Despite a continuous effort in neurophysiological research during last decades, the understanding of mechanisms regulating vital functions is still rather deficient. Investigating the regulation of physiological processes has become a fully multidisciplinary task, highly dependent on the usage of principles from mathematics, physics and computer science in many aspects of view.
Due to changing degrees of neurophysiological data stationarity, the wavelet transformation is often performed in imaging or time-frequency analysis. This multiresolution decomposition results in a biosignal interpretation both in time and frequency domains. Therefore, it is continuously adapted to the analyzed signal properties, and an optimal time–frequency resolution may be reached. Wavelet analysis demonstrated attenuation of high frequency components characterized by great oxygen demands in phrenic neurogram of newborn mammals and even during some specific defence reflex behavior, suggesting dominance of the basic respiratory rhythm generator in hypoxia/hypercapnia conditions.
Beside time - frequency energy distribution, qualitative changes of the neurophysiological data could be demonstrated through nonlinear methods of analysis. Methods of nonlinear dynamics are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Many related parameters reflect system evolution in time and, subsequently, reflect level of new signal pattern generation. They are often understood as a rate of the system complexity. Particular parameters related to level of signal entropy can describe dynamical behavior associated with different neurogram stages. Decreasing of the entropy value detected in electroneurogram or electromyogram during defence reflex behavior is reflecting low probability of system disorder. Thus, different entropy measures show synchroncity of neural firing, level of diffusion energy over neurons or a degree of synaptic activity.
In this research topic, innovative approaches in neurophysiological data modeling will be introduced, and we welcome contributions ranging from original research reports, reviews, technical or methodology articles. The topic will introduce novel biomedical, physical or computational principles in understanding the complex processes of neurophysiological regulations, including, but not limited to, time – frequency analysis, fuzzy logic, brain mapping, artificial neuronal networks or nonlinear dynamics and the chaos theory.
Despite a continuous effort in neurophysiological research during last decades, the understanding of mechanisms regulating vital functions is still rather deficient. Investigating the regulation of physiological processes has become a fully multidisciplinary task, highly dependent on the usage of principles from mathematics, physics and computer science in many aspects of view.
Due to changing degrees of neurophysiological data stationarity, the wavelet transformation is often performed in imaging or time-frequency analysis. This multiresolution decomposition results in a biosignal interpretation both in time and frequency domains. Therefore, it is continuously adapted to the analyzed signal properties, and an optimal time–frequency resolution may be reached. Wavelet analysis demonstrated attenuation of high frequency components characterized by great oxygen demands in phrenic neurogram of newborn mammals and even during some specific defence reflex behavior, suggesting dominance of the basic respiratory rhythm generator in hypoxia/hypercapnia conditions.
Beside time - frequency energy distribution, qualitative changes of the neurophysiological data could be demonstrated through nonlinear methods of analysis. Methods of nonlinear dynamics are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Many related parameters reflect system evolution in time and, subsequently, reflect level of new signal pattern generation. They are often understood as a rate of the system complexity. Particular parameters related to level of signal entropy can describe dynamical behavior associated with different neurogram stages. Decreasing of the entropy value detected in electroneurogram or electromyogram during defence reflex behavior is reflecting low probability of system disorder. Thus, different entropy measures show synchroncity of neural firing, level of diffusion energy over neurons or a degree of synaptic activity.
In this research topic, innovative approaches in neurophysiological data modeling will be introduced, and we welcome contributions ranging from original research reports, reviews, technical or methodology articles. The topic will introduce novel biomedical, physical or computational principles in understanding the complex processes of neurophysiological regulations, including, but not limited to, time – frequency analysis, fuzzy logic, brain mapping, artificial neuronal networks or nonlinear dynamics and the chaos theory.
