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

Publisher Description

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.

Table of Contents

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.

Promotional "Headline"

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

Description for Bookstore

Exploring neuron models, the neural code, decision making and learning, this textbook provides a thorough and up-to-date introduction to computational neuroscience for advanced undergraduate and beginning graduate students. With step-by-step explanations, end-of-chapter summaries and classroom-tested exercises, it is ideal for courses or for self-study.

Description for Library

Exploring neuron models, the neural code, decision making and learning, this textbook provides a thorough and up-to-date introduction to computational neuroscience for advanced undergraduate and beginning graduate students. With step-by-step explanations, end-of-chapter summaries and classroom-tested exercises, it is ideal for courses or for self-study.

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