Stochastic Methods in Neuroscience (Hardcover)
暫譯: 神經科學中的隨機方法 (精裝版)

<內容簡介>

。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.

.<章節目錄>

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: Andre 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