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A Framework for Evaluating Pairwise and Multiway Synchrony Among StimulusDriven Neurons
"... Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for timevarying firing rates that typically appear in stimulusdriven (or ac ..."
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Several authors have discussed previously the use of loglinear models, often called maximum entropy models, for analyzing spike train data to detect synchrony. The usual loglinear modeling techniques, however, do not allow for timevarying firing rates that typically appear in stimulusdriven (or actiondriven) neurons, nor do they incorporate nonPoisson history effects or covariate effects. We generalize the usual approach, combining point process regression models of individualneuron activity with loglinear models of multiway synchronous interaction. The methods are illustrated with results found in spike trains recorded simultaneously from primary visual cortex. We then go on to assess the amount of data needed to reliably detect multiway spiking.
False discovery rate regression: an application to neural synchrony detection in primary visual cortex
, 2013
"... Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global falsediscoveryrate analysis. But this may be inappropriate for many of today’s largescale screening problems, where auxiliary information about each test is often avail ..."
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Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global falsediscoveryrate analysis. But this may be inappropriate for many of today’s largescale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called falsediscoveryrate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a twogroups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed falsediscovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit finetimescale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50 % more synchronous pairs versus a standard FDRcontrolling analysis. The companion R package FDRreg implements all methods described in the paper.
neural synchrony detection in primary visual cortex
, 2013
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
1A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons
"... We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1’s (spike) and 0’s (sil ..."
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We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their cofiring (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1’s (spike) and 0’s (silence) for each neuron is modeled using the logistic ar X iv