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  • Signal Processing for Neuroscientists, A Companion Volume: Advanced Topics, Nonlinear Techniques and Multi-Channel Analysis

    Signal Processing for Neuroscientists, A Companion Volume by van Drongelen, Wim;

    Advanced Topics, Nonlinear Techniques and Multi-Channel Analysis

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      • Publisher's listprice EUR 63.95
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        26 523 Ft (25 260 Ft + 5% VAT)
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      • Discounted price 23 871 Ft (22 734 Ft + 5% VAT)

    26 523 Ft

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    Product details:

    • Publisher Elsevier Science
    • Date of Publication 26 August 2010

    • ISBN 9780123849151
    • Binding Hardback
    • No. of pages186 pages
    • Size 228x152 mm
    • Weight 460 g
    • Language English
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    Long description:

    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 toward some of the more complex analysis techniques.

    This book is a companion to the previously published 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 multi-channel recordings.

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    Table of Contents:

    1. Lomb's Algorithm and the Hilbert Transform

    2. Modeling

    3. Volterra Series

    4. Wiener Series

    5. Poisson-Wiener Series

    6. Decomposition of Multi-Channel Data

    7. Causality

    References

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