Soft Pattern Mining in Neuroscience

Conference paper

DOI: 10.1007/978-3-642-33042-1_1

Volume 190 of the book series Advances in Intelligent Systems and Computing (AISC)
Cite this paper as:
Borgelt C. (2013) Soft Pattern Mining in Neuroscience. In: Kruse R., Berthold M., Moewes C., Gil M., Grzegorzewski P., Hryniewicz O. (eds) Synergies of Soft Computing and Statistics for Intelligent Data Analysis. Advances in Intelligent Systems and Computing, vol 190. Springer, Berlin, Heidelberg

Abstract

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 temporalimprecision and selectiveparticipation, can help to test the temporalcoincidencecodinghypothesis. Future challenges consist in extending these methods, in particular to the case of spatio − temporalcoding.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.European Centre for Soft ComputingMieresSpain