Document Detail


Quantification of sensory information transmission using timeseries decorrelation techniques.
MedLine Citation:
PMID:  12459284     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
To estimate the information transmitted across a neuronal sensory system one has to deal with serial dependence among consecutive samples of the stimulus and the response signal. Common methods usually require a huge amount of data, or are restricted to Gaussian stimuli. Here, we describe stimulus and response as stochastic processes, i.e. as sequences of random variables, in the same coordinate system. Stimulus-response pairs of these random variables must not be considered independently because otherwise the transinformation is overestimated. To account for the linear fraction of the serial dependence, we present two decorrelation techniques based on coordinate transformation. They provide a representation of the processes with uncorrelated random variables and yield a more precise estimate of the transinformation.
Authors:
Marcus Eger; Reinhard Eckhorn
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Bio Systems     Volume:  67     ISSN:  0303-2647     ISO Abbreviation:  BioSystems     Publication Date:    2002 Oct-Dec
Date Detail:
Created Date:  2002-12-02     Completed Date:  2003-06-26     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0430773     Medline TA:  Biosystems     Country:  Ireland    
Other Details:
Languages:  eng     Pagination:  55-65     Citation Subset:  IM    
Affiliation:
Department of Physics, Neurophysics Group, Philipps University, Renthof 7, Marburg 35032, Germany. marcus.eger@physik.uni-marburg.de
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MeSH Terms
Descriptor/Qualifier:
Animals
Cats
Models, Neurological*
Neurons, Afferent / physiology*
Normal Distribution
Synaptic Transmission / physiology*

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