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Book: Spiking Neuron Models by W. Gerstner and W.M. Kistler Spiking Neuron Models Single Neurons, Populations, Plasticity Wulfram Gerstner and Werner M. Kistler Cambridge University Press (August 2002) paperback: ISBN 0 521 89079 9 : 40 USD hardcover: ISBN 0 521 81384 0 : 90 USD Order directly from Cambridge University Press or from Amazon If you want to browse through the book, here is the full book as html Table of Contents Preface Chapter 1: Introduction Part I: Single Neuron Models Chapter 2: Detailed Neurom Models Chapter 3: Two-Dimensional Neuron Models Chapter 4: Formal Spiking Neuron Models Chapter 5: Noise in Spiking Neuron Models http://diwww.epfl.ch/~gerstner/BUCH.html (1 of 2) [25.9.2002 7:13:48]

Book: Spiking Neuron Models by W. Gerstner and W.M. Kistler Part II: Populations of Spiking Neurons Chapter 6: Population Equations Chapter 7: Signal Transmission and Coding Chapter 8: Oscillations and Synchrony Chapter 9: Spatially Structured Networks Part III: Models of Synaptic Plasitity Chapter 10: Hebbian Models Chapter 11: Learning Equations Chapter 12: Plasticity and Coding Bibliography and Index http://diwww.epfl.ch/~gerstner/BUCH.html (2 of 2) [25.9.2002 7:13:48]

Preface: Spiking Neuron Models by W. Gerstner and W.M. Kistler Spiking Neuron Models Single Neurons, Populations, Plasticity Preface The task of understanding the principles of information processing in the brain poses, apart from numerous experimental questions, challenging theoretical problems on all levels from molecules to behavior. This books concentrates on modeling approaches on the level of neurons and small populations of neurons, since we think that this is an appropriate level to adress fundamental questions of neuronal coding, signal transmission, or synaptic plasticity. The focus of the text is on phenomenological models and theoretical concepts. We think of a neuron primarily as a dynamic element that emits output pulses whenever the excitation exceeds some threshold. The resulting sequence of pulses or `spikes' contains all the information that is transmitted from one neuron to the next. In order to understand signal transmission and signal processing in neuronal systems, we need an understanding of their basic elements, i.e., the neurons, which is the topic of part~I. New phenomena emerge when several neurons are coupled. Part~II introduces network concepts, in particular pattern formation, collective excitations, and rapid signal transmission between neuronal populations. Learning concepts presented in Part~III are based on spike-time dependent synaptic plasticity. We wrote this book as an introduction to spiking neuron models for advanced undergraduate or graduate students. It can be used either as the main text for a course that focuses on neuronal dynamics; or as part of a larger course in Computational Neuroscience, theoretical biology, neuronal modeling, biophysics, or http://diwww.epfl.ch/~gerstner/PUBLICATIONS/pref.html (1 of 2) [25.9.2002 7:14:07]

Preface: Spiking Neuron Models by W. Gerstner and W.M. Kistler neural networks. For a one-semester course on neuronal modeling, we usually teach one chapter per week focusing on the first sections of each chapter for lectures and give the remainder as reading assignment. Many of the examples can be reformulated as exercises. While writing the book we had in mind students of physics, mathematics, or computer science with an interest in biology; but it might also be useful for students of biology who are interested in mathematical modeling. All the necessary mathematical concepts are introduced in an elementary fashion. No prior knowledge beyond undergraduate mathematics should be necessary to read the book. An asterisk (*) marks those sections that have a more mathematical focus. These sections can be skipped at a first reading. We have also tried to keep the book self-contained with respect to the underlying Neurobiology. The fundamentals of neuronal excitation and synaptic signal transmission are briefly introduced in Chapter 1 together with an outlook on the principal topics of the book, viz., formal spiking neuron models and the problem of neuronal coding. In Chapter 2 we review biophysical models of neuronal dynamics such as the Hodgkin-Huxley model and models of dendritic integration based on the cable equation. These models are the starting point for a systematic reduction to neuron models with a reduced complexity that are open to an analytical treatment. Whereas Chapter 3 is dedicated to two-dimensional differential equations as a description of neuronal dynamics, Chapter 4 introduces formal spiking neuron models, namely the integrate-and-fire model and the Spike Response Model. These formal neuron models are the foundation for all the following chapters. Part I on ``Single Neuron Models'' is completed by Chapter 5 which gives an overview of spike-train statistics and illustrates how noise can be implemented in spiking neuron models. The step from single neuron models to networks of neurons is taken in Chapter 6 where equations for the macroscopic dynamics of large populations of neurons are derived. Based on these equations phenomena like signal transmission and coding (Chapter 7), oscillations and synchrony (Chapter 8), and pattern formation in spatially structured networks (Chapter 9) are investigated. So far, only networks with a fixed synaptic connectivity have been discussed. The third part of the book, finally, deals with synaptic plasticity and its role for development, learning, and memory. In Chapter 10, principles of Hebbian plasticity are presented and various models of synaptic plasticity are described that are more or less directly inspired by neurbiological findings. Equations that relate the synaptic weight dynamics to statistical properties of the neuronal spike activity are derived in Chapter 11. Last but not least, Chapter 12 presents an -- admittedly personal -- choice of illustrative applications of spike-timing dependent synaptic plasticity to fundamental problems of neuronal coding. While the book contains material which is now considered as standard for courses in Computational Neuroscience, neuronal modeling, or neural networks, it also provides a bridge to current research which has developed over the last few years. In most chapters, the reader will find some sections which either report recent results or shed new light on well-known models. The viewpoint taken in the presentation of the material is of course highly subjective and a bias towards our own research is obvious. Nevertheless, we hope that the book will find the interest of students and researchers in the field. Werner M. Kistler and W. Gerstner Lausanne, November 2001 http://diwww.epfl.ch/~gerstner/PUBLICATIONS/pref.html (2 of 2) [25.9.2002 7:14:07]

Spiking Neuron Models Single Neurons, Populations, Plasticity next up previous contents index Next: Contents Spiking Neuron Models Single Neurons, Populations, Plasticity Wulfram Gerstner and Werner M. Kistler l Contents m 1. Introduction l I. Single Neuron Models m m m m 2. Detailed Neuron Models 3. Two-Dimensional Neuron Models 4. Formal Spiking Neuron Models 5. Noise in Spiking Neuron Models l II. Population Models m m m m 6. Population Equations 7. Signal Transmission and Neuronal Coding 8. Oscillations and Synchrony 9. Spatially Structured Networks l III. Models of Synaptic Plasticity m m 10. Hebbian Models 11. Learning Equations 12. Plasticity and Coding m l Bibliography Index l http://diwww.epfl.ch/~gerstner/SPNM/SPNM.html (1 of 2) [25.9.2002 7:33:32]

Spiking Neuron Models Single Neurons, Populations, Plasticity Gerstner and Kistler Spiking Neuron Models. Single Neurons, Populations, Plasticity Cambridge University Press, 2002 http://diwww.epfl.ch/~gerstner/SPNM/SPNM.html (2 of 2) [25.9.2002 7:33:32]

Contents next up previous index Next: 1. Introduction Up: Spiking Neuron Models Single Previous: Spiking Neuron Models Single Contents l m 1. Introduction n 1.1 Elements of Neuronal Systems n n 1.1.1 The Ideal Spiking Neuron 1.1.2 Spike Trains 1.1.3 Synapses n n 1.2 Elements of Neuronal Dynamics 1.2.1 Postsynaptic Potentials 1.2.2 Firing Threshold and Action Potential n n n 1.3 A Phenomenological Neuron Model n n 1.3.1 Definition of the Model SRM0 1.3.2 Limitations of the Model 1.4 The Problem of Neuronal Coding 1.5 Rate Codes n n n n 1.5.1 Rate as a Spike Count (Average over Time) 1.5.2 Rate as a Spike Density (Average over Several Runs) 1.5.3 Rate as a Population Activity (Average over Several Neurons) 1.6 Spike Codes n n n n 1.6.1 Time-to-First-Spike 1.6.2 Phase 1.6.3 Correlations and Synchrony 1.6.4 Stimulus Reconstruction and Reverse Correlation n n n 1.7 Discussion: Spikes or Rates? 1.8 Summary n l I. Single Neuron Models m 2. Detailed Neuron Models http://diwww.epfl.ch/~gerstner/SPNM/node1.html (1 of 8) [25.9.2002 7:33:33]

Contents n 2.1 Equilibrium potential n 2.1.1 Nernst potential 2.1.2 Reversal Potential n n 2.2 Hodgkin-Huxley Model n 2.2.1 Definition of the model 2.2.2 Dynamics n n n n 2.3 The Zoo of Ion Channels 2.3.1 Sodium Channels 2.3.2 Potassium Channels 2.3.3 Low-Threshold Calcium Current 2.3.4 High-threshold calcium current and Ca2+-Activated Potassium Channels 2.3.5 Calcium Dynamics n n n n 2.4 Synapses n 2.4.1 Inhibitory Synapses 2.4.2 Excitatory Synapses n n 2.5 Spatial Structure: The Dendritic Tree n n 2.5.1 Derivation of the Cable Equation 2.5.2 Green's Function (*) 2.5.3 Non-linear Extensions to the Cable Equation n n 2.6 Compartmental Models 2.7 Summary n m 3. Two-Dimensional Neuron Models n 3.1 Reduction to two dimensions n 3.1.1 General approach 3.1.2 Mathematical steps (*) n n 3.2 Phase plane analysis n n 3.2.1 Nullclines 3.2.2 Stability of Fixed Points 3.2.3 Limit cycles 3.2.4 Type I and type II models n n n 3.3 Threshold and excitability n 3.3.1 Type I models 3.3.2 Type II models n http://diwww.epfl.ch/~gerstner/SPNM/node1.html (2 of 8) [25.9.2002 7:33:33]

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