Neuronal dynamics : from single neurons to networks and models of cognition

著者

    • Gerstner, Wulfram
    • Kistler, Werner M.
    • Naud, Richard
    • Paninski, Liam

書誌事項

Neuronal dynamics : from single neurons to networks and models of cognition

Wulfram Gerstner ... [et al.]

Cambridge University Press, 2014

  • : hard
  • : pbk

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注記

Other authors: Werner M. Kistler, Richard Naud, Liam Paninski

Includes bibliographical references and index

内容説明・目次

内容説明

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.

目次

  • Preface
  • Part I. Foundations of Neuronal Dynamics: 1. Introduction
  • 2. The Hodgkin-Huxley model
  • 3. Dendrites and synapses
  • 4. Dimensionality reduction and phase plane analysis
  • Part II. Generalized Integrate-and-Fire Neurons: 5. Nonlinear integrate-and-fire models
  • 6. Adaptation and firing patterns
  • 7. Variability of spike trains and neural codes
  • 8. Noisy input models: barrage of spike arrivals
  • 9. Noisy output: escape rate and soft threshold
  • 10. Estimating models
  • 11. Encoding and decoding with stochastic neuron models
  • Part III. Networks of Neurons and Population Activity: 12. Neuronal populations
  • 13. Continuity equation and the Fokker-Planck approach
  • 14. The integral-equation approach
  • 15. Fast transients and rate models
  • Part IV. Dynamics of Cognition: 16. Competing populations and decision making
  • 17. Memory and attractor dynamics
  • 18. Cortical field models for perception
  • 19. Synaptic plasticity and learning
  • 20. Outlook: dynamics in plastic networks
  • Bibliography
  • Index.

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