We present a computational algorithm aimed to classify single unit spike trains on the basis of observed interspikes intervals (ISI). The neuronal activity is modeled with a stochastic leaky integrate and fire model and the inverse first passage time method is extended to the Ornstein-Uhlenbeck (OU) process. Differences between spike trains are detected in terms of the boundary shape. The proposed classification method is applied to the analysis of multiple single units recorded simultaneously in the thalamus and in the cerebral cortex of unanesthetized rats during spontaneous activity.We show the existence of at least three different firing patterns that could not be classified using the usual statistical indices.

On the classification of experimental data modeled via a stochastic leaky integrate and fire model through boundary values

SACERDOTE, Laura Lea;ZUCCA, CRISTINA
2006

Abstract

We present a computational algorithm aimed to classify single unit spike trains on the basis of observed interspikes intervals (ISI). The neuronal activity is modeled with a stochastic leaky integrate and fire model and the inverse first passage time method is extended to the Ornstein-Uhlenbeck (OU) process. Differences between spike trains are detected in terms of the boundary shape. The proposed classification method is applied to the analysis of multiple single units recorded simultaneously in the thalamus and in the cerebral cortex of unanesthetized rats during spontaneous activity.We show the existence of at least three different firing patterns that could not be classified using the usual statistical indices.
68
1257
1264
FIrst Passage time; Inverse first passage time problem; Interspikes Intervals; Leaky Integrate and Fire model; Ornstein-Uhlenbeck process; Gamma distribution
Sacerdote L.; Villa A.E.P.; Zucca C.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/9713
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