大葉大學圖書館 |

Wavelets in neuroscience[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	515.2433
書名/作者:	Wavelets in neuroscience/ by Alexander E. Hramov ... [et al.].
其他作者:	Hramov, Alexander E.
出版者:	Berlin, Heidelberg : : Springer Berlin Heidelberg :, 2015.
面頁冊數:	xvi, 318 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Wavelets (Mathematics)
標題:	Neurosciences.
標題:	Physics.
標題:	Nonlinear Dynamics.
標題:	Physiological, Cellular and Medical Topics.
標題:	Neurobiology.
標題:	Complex Networks.
標題:	Signal, Image and Speech Processing.
標題:	Systems Biology.
ISBN:	9783662438503 (electronic bk.)
ISBN:	9783662438497 (paper)
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-662-43850-3

Wavelets in neuroscience[electronic resource] /
Wavelets in neuroscience[electronic resource] /by Alexander E. Hramov ... [et al.]. - Berlin, Heidelberg :Springer Berlin Heidelberg :2015. - xvi, 318 p. :ill. (some col.), digital ;24 cm. - Springer series in synergetics,0172-7389. - Springer series in synergetics..

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.

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.

ISBN: 9783662438503 (electronic bk.)

Standard No.: 10.1007/978-3-662-43850-3doiSubjects--Topical Terms:

393849
Wavelets (Mathematics)

LC Class. No.: QA403.3

Dewey Class. No.: 515.2433

Wavelets in neuroscience[electronic resource] /
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520 $a 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.
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