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Bulletin of Mathematical Biology

, Volume 68, Issue 6, pp 1257–1274 | Cite as

On the Classification of Experimental Data Modeled Via a Stochastic Leaky Integrate and Fire Model Through Boundary Values

  • L. Sacerdote
  • A. E. P. Villa
  • C. Zucca
Original Article

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.

Keywords

Neuron Interspike times Leaky integrate and fire Ornstein-Uhlenbeck Inverse first passage time problem Fano factor Gamma distribution 

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Copyright information

© Society for Mathematical Biology 2006

Authors and Affiliations

  1. 1.Department of MathematicsUniversity of TorinoCarlo Alberto 10Italy
  2. 2.INSERM, U318, Laboratoire de NeurobiophysiqueUniversity Joseph FourierGrenobleFrance
  3. 3.Neuroheuristic Research Group, Institute of Computer Science and Organization INFORGEUniversity of LausanneLausanneSwitzerland

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