Neural Spike Train Synchronization Indices: Definitions, Interpretations, and Applications [Електронний ресурс] / David M. Halliday, J. R. Rosenberg, P. Breeze, B. A. Conway // IEEE Transactions on Biomedical Engineering [Електронний ресурс]. – 2006. – № 6. – Pp. 1056 – 1066
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Статистика використання: Завантажень: 1
Складова документа:
IEEE Transactions on Biomedical Engineering [Електронний ресурс] : вестник ин-та радиоинженеров. № 6. 53 / IEEE Engineering in medicine and Biology Group // IEEE Transactions on Biomedical Engineering. – USA, 2006
Анотація:
A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second-order cumulant density (covariance
density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second-order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% to 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a
density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second-order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% to 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a