Document Detail


Causal pattern recovery from neural spike train data using the Snap Shot Score.
MedLine Citation:
PMID:  19644745     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
We present a new approach to learning directed information flow networks from multi-channel spike train data. A novel scoring function, the Snap Shot Score, is used to assess potential networks with respect to their quality of causal explanation for the data. Additionally, we suggest a generic concept of plausibility in order to assess network learning techniques under partial observability conditions. Examples demonstrate the assessment of networks with the Snap Shot Score, and neural network simulations show its performance in complex situations with partial observability. We discuss the application of the new score to real data and indicate how it can be modified to suit other neural data types.
Authors:
Christoph Echtermeyer; Tom V Smulders; V Anne Smith
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2009-07-31
Journal Detail:
Title:  Journal of computational neuroscience     Volume:  29     ISSN:  1573-6873     ISO Abbreviation:  J Comput Neurosci     Publication Date:  2010 Aug 
Date Detail:
Created Date:  2010-09-16     Completed Date:  2011-01-05     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9439510     Medline TA:  J Comput Neurosci     Country:  United States    
Other Details:
Languages:  eng     Pagination:  231-52     Citation Subset:  IM    
Affiliation:
School of Biology, University of St Andrews, St Andrews, UK. ce86@st-andrews.ac.uk
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MeSH Terms
Descriptor/Qualifier:
Action Potentials / physiology*
Algorithms
Animals
Computer Simulation
Information Services
Neural Networks (Computer)*
Neurons / physiology*
Pattern Recognition, Automated
Time Factors

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


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