Document Detail


Ergodicity of spike trains: when does trial averaging make sense?
MedLine Citation:
PMID:  12816576     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Neuronal information processing is often studied on the basis of spiking patterns. The relevant statistics such as firing rates calculated with the peri-stimulus time histogram are obtained by averaging spiking patterns over many experimental runs. However, animals should respond to one experimental stimulation in real situations, and what is available to the brain is not the trial statistics but the population statistics. Consequently, physiological ergodicity, namely, the consistency between trial averaging and population averaging, is implicitly assumed in the data analyses, although it does not trivially hold true. In this letter, we investigate how characteristics of noisy neural network models, such as single neuron properties, external stimuli, and synaptic inputs, affect the statistics of firing patterns. In particular, we show that how high membrane potential sensitivity to input fluctuations, inability of neurons to remember past inputs, external stimuli with large variability and temporally separated peaks, and relatively few contributions of synaptic inputs result in spike trains that are reproducible over many trials. The reproducibility of spike trains and synchronous firing are contrasted and related to the ergodicity issue. Several numerical calculations with neural network examples are carried out to support the theoretical results.
Authors:
Naoki Masuda; Kazuyuki Aihara
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Neural computation     Volume:  15     ISSN:  0899-7667     ISO Abbreviation:  Neural Comput     Publication Date:  2003 Jun 
Date Detail:
Created Date:  2003-06-20     Completed Date:  2003-07-17     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  9426182     Medline TA:  Neural Comput     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1341-72     Citation Subset:  IM    
Affiliation:
Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, University of Tokyo, Japan. masuda@sat.t.u-tokyo.ac.jp
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MeSH Terms
Descriptor/Qualifier:
Action Potentials / physiology*
Feedback
Models, Neurological*
Neurons / physiology*
Periodicity
Synapses / physiology

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


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