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An accretion based data mining algorithm for identification of sets of correlated neurons
© Berger et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Assemblies of synchronously active neurons were suggested as the key mechanism for cortical information processing. Testing this hypothesis requires to observe large sets of neurons simultaneously, which is possible now due to recent advancements in electrophysiology. However, tools for analyzing such massively parallel data are lagging behind. Mere pairwise analysis is not sufficient to reliably detect synchronous spike patterns involving larger groups of neurons, and thus do not allow to conclusively identify assemblies. Instead methods that consider higher-order correlations are needed. Available tools for correlation analysis are not applicable, either because of the expected combinatorial explosion due to the required consideration of all individual spike patterns [1, 2], or, the methods are not designed to identify the specific set of assembly neurons [3–6].
Partially funded by BCCN Berlin (01GQ0413) and Helmholtz Alliance on Systems Biology.
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