This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience and a Professor of Life Sciences and Computer Science at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He studied physics in Tubingen and Munich and holds a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons and synaptic plasticity. He teaches computational neuroscience to physicists, computer scientists, mathematicians, and life scientists. He is a co-author of Spiking Neuron Models (Cambridge, 2002).
Inhaltsangabe
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.
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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