Logo - springer
Slogan - springer

Physics - Statistical Physics & Dynamical Systems | Wavelets in Neuroscience

Wavelets in Neuroscience

Hramov, A.E., Koronovskii, A.A., Makarov, V.A., Pavlov, A.N., Sitnikova, E.

2015, XVI, 318 p. 138 illus., 20 illus. in color.

Available Formats:
eBook
Information

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.

 
$99.00

(net) price for USA

ISBN 978-3-662-43850-3

digitally watermarked, no DRM

Included Format: PDF and EPUB

download immediately after purchase


learn more about Springer eBooks

add to marked items

Hardcover
Information

Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

 
$129.00

(net) price for USA

ISBN 978-3-662-43849-7

free shipping for individuals worldwide

usually dispatched within 3 to 5 business days


add to marked items

  • Authored by leading researchers in the field
  • Self-contained introduction and review of an emerging topic
  • Suitable as graduate-level text for non-specialists and students

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 graduate students specializing in the corresponding areas.

Content Level » Research

Keywords » Data Analysis in Neurophysics - Dynamical Systems in Neuroscience - Neurodynamics and Neurophysiology - Neuronal Networks - Neuronal Spikes and Activity - Wavelet Theory and Analysis

Related subjects » Complexity - Neuroscience - Signals & Communication - Statistical Physics & Dynamical Systems

Table of contents 

MathematicalMethods of Signal Processing in Neuroscience.- Brief Tour of Wavelet Theory.- Analysis of Single Neuron Recordings.- Classification of Neuronal Spikes from Extracellular Recordings.- Wavelet Approach to the Study of Rhythmic Neuronal Activity.- Time–Frequency Analysis of EEG: From Theory to Practice.- Automatic Diagnostics and Processing of EEG.- Conclusion.- Index.

Popular Content within this publication 

 

Articles

Read this Book on Springerlink

Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Nonlinear Dynamics.