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A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts
2012
PLoS Computational Biology
Evaluating the importance of higher-order correlations of neural spike counts has been notoriously hard. A large number of samples are typically required in order to estimate higher-order correlations and resulting information theoretic quantities. In typical electrophysiology data sets with many experimental conditions, however, the number of samples in each condition is rather small. Here we describe a method that allows to quantify evidence for higher-order correlations in exactly these
doi:10.1371/journal.pcbi.1002539
pmid:22685392
pmcid:PMC3369943
fatcat:os4gjw7nqrgl3ale36dhfltwpy