Document Detail


Probability distributions of the logarithm of inter-spike intervals yield accurate entropy estimates from small datasets.
MedLine Citation:
PMID:  18620755     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The maximal information that the spike train of any neuron can pass on to subsequent neurons can be quantified as the neuronal firing pattern entropy. Difficulties associated with estimating entropy from small datasets have proven an obstacle to the widespread reporting of firing pattern entropies and more generally, the use of information theory within the neuroscience community. In the most accessible class of entropy estimation techniques, spike trains are partitioned linearly in time and entropy is estimated from the probability distribution of firing patterns within a partition. Ample previous work has focused on various techniques to minimize the finite dataset bias and standard deviation of entropy estimates from under-sampled probability distributions on spike timing events partitioned linearly in time. In this manuscript we present evidence that all distribution-based techniques would benefit from inter-spike intervals being partitioned in logarithmic time. We show that with logarithmic partitioning, firing rate changes become independent of firing pattern entropy. We delineate the entire entropy estimation process with two example neuronal models, demonstrating the robust improvements in bias and standard deviation that the logarithmic time method yields over two widely used linearly partitioned time approaches.
Authors:
Alan D Dorval
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2008-05-23
Journal Detail:
Title:  Journal of neuroscience methods     Volume:  173     ISSN:  0165-0270     ISO Abbreviation:  J. Neurosci. Methods     Publication Date:  2008 Aug 
Date Detail:
Created Date:  2008-07-28     Completed Date:  2008-11-05     Revised Date:  2014-09-08    
Medline Journal Info:
Nlm Unique ID:  7905558     Medline TA:  J Neurosci Methods     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  129-39     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Action Potentials / physiology*
Animals
Computer Simulation
Databases, Factual*
Entropy*
Information Theory
Models, Neurological*
Neural Networks (Computer)
Neurons / physiology*
Probability*
Statistics as Topic
Grant Support
ID/Acronym/Agency:
K25 NS053544/NS/NINDS NIH HHS; K25 NS053544-01/NS/NINDS NIH HHS; K25 NS053544-02/NS/NINDS NIH HHS; K25 NS053544-03/NS/NINDS NIH HHS; K25-NS0535444/NS/NINDS NIH HHS
Comments/Corrections

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


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