Table of Contents Author Guidelines Submit a Manuscript

Modeling and Analysis of Neural Spike Trains

Call for Papers

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 Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at according to the following timetable:

Manuscript DueFriday, 22 November 2013
First Round of ReviewsFriday, 8 November 2013
Publication DateFriday, 3 January 2014

Lead Guest Editor

  • Wei Wu, Department of Statistics, Florida State University, Tallahassee, FL 32306, USA

Guest Editors

  • Asohan Amarasingham, Department of Mathematics, The City College of New York, New York, NY 10031, USA
  • Zhe (Sage) Chen, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • Sung-Phil Kim, Research and Business Foundation, Korea University, Seoul, Republic of Korea