Last edited by Kigalmaran
Saturday, July 25, 2020 | History

2 edition of Stochastic methods in neuroscience found in the catalog.

Stochastic methods in neuroscience

Stochastic methods in neuroscience

  • 300 Want to read
  • 35 Currently reading

Published by New York, Oxford University Press in Oxford .
Written in English

    Subjects:
  • Computational neuroscience,
  • Stochastic processes,
  • Neurosciences -- Mathematics

  • Edition Notes

    Includes bibliographical references and index.

    Statementedited by Carlo Laing and Gabriel J. Lord.
    ContributionsLaing, Carlo., Lord, Gabriel J.
    Classifications
    LC ClassificationsQP357.5 .S76 2010
    The Physical Object
    Paginationxxiii, 370 p., [4] p. of plates :
    Number of Pages370
    ID Numbers
    Open LibraryOL24048601M
    ISBN 100199235074
    ISBN 109780199235070
    LC Control Number2009517096

    This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and researchers powerful tools for understanding uncertainty quantification for risk by:   Fundamentals of Computational Neuroscience: Edition 2 - Ebook written by Thomas Trappenberg. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Fundamentals of Computational Neuroscience: Edition : Thomas Trappenberg.

      This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is : Springer International Publishing. approach to stochastic neural dynamics circa [45]. But a clear formulation of the path-integral approach to stochastic neural dynamics awaited the work of Buice, Cowan and Chow [14–16]. In the present issue Chow and Buice [19] provide a tuto-rial introduction to path-integral methods specifically tailored to the case of stochasticAuthor: Paul C. Bressloff, Bard Ermentrout, Olivier Faugeras, Peter J. Thomas.

      Mathematical Foundations of Neuroscience - Ebook written by G. Bard Ermentrout, David H. Terman. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Mathematical Foundations of Neuroscience/5(2). Electrical signaling in neurons is mediated by the opening and closing of large numbers of individual ion channels. The ion channels' state transitions are stochastic and introduce fluctuations in the macroscopic current through ion channel populations. This creates an unavoidable source of intrinsic electrical noise for the neuron, leading to fluctuations in the Cited by:


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Stochastic methods in neuroscience Download PDF EPUB FB2

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.

Stochastic Methods in Neuroscience 1st Edition by Carlo Laing (Editor), Gabriel J. Lord (Editor) out of 5 stars 1 rating. ISBN ISBN Why is ISBN important. ISBN. This bar-code number lets you verify that you're getting exactly the right version or edition of a book.

5/5(1). Stochastic Methods in Neuroscience - Kindle edition by Laing, Carlo, Lord, Gabriel J. Download it once and read it on your Kindle device, PC, phones or tablets.

Use features like bookmarks, note taking and highlighting while reading Stochastic Methods in Neuroscience.5/5(1). The NOOK Book (eBook) of the Stochastic Methods in Neuroscience by Carlo Laing at Barnes & Noble.

FREE Shipping on $35 or more. Due to COVID, orders may be delayed. Read "Stochastic Methods in Neuroscience" by available from Rakuten Kobo. Great interest is now being shown in computational and mathematical neuroscience, fuelled in part by the rise in Brand: OUP Oxford.

ISBN: OCLC Number: Description: xxiii, pages, 4 unnumbered pages of plates: illustrations ; 24 cm: Contents: A brief introduction to some simple stochastic processes / Benjamin Lindner --Markov chain models of ion channels and calcium release sites / Jeffrey R.

Groff, Hilary DeRemigio and Gregory D. Smith --Stochastic dynamic. Get this from a library. Stochastic methods in neuroscience.

[Carlo Laing; Gabriel J Lord;] -- This text concentrates on the intersection between stochastic dynamics and neuroscience, presenting a series of self-contained chapters on major aspects of.

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.

Stochastic Methods in Neuroscience Carlo Laing, Gabriel J. Lord 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.

In other chapters of this book (notably Longtin), the effects of population density methods. The phase resetting curve & weak perturbations. Preliminaries. Many authors (particularly in physics) define an oscillator to be any dynami- from neuroscience, the 0 phase, is often defined as the time of the spike.

Thus a. We give a brief introduction to modelling in mathematical neuroscience, to stochastic processes, and stochastic differential equations as well as an overview of the book.

Jump to Content Jump to Main Navigation. OSO version build Stochastic Methods in. Stochastic refers to a randomly determined process. The word first appeared in English to describe a mathematical object called a stochastic process, but now in mathematics the terms stochastic process and random process are considered interchangeable.

The word, with its current definition meaning random, came from German, but it originally came from Greek. Books shelved as stochastic-processes: Introduction to Stochastic Processes by Gregory F. Lawler, Adventures in Stochastic Processes by Sidney I.

Resnick. Illustrates numerical methods by applying them to specific topics in neuroscience, including Hodgkin-Huxley equations, probabilities to describe stochastic release, stochastic processes to describe noise in neurons, Fourier transforms to describe the receptive fields of visual neurons.

Stochastic Network Models in Neuroscience: A Festschrift for Jack Cowan. path-integral methods, stochastic synchronization, higher-order correlation analysis, and pattern formation in visual cortex. Jack Cowan’s remarkable career has spanned, and molded, the development of neuroscience as a quantitative and mathematical discipline Author: Paul C.

Bressloff, Bard Ermentrout, Olivier Faugeras, Peter J. Thomas. Stochastic Methods in Neuroscience Hardcover – 24 Sept. by Carlo Laing (Editor), Gabriel J Lord (Editor) out of 5 stars 1 rating.

See all 3 formats and editions Hide other formats and editions. Amazon Price New from 5/5(1). This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling.

Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight.

An extensive bibliography is included. He is the co-editor of Stochastic Methods in Neuroscience with C. Liang, has organised a number of international meetings in the field, and is principal investigator on the porous media processes and mathematics network funded by the Engineering and.

This book gives a comprehensive introduction to numerical methods and analysis of stochastic processes, random fields and stochastic differential equations, and offers graduate students and. In more detail, the book presents the analytic methods and tools for approximating solutions of mixed boundary value problems, with particular emphasis on the narrow escape problem.

Informed throughout by real-world applications, the book includes topics such as the Fokker-Planck equation, boundary layer analysis, WKB approximation. As there is not yet a comprehensive methods book addressing the topic as a whole, this volume fills that gap, presenting state-of-the-art approaches and the technical advancements that enable their application to different scientific problems in neuroscience.Stochastic Resonance: From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization, is a science text, with a foreword by Sergey M.

Bezrukov and Bart Kosko, which notably explores the relationships between stochastic resonance, suprathreshold stochastic resonance, stochastic quantization, and computational book critically Author: Mark D. McDonnell, Nigel G. Stocks. Stochastic Numerical Methods introduces at Master level the numerical methods that use probability or stochastic concepts to analyze random processes.

The book aims at being rather general and is addressed at students of natural sciences (Physics, Chemistry, Mathematics, Biology, etc.) and Engineering, but also social sciences (Economy, Sociology, etc.) where .