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

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Brain, Vision, and Artificial Intelligence (BVAI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3704))

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Abstract

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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Author information

Authors and Affiliations

  1. Dept. of Mathematics, University of Torino, Via Carlo Alberto 10, 10123, Torino, Italy

    Laura Sacerdote & Cristina Zucca

Authors
  1. Laura Sacerdote
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  2. Cristina Zucca
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Editor information

Editors and Affiliations

  1. Istituto di Cibernetica “Eduardo Caianiello”, CNR, Pozzuoli (NA), Italy

    Massimo De Gregorio

  2. Istituto di Cibernetica “E. Caianiello” del CNR, Via Campi Flegrei, 34, 80078, Pozzuoli (NA), Italy

    Vito Di Maio

  3. Institute of Cybernetics “E. Caianiello”, CNR, Pozzuoli, (Naples), Italy

    Maria Frucci

  4. Istituto di Cibernetica “Eduardo Caianiello”, CNR, Pozzuoli, Italy

    Carlo Musio

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© 2005 Springer-Verlag Berlin Heidelberg

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Sacerdote, L., Zucca, C. (2005). Inverse First Passage Time Method in the Analysis of Neuronal Interspike Intervals of Neurons Characterized by Time Varying Dynamics. In: De Gregorio, M., Di Maio, V., Frucci, M., Musio, C. (eds) Brain, Vision, and Artificial Intelligence. BVAI 2005. Lecture Notes in Computer Science, vol 3704. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11565123_7

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  • DOI: https://doi.org/10.1007/11565123_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29282-1

  • Online ISBN: 978-3-540-32029-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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