Detecting dependencies between spike trains of pairs of neurons through copulas

Sacerdote, Laura; Tamborrino, Massimiliano; Zucca, Cristina

doi:10.1016/j.brainres.2011.08.064

Cited by 21 publications

(32 citation statements)

References 25 publications

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“…In the analysis of spike train data, the distribution of the first-spike latency has been used to estimate the conditional entropy of neural responses (Jenison and Reale, 2004). The neural dependencies have been characterized by copula models based on the distribution of either the spike counts (Berkes et al, 2009) or the interspike intervals (Sacerdote et al, 2012;Hu et al, 2015). A recent study has shown that the synchronous spiking among multiple neurons can be detected using the copula model, whereby the parameters in the model can be estimated within a semiparametric Bayesian framework (Shahbaba et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Copula Regression Analysis of Simultaneously Recorded Frontal Eye Field and Inferotemporal Spiking Activity during Object-Based Working Memory

Clark²,

Gong

et al. 2015

Journal of Neuroscience

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Inferotemporal (IT) neurons are known to exhibit persistent, stimulus-selective activity during the delay period of object-based working memory tasks. Frontal eye field (FEF) neurons show robust, spatially selective delay period activity during memory-guided saccade tasks. We present a copula regression paradigm to examine neural interaction of these two types of signals between areas IT and FEF of the monkey during a working memory task. This paradigm is based on copula models that can account for both marginal distribution over spiking activity of individual neurons within each area and joint distribution over ensemble activity of neurons between areas. Considering the popular GLMs as marginal models, we developed a general and flexible likelihood framework that uses the copula to integrate separate GLMs into a joint regression analysis. Such joint analysis essentially leads to a multivariate analog of the marginal GLM theory and hence efficient model estimation. In addition, we show that Granger causality between spike trains can be readily assessed via the likelihood ratio statistic. The performance of this method is validated by extensive simulations, and compared favorably to the widely used GLMs. When applied to spiking activity of simultaneously recorded FEF and IT neurons during working memory task, we observed significant Granger causality influence from FEF to IT, but not in the opposite direction, suggesting the role of the FEF in the selection and retention of visual information during working memory. The copula model has the potential to provide unique neurophysiological insights about network properties of the brain.

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Section: Discussionmentioning

confidence: 99%

Copula Regression Analysis of Simultaneously Recorded Frontal Eye Field and Inferotemporal Spiking Activity during Object-Based Working Memory

Clark²,

Gong

et al. 2015

Journal of Neuroscience

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show abstract

“…In real neuronal data, however, the non-renewal property of the spike trains is often observed (Farkhooi et al, 2009;Avila-Akerberg and Chacron, 2011). For such situations, as well as for the situations when the individual spike trains are not independent (Sacerdote et al, 2012), the results presented here have to be taken only as a preliminary hint and more specialized methods should be developed.…”

Section: Introductionmentioning

confidence: 95%

Estimating individual firing frequencies in a multiple spike train record

Pokora

Lánský

2012

Journal of Neuroscience Methods

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“…Other researchers have developed methods for assessing similarity and correlations in spike trains (Sacerdote et al, 2012;Lyttle and Fellous, 2011). Interesting recent approaches for the inference of functional connectivity between neurons in a network using nonparametric statistics include Ostojic et al (2009) and Eldawlatly et al (2008).…”

Section: Introductionmentioning

confidence: 99%