
Edited by Carlo Laing and Gabriel J Lord
400 pages
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110 line illustrations, 1 halftone
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234x156mm
978-0-19-923507-0
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Hardback
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24 September 2009
Price:
£49.95 £12.48
Please note, this offer price only applies to individual customers when ordering direct from Oxford University Press, while stock lasts. No further discounts will apply. If you are a bookseller, please contact your OUP sales representative.
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- Topical and timely work in a growing field
- Brings together research from disparate sources
- Introductory material through to cutting edge research
- Extensive, up to date bibliography
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 realistic models. 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 written by 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.Readership: Graduates and researchers in computational neuroscience, stochastic systems, statistics
and mathematics.
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Edited by Carlo Laing, Institute of Information and Mathematical Sciences, Massey University, New Zealand, and Gabriel J Lord, Heriot-Watt University, Edinburgh Contributors:
Carlo Laing and Gabriel J Lord
Benjamin Lindner
Jeffrey R Groff, Hilary DeRemigio, and Gregory D Smith
Nils Berglund and Barbara Gentz
André Longtin
Bard Ermentrout
Brent Doiron
Daniel Tranchina
Marco A Huertas and Gregory D Smith
Alin Destexhe and Michelle Rudolph-Lilith
Liam Paninski, Emery N Brown, Satish Iyengar, and Robert E Kass
A
Aldo Faisal
Hasan Alzubaidi, Hagen Gilsing, Tony Shardlow
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PrefaceCarlo Laing and Gabriel J Lord:
Nomenclature
1: Benjamin Lindner: A brief introduction to some basic stochastic processes
2: Jeffrey R Groff, Hilary DeRemigio, and Gregory D Smith: Markov chain models of ion channels and calcium release sites
3: Nils Berglund and Barbara Gentz: Stochastic dynamic bifurcations and excitability
4: André Longtin: Neural coherence and stochastic resonance
5: Bard Ermentrout: Noisy oscillators
6: Brent Doiron: The role of variablity in populations of spiking neuons
7: Daniel Tranchina: Population density methods in large-scale neural network modelling
8: Marco A Huertas and Gregory D Smith: A population density model of the driven LGN/PGN
9: Alin Destexhe and Michelle Rudolph-Lilith: Syanptic "noise": experiments, computatioal consequences and methods to analyze experimental data
10: Liam Paninski, Emery N Brown, Satish Iyengar, and Robert E Kass: Statistical models of spike trains
11: A Aldo Faisal: Stochastic simulations of neurons, axons, and action potentials
12: Hasan Alzubaidi, Hagen Gilsing, Tony Shardlow: Numerical simulations of SDEs and SPDEs from neural systems using SDELAB
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The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
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