Neural Comput - Firing variability is higher than deduced from the empirical coefficient of variation.

Tópicos

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Resumo

A convenient and often used summary measure to quantify the firing variability in neurons is the coefficient of variation (CV), defined as the standard deviation divided by the mean. It is therefore important to find an estimator that gives reliable results from experimental data, that is, the estimator should be unbiased and have low estimation variance. When the CV is evaluated in the standard way (empirical standard deviation of interspike intervals divided by their average), then the estimator is biased, underestimating the true CV, especially if the distribution of the interspike intervals is positively skewed. Moreover, the estimator has a large variance for commonly used distributions. The aim of this letter is to quantify the bias and propose alternative estimation methods. If the distribution is assumed known or can be determined from data, parametric estimators are proposed, which not only remove the bias but also decrease the estimation errors. If no distribution is assumed and the data are very positively skewed, we propose to correct the standard estimator. When defining the corrected estimator, we simply use that it is more stable to work on the log scale for positively skewed distributions. The estimators are evaluated through simulations and applied to experimental data from olfactory receptor neurons in rats.

Resumo Limpo

conveni often use summari measur quantifi fire variabl neuron coeffici variat cv defin standard deviat divid mean therefor import find estim give reliabl result experiment data estim unbias low estim varianc cv evalu standard way empir standard deviat interspik interv divid averag estim bias underestim true cv especi distribut interspik interv posit skew moreov estim larg varianc common use distribut aim letter quantifi bias propos altern estim method distribut assum known can determin data parametr estim propos remov bias also decreas estim error distribut assum data posit skew propos correct standard estim defin correct estim simpli use stabl work log scale posit skew distribut estim evalu simul appli experiment data olfactori receptor neuron rat

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