TY - GEN digital ID - 131713629 TI - Wavelets in Neuroscience AU - Hramov, Alexander E. AU - Koronovskii, Alexey A. AU - Makarov, Valeri A. AU - Pavlov, Alexey N. AU - Sitnikova, Evgenia PY - 2015 SN - 9783662438503 9783662438510 9783662438497 9783662510780 PB - Berlin, Heidelberg Springer DB - UniCat KW - Complex analysis KW - Classical mechanics. Field theory KW - Statistical physics KW - Physics KW - Biomathematics. Biometry. Biostatistics KW - Biology KW - Human biochemistry KW - Physiology of nerves and sense organs KW - Applied physical engineering KW - Biotechnology KW - Artificial intelligence. Robotics. Simulation. Graphics KW - Computer. Automation KW - DIP (documentimage processing) KW - beeldverwerking KW - medische biochemie KW - chaos KW - toegepaste wiskunde KW - biomathematica KW - theoretische fysica KW - spraaktechnologie KW - complexe analyse (wiskunde) KW - biologie KW - statistiek KW - ingenieurswetenschappen KW - fysica KW - neurobiologie KW - dynamica KW - signaalverwerking UR - https://www.unicat.be/uniCat?func=search&query=sysid:131713629 AB - 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 gradua te students specializing in the corresponding areas. ER -