From Single Neurons to Networks and Models of Cognition
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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
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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.Read more
- Retains the best elements of Gerstner and Kistler's earlier book, Spiking Neuron Models
- Will appeal to neurobiologists and theoreticians alike
- Python source code for numerical simulations is available online
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- Date Published: July 2014
- format: Adobe eBook Reader
- isbn: 9781139990851
- contains: 280 b/w illus. 6 tables 80 exercises
- availability: This item is not supplied by Cambridge University Press in your region. Please contact eBooks.com for availability.
Table of Contents
Part I. Foundations of Neuronal Dynamics:
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
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