Cross nearest-spike interval based method to measure synchrony dynamics

Montoro, Aldana M. González; Cao, Ricardo; Faes, Christel; Molenberghs, Geert; Espinosa, Nelson; Cudeiro, Javier; Mariño, Jorge

doi:10.3934/mbe.2014.11.27

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Common methods to measure synchrony are, for example, the cross-correlogram or the joint peristimulus time histogram [ 6 ]. Other methods not based on cross-correlation analysis include unitary event analysis [ 7 , 8 ], conditional synchrony measure [ 9 ] and a method based on the distances between the closest spikes [ 10 ], among others. Most commonly, association measures are used to test for the presence/absence of synchrony.…”

Section: Introductionmentioning

confidence: 99%

Functional two-way analysis of variance and bootstrap methods for neural synchrony analysis

et al. 2014

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BackgroundPairwise association between neurons is a key feature in understanding neural coding. Statistical neuroscience provides tools to estimate and assess these associations. In the mammalian brain, activating ascending pathways arise from neuronal nuclei located at the brainstem and at the basal forebrain that regulate the transition between sleep and awake neuronal firing modes in extensive regions of the cerebral cortex, including the primary visual cortex, where neurons are known to be selective for the orientation of a given stimulus. In this paper, the estimation of neural synchrony as a function of time is studied in data obtained from anesthetized cats. A functional data analysis of variance model is proposed. Bootstrap statistical tests are introduced in this context; they are useful tools for the study of differences in synchrony strength regarding 1) transition between different states (anesthesia and awake), and 2) affinity given by orientation selectivity.ResultsAn analysis of variance model for functional data is proposed for neural synchrony curves, estimated with a cross-correlation based method. Dependence arising from the experimental setting needs to be accounted for. Bootstrap tests allow the identification of differences between experimental conditions (modes of activity) and between pairs of neurons formed by cells with different affinities given by their preferred orientations. In our test case, interactions between experimental conditions and preferred orientations are not statistically significant.ConclusionsThe results reflect the effect of different experimental conditions, as well as the affinity regarding orientation selectivity in neural synchrony and, therefore, in neural coding. A cross-correlation based method is proposed that works well under low firing activity. Functional data statistical tools produce results that are useful in this context. Dependence is shown to be necessary to account for, and bootstrap tests are an appropriate method with which to do so.

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Section: Introductionmentioning

confidence: 99%

Functional two-way analysis of variance and bootstrap methods for neural synchrony analysis

et al. 2014

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Bootstrap testing for cross-correlation under low firing activity

et al. 2015

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A new cross-correlation synchrony index for neural activity is proposed. The index is based on the integration of the kernel estimation of the cross-correlation function. It is used to test for the dynamic synchronization levels of spontaneous neural activity under two induced brain states: sleep-like and awake-like. Two bootstrap resampling plans are proposed to approximate the distribution of the test statistics. The results of the first bootstrap method indicate that it is useful to discern significant differences in the synchronization dynamics of brain states characterized by a neural activity with low firing rate. The second bootstrap method is useful to unveil subtle differences in the synchronization levels of the awake-like state, depending on the activation pathway.

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Cross nearest-spike interval based method to measure synchrony dynamics

Cited by 2 publications

References 13 publications

Functional two-way analysis of variance and bootstrap methods for neural synchrony analysis

Functional two-way analysis of variance and bootstrap methods for neural synchrony analysis

Bootstrap testing for cross-correlation under low firing activity

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