Statistical smoothing of neuronal data.

Robert E Kass; Valérie Ventura; Can Cai

doi:10.1088/0954-898x/14/1/301

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Statistical smoothing of neuronal data.

Robert E Kass·Valérie Ventura·Can Cai

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Abstract

The purpose of smoothing (filtering) neuronal data is to improve the estimation of the instantaneous firing rate. In some applications, scientific interest centres on functions of the instantaneous firing rate, such as the time at which the maximal firing rate occurs or the rate of increase of firing rate over some experimentally relevant period. In others, the instantaneous firing rate is needed for probability-based calculations. In this paper we point to the very substantial gains in statistical efficiency from smoothing methods compared to using the peristimulus-time histogram (PSTH), and we also demonstrate a new method of adaptive smoothing known as Bayesian adaptive regression splines (DiMatteo 1, Genovese C R and Kass R E 2001 Biometrika 88 1055-71). We briefly review additional applications of smoothing with non-Poisson processes and in the joint PSTH for a pair of neurons.

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Action Potentials Bayes Theorem Data Interpretation, Statistical Models, Neurological Neurons Signal Processing, Computer-Assisted Statistical Distributions Time Factors Comparative Study Journal Article

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Robert E Kass, Valérie Ventura, Can Cai. 2003. Statistical smoothing of neuronal data.. https://doi.org/10.1088/0954-898x%2F14%2F1%2F301

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