Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition

Wulfram Gerstner; Werner M. Kistler; Richard Naud; Liam Paninski

doi:10.1017/CBO9781107447615

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Neuronal Dynamics

Neuronal Dynamics

From Single Neurons to Networks and Models of Cognition

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Cited by 817
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This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Wulfram Gerstner, École Polytechnique Fédérale de Lausanne, Werner M. Kistler, Richard Naud, University of Ottawa, Liam Paninski, Columbia University, New York

Publisher:: Cambridge University Press
Online publication date:: August 2014
Print publication year:: 2014
Online ISBN:: 9781107447615
DOI:: https://doi.org/10.1017/CBO9781107447615

Subjects:: Life Sciences, Computer Science, Computational Biology and Bioinformatics, Neurosciences

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What happens in our brain when we make a decision? What triggers a neuron to send out a signal? What is the neural code? This textbook for advanced undergraduate and beginning graduate students provides a thorough and up-to-date introduction to the fields of computational and theoretical neuroscience. It covers classical topics, including the Hodgkin–Huxley equations and Hopfield model, as well as modern developments in the field such as generalized linear models and decision theory. Concepts are introduced using clear step-by-step explanations suitable for readers with only a basic knowledge of differential equations and probabilities, and are richly illustrated by figures and worked-out examples. End-of-chapter summaries and classroom-tested exercises make the book ideal for courses or for self-study. The authors also give pointers to the literature and an extensive bibliography, which will prove invaluable to readers interested in further study.

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Neuronal Dynamics

From Single Neurons to Networks and Models of Cognition

This Book has been cited by the following publications. This list is generated based on data provided by Crossref.

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-xii

PART ONE - FOUNDATIONS OF NEURONAL DYNAMICS
pp 1-2

1 - Introduction: neurons and mathematics
pp 3-27

2 - Ion channels and the Hodgkin–Huxley model
pp 28-57

3 - Dendrites and synapses
pp 58-80

4 - Dimensionality reduction and phase plane analysis
pp 81-114

PART TWO - GENERALIZED INTEGRATE-AND-FIRE NEURONS
pp 115-118

5 - Nonlinear integrate-and-fire models
pp 119-135

6 - Adaptation and firing patterns
pp 136-167

7 - Variability of spike trains and neural codes
pp 168-201

8 - Noisy input models: barrage of spike arrivals
pp 202-223

9 - Noisy output: escape rate and soft threshold
pp 224-242

10 - Estimating parameters of probabilistic neuron models
pp 243-266

11 - Encoding and decoding with stochastic neuron models
pp 267-286

PART THREE - NETWORKS OF NEURONS AND POPULATION ACTIVITY
pp 287-290

12 - Neuronal populations
pp 291-324

13 - Continuity equation and the Fokker–Planck approach
pp 325-356

14 - Quasi-renewal theory and the integral-equation approach
pp 357-394

15 - Fast transients and rate models
pp 395-416

PART FOUR - DYNAMICS OF COGNITION
pp 417-420

16 - Competing populations and decision making
pp 421-441

17 - Memory and attractor dynamics
pp 442-466

18 - Cortical field models for perception
pp 467-490

19 - Synaptic plasticity and learning
pp 491-523

20 - Outlook: dynamics in plastic networks
pp 524-546

References
pp 547-572

Index
pp 573-577

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Neuronal Dynamics

From Single Neurons to Networks and Models of Cognition

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