Titlebook: Wavelets in Neuroscience; Alexander E. Hramov,Alexey A. Koronovskii,Evgenia Book 2015 Springer-Verlag Berlin Heidelberg 2015 Data Analysi

剝削

nonsensical

旅行路線

Mathematical Methods of Signal Processing in Neuroscience,ive the rationale for the development of specific mathematical approaches for decoding information from non-stationary neurophysiological processes with time-varying features. Second, we focus on the development of mathematical methods for automatic processing and analysis of neurophysiological sign

樂意

Analysis of Single Neuron Recordings,ned with ordinary frequency or time domain methods. We discuss the possibility of studying intracellular dynamics and information encoding by individual neurons. We characterize the dynamical stability of the neuronal response and propose an approach to quantify wavelet coherence.

Deceit

Sigmoidoscopy

trigger

Analysis of Single Neuron Recordings,ned with ordinary frequency or time domain methods. We discuss the possibility of studying intracellular dynamics and information encoding by individual neurons. We characterize the dynamical stability of the neuronal response and propose an approach to quantify wavelet coherence.

Tdd526

Wavelet Approach to the Study of Rhythmic Neuronal Activity,e the general physical and mathematical approaches to time–frequency analysis of rhythmic EEG activity using the continuous wavelet transform. Besides that, we review some recent achievements of wavelet-based studies of electrical brain activity, including (i) time–frequency analysis of EEG structur

苦笑

Classification of Neuronal Spikes from Extracellular Recordings,amics of small groups of neurons. We discuss general principles of spike sorting and propose several wavelet-based techniques to improve the quality of spike separation, including an approach for optimal sorting with wavelets and filtering techniques. Finally, we consider the application of artifici

FIN

Wavelet Approach to the Study of Rhythmic Neuronal Activity,e the general physical and mathematical approaches to time–frequency analysis of rhythmic EEG activity using the continuous wavelet transform. Besides that, we review some recent achievements of wavelet-based studies of electrical brain activity, including (i) time–frequency analysis of EEG structur