Inverse First Passage Time Method in the Analysis of Neuronal Interspike Intervals of Neurons Characterized by Time Varying Dynamics

We propose a new method to analyze time series recorded by single neuronal units in order to identify possible differences in the time evolution of the considered neuron. The effect of different dynamics is artificially concentrated in the boundary shape by means of the inverse first passage time method applied to the stochastic leaky integrate and fire model. In particular, the evolution in the dynamics is recognized by means of a suitable time window fragmentation on the observed data and the repeated use of the inverse first passage time algorithm. The comparison of the boundary shapes in the different time windows detects this evolution. A simulation example of the method and its biological implications are discussed.

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