Correcting for the Sampling Bias Problem in Spike Train Information Measures

Panzeri, Stefano; Senatore, R; Montemurro, Marcelo A.; Petersen, Rasmus S.

doi:10.1152/jn.00559.2007

Cited by 396 publications

(465 citation statements)

References 26 publications

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“…A wide variety of approaches have been proposed to address this problem [Paninski, 2003; Panzeri et al, 2007]. The simplest approach is to subtract the mean of the distribution expected under the null hypothesis that there is no relationship between the two variables.…”

Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

249

298

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We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

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Section: Review Of Information Theory For Neuroimagingmentioning

confidence: 99%

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

249

298

View full text Add to dashboard Cite

show abstract

“…We calculate the mutual information in the merged partner sequence, denoted by I merge i , which measures the predictability of conversation events that does not result from the burstiness. We do not directly compare I merge i with the original I i because the merging procedure shortens the length of the partner sequence and the amount of mutual information generally depends on the length of a sequence [36]. Instead, we carry out a bootstrap test for I merge i .…”

Section: Appendix D: Components Of the Predictability Of The Conversamentioning

confidence: 99%

Predictability of Conversation Partners

et al. 2011

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Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cellphone locations, [C. Song et al., Science 327, 1018(2010] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by longtailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community-in the sense of an abundance of surrounding triangles-tend to have low predictability, and those bridging different communities tend to have high predictability.

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“…This estimation of MI is different from nonparametric approaches in that it can access dependence that is only in reach of the classifier; thus one has to make sure that the classifier captures the main aspects of its dependence. Note that we use the naive estimator for mutual information [without bias correction (Panzeri et al 2007)]. Since all MI value calculations involve an identical number of bins-that is, two, one for each class-we can nevertheless safely compare results even for classifications with different numbers of features.…”

Section: I͓smentioning

confidence: 99%

Inferring Spike Trains From Local Field Potentials

Rasch¹,

Gretton²,

Murayama³

et al. 2008

Journal of Neurophysiology

204

200

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We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation across electrodes, the low-frequency structure in spike trains (in the 100-ms range) can be inferred with reasonable accuracy, whereas exact spike positions are not reliably predicted. Two kinds of features of the LFP are exploited for prediction: the frequency power of bands in the high ␥-range (40 -90 Hz) and information contained in lowfrequency oscillations (Ͻ10 Hz), where both phase and power modulations are informative. Information analysis revealed that both features code (mainly) independent aspects of the spike-to-LFP relationship, with the low-frequency LFP phase coding for temporally clustered spiking activity. Although both features and prediction quality are similar during seminatural movie stimuli and spontaneous activity, prediction performance during spontaneous activity degrades much more slowly with increasing electrode distance. The general trend of data obtained with anesthetized animals is qualitatively mirrored in that of a more limited data set recorded in V1 of non-anesthetized monkeys. In contrast to the cortical field potentials, thalamic LFPs (e.g., LFPs derived from recordings in the dorsal lateral geniculate nucleus) hold no useful information for predicting spiking activity.

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Correcting for the Sampling Bias Problem in Spike Train Information Measures

Cited by 396 publications

References 26 publications

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Predictability of Conversation Partners

Inferring Spike Trains From Local Field Potentials

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