Computational Intelligence and Neuroscience

Modeling and Analysis of Neural Spike Trains


Publishing date
03 Jan 2014
Status
Published
Submission deadline
22 Nov 2013

Lead Editor

1Department of Statistics, Florida State University, Tallahassee, FL 32306, USA

2Department of Mathematics, The City College of New York, New York, NY 10031, USA

3Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

4Research and Business Foundation, Korea University, Seoul, Republic of Korea


Modeling and Analysis of Neural Spike Trains

Description

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 because 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-the-art 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 larger-scale neuronal ensemble activity, and current research has been devoted to integrating and analyzing increasingly large-volume, high-dimensional, and fine-grain experimental data.

The main focus of this special issue is on providing an international forum for researchers to present the most recent developments and innovative ideas in the field. We aim to incorporate new contributions in theory, algorithms, and applications. Review articles that summarize certain types of methods (e.g., state-space models, spike train metrics, and spike sorting) are welcome. Papers that focus on clinical and engineering developments involving neural signals (e.g., brain-machine interfaces) are also solicited. Potential topics include, but are not limited to:

  • Statistical modeling and neural signal processing
  • Neural network modeling and analysis
  • Neural dynamics
  • Ensemble neural coding
  • Spike-based brain-machine interfaces and neural prostheses
  • Representation of high-dimensional ensemble activity
  • Spike train metrics
  • Spike detection and sorting
  • Synchrony between spike trains
  • Spike-LFP dependence and correlation

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at http://www.hindawi.com/journals/cin/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at http://mts.hindawi.com/submit/journals/cin/nst/ according to the following timetable:


Articles

  • Special Issue
  • - Volume 2014
  • - Article ID 161203
  • - Editorial

Modeling and Analysis of Neural Spike Trains

Wei Wu | Asohan Amarasingham | ... | Sung-Phil Kim
  • Special Issue
  • - Volume 2014
  • - Article ID 757068
  • - Research Article

Sparse Data Analysis Strategy for Neural Spike Classification

Vincent Vigneron | Hsin Chen
  • Special Issue
  • - Volume 2014
  • - Article ID 643059
  • - Research Article

Spike Sorting by Joint Probabilistic Modeling of Neural Spike Trains and Waveforms

Brett A. Matthews | Mark A. Clements
  • Special Issue
  • - Volume 2014
  • - Article ID 870160
  • - Research Article

A Tensor-Product-Kernel Framework for Multiscale Neural Activity Decoding and Control

Lin Li | Austin J. Brockmeier | ... | José C. Príncipe
  • Special Issue
  • - Volume 2014
  • - Article ID 575716
  • - Research Article

Prediction of Human's Ability in Sound Localization Based on the Statistical Properties of Spike Trains along the Brainstem Auditory Pathway

Ram Krips | Miriam Furst
  • Special Issue
  • - Volume 2014
  • - Article ID 476580
  • - Research Article

Homogenous Chaotic Network Serving as a Rate/Population Code to Temporal Code Converter

Mikhail V. Kiselev
  • Special Issue
  • - Volume 2013
  • - Article ID 251905
  • - Review Article

An Overview of Bayesian Methods for Neural Spike Train Analysis

Zhe Chen
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication38 days
CiteScore2.270
Impact Factor2.154
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