The inverse gaussian distribution: theory, methodology, and applications
The inverse gaussian distribution: theory, methodology, and applications
Spikes: exploring the neural code
Spikes: exploring the neural code
Differences in spiking patterns among cortical neurons
Neural Computation
Mean instantaneous firing frequency is always higher than the firing rate
Neural Computation
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Parameters of spike trains observed in a short time window
Neural Computation
Discrimination with spike times and isi distributions
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
A characterization of the time-rescaled gamma process as a model for spike trains
Journal of Computational Neuroscience
Journal of Computational Neuroscience
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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.