Neuronal Noise
Alain Destexhe & Michelle
Rudolph-Lilith
Springer, New York, 2012 (Preface by Christof Koch; ISBN: 978-0-387-79019-0)
"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.
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