Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Paperback)
暫譯: 理論神經科學:神經系統的計算與數學建模 (平裝本)
Peter Dayan, Laurence F. Abbott
- 出版商: MIT
- 出版日期: 2005-08-12
- 售價: $2,880
- 貴賓價: 9.5 折 $2,736
- 語言: 英文
- 頁數: 480
- 裝訂: Paperback
- ISBN: 0262541858
- ISBN-13: 9780262541855
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相關分類:
人工智慧、Machine Learning
海外代購書籍(需單獨結帳)
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商品描述
Description:
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.
The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Peter Dayan is on the faculty of the Gatsby Computational Neuroscience Unit at University College London.
L. F. Abbott is the Nancy Lurie Marks Professor of Neuroscience and Director of the Volen Center for Complex Systems at Brandeis University. He is the coeditor of Neural Codes and Distributed Representations (MIT Press, 1999).
Table of Contents:
Preface I Neural Encoding and Decoding 1 Neural Encoding I: Firing Rates and Spike Statistics 2 Neural Encoding II: Reverse Correlation and Visual Receptive Fields 3 Neural Decoding 4 Information Theory II Neurons and Neural Circuits 5 Model Neurons I: Neuroelectronics 6 Model Neurons II: Conductances and Morphology 7 Network Models III Adaptation and Learning 8 Plasticity and Learning 9 Classical Conditioning and Reinforcement Learning 10 Representational Learning Mathematical Appendix References Index Exercises
商品描述(中文翻譯)
**描述:**
理論神經科學提供了一個定量基礎,用於描述神經系統的功能、確定其運作方式,以及揭示其運作的一般原則。本書介紹了理論神經科學的基本數學和計算方法,並展示了在視覺、感覺運動整合、發展、學習和記憶等多個領域的應用。
本書分為三個部分。第一部分討論感官刺激與神經反應之間的關係,重點在於神經元的放電活動如何表徵信息。第二部分基於細胞和突觸生物物理學討論神經元和神經電路的建模。第三部分分析可塑性在發展和學習中的角色。附錄涵蓋所使用的數學方法,並且書籍的網站上提供了練習題。
彼得·戴恩(Peter Dayan)是倫敦大學學院(University College London)Gatsby計算神經科學單位的教職員。
L. F. 阿博特(L. F. Abbott)是布蘭代斯大學(Brandeis University)神經科學的南希·盧里·馬克斯教授及複雜系統沃倫中心的主任。他是《神經編碼與分佈表示》(Neural Codes and Distributed Representations,麻省理工學院出版社,1999)的共同編輯。
**目錄:**
- 前言
- I 神經編碼與解碼
- 1 神經編碼 I:放電率與尖峰統計
- 2 神經編碼 II:反向相關與視覺感受野
- 3 神經解碼
- 4 信息理論
- II 神經元與神經電路
- 5 模型神經元 I:神經電子學
- 6 模型神經元 II:導電性與形態學
- 7 網絡模型
- III 適應與學習
- 8 可塑性與學習
- 9 古典條件反射與強化學習
- 10 表徵學習
- 數學附錄
- 參考文獻
- 索引
- 練習題