Computational Modeling Methods for Neuroscientists (Hardcover)

Erik De Schutter

  • 出版商: MIT
  • 出版日期: 2009-11-01
  • 售價: $1,750
  • 貴賓價: 9.8$1,715
  • 語言: 英文
  • 頁數: 432
  • 裝訂: Hardcover
  • ISBN: 0262013274
  • ISBN-13: 9780262013277
  • 相關分類: 人工智慧
  • 立即出貨 (庫存=1)

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商品描述

This book offers an introduction to current methods in computational modeling in neuroscience. The book describes realistic modeling methods at levels of complexity ranging from molecular interactions to large neural networks. A "how to" book rather than an analytical account, it focuses on the presentation of methodological approaches, including the selection of the appropriate method and its potential pitfalls. It is intended for experimental neuroscientists and graduate students who have little formal training in mathematical methods, but it will also be useful for scientists with theoretical backgrounds who want to start using data-driven modeling methods. The mathematics needed are kept to an introductory level; the first chapter explains the mathematical methods the reader needs to master to understand the rest of the book.

The chapters are written by scientists who have successfully integrated data-driven modeling with experimental work, so all of the material is accessible to experimentalists. The chapters offer comprehensive coverage with little overlap and extensive cross-references, moving from basic building blocks to more complex applications.

Contributors: Pablo Achard, Haroon Anwar, Upinder S. Bhalla, Michiel Berends, Nicolas Brunel, Ronald L. Calabrese, Brenda Claiborne, Hugo Cornelis, Erik De Schutter, Alain Destexhe, Bard Ermentrout, Kristen Harris, Sean Hill, John R. Huguenard, William R. Holmes, Gwen Jacobs, Gwendal LeMasson, Henry Markram, Reinoud Maex, Astrid A. Prinz, Imad Riachi, John Rinzel, Arnd Roth, Felix Schürmann, Werner Van Geit, Mark C. W. van Rossum, Stefan Wils

Computational Neuroscience series

商品描述(中文翻譯)

這本書介紹了神經科學中目前的計算建模方法。該書描述了從分子相互作用到大型神經網絡的不同複雜程度的實際建模方法。這本書著重於方法論的呈現,而不是分析性的描述,包括選擇適當方法及其潛在陷阱。它旨在為實驗神經科學家和研究生提供幫助,他們在數學方法方面缺乏正式培訓,但對於具有理論背景並希望開始使用數據驅動建模方法的科學家也很有用。所需的數學知識保持在入門級水平;第一章解釋了讀者需要掌握的數學方法,以便理解本書的其餘部分。

這些章節是由成功將數據驅動建模與實驗工作相結合的科學家撰寫的,因此所有材料對實驗者都是可理解的。這些章節提供了全面的覆蓋,幾乎沒有重疊,並且有廣泛的交叉引用,從基本構建塊到更複雜的應用。

貢獻者:Pablo Achard、Haroon Anwar、Upinder S. Bhalla、Michiel Berends、Nicolas Brunel、Ronald L. Calabrese、Brenda Claiborne、Hugo Cornelis、Erik De Schutter、Alain Destexhe、Bard Ermentrout、Kristen Harris、Sean Hill、John R. Huguenard、William R. Holmes、Gwen Jacobs、Gwendal LeMasson、Henry Markram、Reinoud Maex、Astrid A. Prinz、Imad Riachi、John Rinzel、Arnd Roth、Felix Schürmann、Werner Van Geit、Mark C. W. van Rossum、Stefan Wils

《計算神經科學系列》