Document Detail


Measuring spike pattern reliability with the Lempel-Ziv-distance.
MedLine Citation:
PMID:  16584787     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel-Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calculating the deviation of the mean distance of spike trains obtained from multiple presentations of an identical stimulus from a Poisson reference. Using both the Lempel-Ziv-distance (LZ-distance) and a distance focussing on coincident firing, the pattern and timing reliability of neuronal firing is determined for spike data obtained along the visual information processing pathway of macaque monkey (LGN, simple and complex cells of V1, and area MT). In combination with the sequential superparamagnetic clustering algorithm, we show that the LZ-distance groups together spike trains with similar but not necessarily synchronized firing patterns. For both applications, we show how the LZ-distance gives additional insights, as it adds a new perspective on the problem of firing reliability determination and allows neuron classifications in cases, where other distance measures fail.
Authors:
Markus Christen; Adam Kohn; Thomas Ott; Ruedi Stoop
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2006-04-03
Journal Detail:
Title:  Journal of neuroscience methods     Volume:  156     ISSN:  0165-0270     ISO Abbreviation:  J. Neurosci. Methods     Publication Date:  2006 Sep 
Date Detail:
Created Date:  2006-08-24     Completed Date:  2006-11-14     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  7905558     Medline TA:  J Neurosci Methods     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  342-50     Citation Subset:  IM    
Affiliation:
Institute of Neuroinformatics, University/ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland. markus@ini.phys.ethz.ch
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Animals
Cerebral Cortex / cytology,  physiology
Cluster Analysis
Electroencephalography
Electrophysiology / methods*
Macaca
Membrane Potentials / physiology
Models, Neurological
Models, Statistical
Neurons / classification*,  physiology*
Reproducibility of Results

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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