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Signal processing for neuroscientist...

Drongelen, Wim van.{me_controlnum}

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Signal processing for neuroscientists = a companion volume : advanced topics, nonlinear techniques and multi-channel analysis /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Signal processing for neuroscientists/ Wim van Drongelen.
Reminder of title:	a companion volume : advanced topics, nonlinear techniques and multi-channel analysis /
Author:	Drongelen, Wim van.{me_controlnum}
Published:	Oxford :Elsevier, : 2010.,
Description:	p. cm.
[NT 15003449]:	Lomb's Algorithm and the Hilbert Transform -- Modeling -- Volterra Series -- Wiener Series -- Poisson-Wiener Series -- Decomposition of Multi-Channel Data -- Causality.
Subject:	Neurology - Mathematical models. -
Online resource:	http://www.sciencedirect.com/science/book/9780123849151An electronic book accessible through the World Wide Web; click for information
ISBN:	9780123849151

Signal processing for neuroscientists = a companion volume : advanced topics, nonlinear techniques and multi-channel analysis /
Drongelen, Wim van.{me_controlnum}

Signal processing for neuroscientistsa companion volume : advanced topics, nonlinear techniques and multi-channel analysis /[electronic resource] :Wim van Drongelen. - Oxford :Elsevier,2010. - p. cm.

Includes bibliographical references.

Lomb's Algorithm and the Hilbert Transform -- Modeling -- Volterra Series -- Wiener Series -- Poisson-Wiener Series -- Decomposition of Multi-Channel Data -- Causality.

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.

Electronic reproduction.
Amsterdam :
Elsevier Science & Technology,
2010.

Mode of access: World Wide Web.

ISBN: 9780123849151

Source: 1101653:10954061Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

926187
Neurology
--Mathematical models.Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: RC346 / .D76 2010

Dewey Class. No.: 610.28

Signal processing for neuroscientists = a companion volume : advanced topics, nonlinear techniques and multi-channel analysis /
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505 0 $a Lomb's Algorithm and the Hilbert Transform -- Modeling -- Volterra Series -- Wiener Series -- Poisson-Wiener Series -- Decomposition of Multi-Channel Data -- Causality.
520 $a 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.
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