Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (Paperback)

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

描述：
理論神經科學提供了描述神經系統所做的事情、確定它們如何運作以及揭示它們運作的一般原則的定量基礎。本書介紹了理論神經科學的基本數學和計算方法，並在視覺、感覺運動整合、發展、學習和記憶等多個領域中提供應用。

本書分為三個部分。第一部分討論感官刺激和神經反應之間的關係，重點是神經元的尖峰活動所代表的信息表示。第二部分基於細胞和突觸生物物理學，討論了神經元和神經回路的建模。第三部分分析了發展和學習中可塑性的作用。附錄涵蓋了使用的數學方法，並且書的網站上提供了練習題。

彼得·戴安（Peter Dayan）是倫敦大學學院Gatsby計算神經科學單位的教職員。

L. F. Abbott是布蘭迪斯大學Nancy Lurie Marks神經科學教授和複雜系統Volen中心主任。他是《神經編碼和分佈式表示》（MIT Press，1999年）的合編者。

目錄：
前言
第一部分 神經編碼和解碼
1. 神經編碼I：發射率和尖峰統計
2. 神經編碼II：反向相關和視覺感受野
3. 神經解碼
4. 信息理論