Soft Pattern Mining in Neuroscience
- 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
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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