Soft Pattern Mining in Neuroscience

  • Christian BorgeltEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 190)


While the lower-level mechanisms of neural information processing (in biological neural networks) are fairly well understood, the principles of higher-level processing remain a topic of intense debate in the neuroscience community. With many theories competing to explain how stimuli are encoded in nerve signal (spike) patterns, data analysis tools are desired by which proper tests can be carried out on recorded parallel spike trains. This paper surveys how pattern mining methods, especially soft methods that tackle the core problems of temporal imprecision and selective participation, can help to test the temporal coincidence coding hypothesis. Future challenges consist in extending these methods, in particular to the case of spatio − temporal coding.


Spike Train Pattern Mining Frequency Code Neuroscience Community Synchronous Spike 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.European Centre for Soft ComputingMieresSpain

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