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• W2013171232 endingPage "2032" @default.
• W2013171232 startingPage "2007" @default.
• W2013171232 abstract "Several authors have previously discussed the use of log-linear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual log-linear modeling techniques, however, do not allow time-varying firing rates that typically appear in stimulus-driven (or action-driven) neurons, nor do they incorporate non-Poisson history effects or covariate effects. We generalize the usual approach, combining point-process regression models of individual neuron activity with log-linear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then assess the amount of data needed to reliably detect multiway spiking." @default.
• W2013171232 created "2016-06-24" @default.
• W2013171232 creator A5026044539 @default.
• W2013171232 creator A5030912263 @default.
• W2013171232 date "2012-08-01" @default.
• W2013171232 modified "2023-09-26" @default.
• W2013171232 title "A Framework for Evaluating Pairwise and Multiway Synchrony Among Stimulus-Driven Neurons" @default.
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• W2013171232 doi "https://doi.org/10.1162/neco_a_00307" @default.
• W2013171232 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3374919" @default.
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