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Statistical Identification of Synchronous Spiking
"... 3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7 ..."
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Cited by 4 (3 self)
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3 Spike trains and firing rate 5 3.1 Point processes, conditional intensities, and firing rates.............. 7
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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Cited by 1 (0 self)
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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