- Poster presentation
- Open Access
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
- Spike Train
- Data Mining Algorithm
- Data Mining Approach
- Spike Pattern
- Assembly Neuron
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.
- Grün S, Diesmann M, Aertsen A: Neural Computation. 2002, 14: 43-80.PubMedView ArticleGoogle Scholar
- Shimazaki H, Amari S, Brown E, Grün S: Proc IEEE Intern Conf Acoustics, Speech, Signal Proc. (ICASSP). 2009Google Scholar
- Staude B, Rotter S, Grün S: Soc Neurosci, Program No. 103.9. San Diego. 2007Google Scholar
- Grün S, Abeles M, Diesmann M: Lecture Notes in Computer Science. 2008, 5286: 96-114.View ArticleGoogle Scholar
- Louis S, Grün S: CNS Meeting. 2009Google Scholar
- Grün S, Berger D, Borgelt C: Proc IEEE Intern Conf Acoustics, Speech, Signal Proc. (ICASSP). 2009Google Scholar
- Gerstein GL, Perkel DH, Subramanian KN: Brain Research. 1978, 140: 43-62.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd.