Neuronal Noise


Alain Destexhe & Michelle Rudolph-Lilith

Springer, New York, 2012 (Preface by Christof Koch; ISBN: 978-0-387-79019-0)


Summary:

"Neuronal Noise" combines experimental, theoretical and computational results to show how noise is inherent to neuronal activity, and how noise can be important for neuronal computations. The book covers many aspects of noise in neurons, with an emphasis on the largest source of noise: synaptic noise. It provides students and young researchers with an overview of the important methods and concepts that have emerged from research in this area. It also provides the specialist with a summary of the large body of sometimes contrasting experimental data, and different theories proposed to explore the computational power that various forms of "noise" can confer to neurons.

The book was published online and in paper format in February 2012.


Table of Contents:

Foreword (by Christof Koch)

Preface

Acknowledgments

Chapter I - Introduction

Chapter II - Basics

Chapter III - Synaptic noise

Chapter IV - Models of synaptic noise

Chapter V - Integrative properties in the presence of noise

Chapter VI - Recreating synaptic noise in dynamic-clamp

Chapter VII - The mathematics of synaptic noise

Chapter VIII - Analyzing synaptic noise

Chapter IX - Case studies

Chapter X - Conclusions and perspectives

Appendix A - Numerical integration of stochastic differential equations

Appendix B - Distributed generator algorithm

Appendix C - The Fokker-Planck formalism

Appendix D - The RT-Neuron interface for dynamic-clamp

References

Index


See the Springer "Neuronal Noise" web page


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