Year: 2014
Type: article
Abstract: It is well known that time-dependent information is represented via sequences of stereotyped spike waveforms in the nervous system.Mathematical modeling and analysis of waveform sequences (or spike trains) have been one of the central problems in the field of computational neuroscience.This problem is significantly challenging since population neuronal activity is often stochastic, highly correlated, and nonstationary across time.A great deal of effort has been devoted to characterizing this activity by using state-of-theart methodologies, such as artificial neural networks, signal processing methods, adaptive filtering theory, parametric and nonparametric probabilistic models, Bayesian inference, metric-based analysis, and information-theoretic methods.Advances in technology have enabled us to record largerscale neuronal ensemble activity, and current research has devoted a lot to integrating and analyzing increasingly largevolume, high-dimensional, and fine-grain experimental data.The main focus of this special issue is to provide an international forum for researchers to present the most recent developments and innovative ideas in the field.We aim to incorporate new contributions in theories, algorithms, and applications.A total of 17 submissions, which cover a broad spectrum of spike train modeling and analysis, were received for this special issue.Each submission was rigorously reviewed by external referees as well as the Guest Editor.To ensure the high quality of papers, we finally accepted 6 articles for this special issue.The following is a brief summary for each of these accepted articles.
DOI: 10.1155/2014/161203
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,550
OpenAlex ID: W1996699120