Neural Networks - Special issue on organisation of computation in brain-like systems
Differences in spiking patterns among cortical neurons
Neural Computation
A Spike-Train Probability Model
Neural Computation
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Compositionality of arm movements can be realized by propagating synchrony
Journal of Computational Neuroscience
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Journal of Computational Neuroscience
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Spike time irregularity can be measured by the coefficient of variation. However, it overestimates the irregularity in the case of pronounced firing rate changes. Several alternative measures that are local in time and therefore relatively rate-independent were proposed. Here we compared four such measures: CV2, LV, IR and SI. First, we asked which measure is the most efficient for time-resolved analyses of experimental data. Analytical results show that CV2 has the less variable estimates. Second, we derived useful properties of CV2 for gamma processes. Third, we applied CV2 on recordings from the motor cortex of a monkey performing a delayed motor task to characterize the irregularity, that can be modulated or not, and decoupled or not from firing rate. Neurons with a CV2-rate decoupling have a rather constant CV2 and discharge mainly irregularly. Neurons with a CV2-rate coupling can modulate their CV2 and explore a larger range of CV2 values.