Multiple neural spike train data analysis: state-of-the-art and future challenges
Top Cited Papers
- 27 April 2004
- journal article
- review article
- Published by Springer Nature in Nature Neuroscience
- Vol. 7 (5), 456-461
- https://doi.org/10.1038/nn1228
Abstract
Multiple electrodes are now a standard tool in neuroscience research that make it possible to study the simultaneous activity of several neurons in a given brain region or across different regions. The data from multi-electrode studies present important analysis challenges that must be resolved for optimal use of these neurophysiological measurements to answer questions about how the brain works. Here we review statistical methods for the analysis of multiple neural spike-train data and discuss future challenges for methodology research.Keywords
This publication has 54 references indexed in Scilit:
- Recursive Bayesian Decoding of Motor Cortical Signals by Particle FilteringJournal of Neurophysiology, 2004
- Dynamic Analyses of Information Encoding in Neural EnsemblesNeural Computation, 2004
- Likelihood Methods for Neural Spike Train Data AnalysisPublished by Taylor & Francis ,2003
- Non-parametric significance estimation of joint-spike events by shuffling and resamplingNeurocomputing, 2003
- Information and Statistical Structure in Spike TrainsNetwork: Computation in Neural Systems, 2003
- Direct Cortical Control of 3D Neuroprosthetic DevicesScience, 2002
- Statistical Significance of Coincident Spikes: Count-Based Versus Rate-Based StatisticsNeural Computation, 2002
- A Spike-Train Probability ModelNeural Computation, 2001
- Dynamics of the Hippocampal Ensemble Code for SpaceScience, 1993
- Nerve Cell Spike Train Data Analysis: A Progression of TechniqueJournal of the American Statistical Association, 1992