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Wavelets in Neuroscience

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This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade.

Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics.

Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural networks (chapter 4). The features of time-frequency organization of EEG signals are then extensively discussed, from theory to practical applications (chapters 5 and 6). Lastly, the technical details of automatic diagnostics and processing of EEG signals using wavelets are examined (chapter 7).

The book will be a useful resource for neurophysiologists and physicists familiar with nonlinear dynamical systems and data processing, as well as for graduat

e students specializing in the corresponding areas.

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Publisher

: Springer Berlin, Heidelberg., 2015

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Language

English

ISBN/ISSN

978-3-662-43850-3

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NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Signal
systems biology
Neurobiology
Image and Speech Processing
Applications of Graph Theory and Complex Networks
Physiological
Cellular and Medical Topics

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Statement of Responsibility

Alexander E. Hramov, Alexey A. Koronovskii, Valeri A. Makarov, Alexey N. Pavlov, Evgenia Sitnikova

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agus

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