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Summary Table of Contents

The popularity of signal processing in neuroscience is increasing and with the current availability and development of computer hardware and software it is anticipated that the current growth will continue. Because electrode fabrication has improved and measurement equipment is getting less expensive, electrophysiological measurements with large numbers of channels are now very common. In addition, neuroscience has entered the age of light and fluorescence measurements are fully integrated into the researcher's toolkit. Because each image in a movie contains multiple pixels, these measurements are multi-channel by nature. Furthermore, the availability of both generic and specialized software packages for data analysis has altered the neuroscientist's attitude towards some of the more complex analysis techniques.This book is a companion to the previously published book, 'Signal Processing for Neuroscientists: An Introduction to the Analysis of Physiological Signals', which introduced readers to the basic concepts. It discusses several advanced techniques, rediscovers methods to describe nonlinear systems, and examines the analysis of mulit-channel recordings. Covers the more advanced topics of linear and nonlinear systems analysis and multi-channel analysis Includes practical examples implemented in MATLAB Provides multiple references to the basics to help the student

Preface	p. vii
Lomb's Algorithm and the Hilbert Transform	p. 1
Introduction	p. 1
Unevenly Sampled Data	p. 1
The Hilbert Transform	p. 8
	p. 17
	p. 18
	p. 19
Modeling	p. 21
Introduction	p. 21
Different Types of Models	p. 21
Examples of Parametric and Nonparametr... MOREic Models	p. 23
Polynomials	p. 26
Nonlinear Systems with Memory	p. 32
	p. 36
Volterra Series	p. 39
Introduction	p. 39
Volterra Series	p. 42
A Second-Order Volterra System	p. 45
General Second-Order System	p. 51
System Tests for Internal Structure	p. 53
Sinusoidal Signals	p. 57
Wiener Series	p. 61
Introduction	p. 61
Wiener Kernels	p. 62
Determination of the Zero-, First-, and Second-Order Wiener Kernels	p. 69
Implementation of the Cross-Correlation Method	p. 73
Relation between Wiener and Volterra Kernels	p. 77
Analyzing Spiking Neurons Stimulated with Noise	p. 79
Nonwhite Gaussian Input	p. 84
Summary	p. 86
	p. 87
	p. 89
Poisson-Wiener Series	p. 91
Introduction	p. 91
Systems with Impulse Train Input	p. 91
Determination of the Zero-, First-, and Second-Order Poisson-Wiener Kernels	p. 103
Implementation of the Cross-Correlation Method	p. 109
Spiking Output	p. 111
Summary	p. 112
	p. 113
	p. 117
Decomposition of Multichannel Data	p. 119
Introduction	p. 119
Mixing and Unmixing of Signals	p. 120
Principal Component Analysis	p. 123
Independent Component Analysis	p. 134
	p. 155
Causality	p. 159
Introduction	p. 159
Granger Causality	p. 160
Directed Transfer Function	p. 160
Combination of Multichannel Methods	p. 175
References	p. 177
Table of Contents provided by Ingram. All Rights Reserved.

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Signal Processing for Neuroscientists : Advanced Topics, Nonlinear Techniques and Multi-Channel...

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