One-dimensional leaky integrate and fire neuronal models describe interspike intervals (ISIs) of a neuron as a renewal process and disregarding the neuron geometry. Many multi-compartment models account for the geometrical features of the neuron but are too complex for their mathematical tractability. Leaky integrate and fire two-compartment models seem a good compromise between mathematical tractability and an improved realism. They indeed allow to relax the renewal hypothesis, typical of one-dimensional models, without introducing too strong mathematical difficulties. Here, we pursue the analysis of the two-compartment model studied by Lansky and Rodriguez (Phys D 132:267–286, 1999), aiming of introducing some specific mathematical results used together with simulation techniques. With the aid of these methods, we investigate dependency properties of ISIs for different values of the model parameters. We show that an increase of the input increases the strength of the dependence between successive ISIs.

On dependency properties of the ISIs generated by a two-compartmental neuronal model / Elisa Benedetto; Laura Sacerdote. - In: BIOLOGICAL CYBERNETICS. - ISSN 0340-1200. - 107:1(2013), pp. 95-106.

On dependency properties of the ISIs generated by a two-compartmental neuronal model

BENEDETTO, ELISA;SACERDOTE, Laura Lea
2013

Abstract

One-dimensional leaky integrate and fire neuronal models describe interspike intervals (ISIs) of a neuron as a renewal process and disregarding the neuron geometry. Many multi-compartment models account for the geometrical features of the neuron but are too complex for their mathematical tractability. Leaky integrate and fire two-compartment models seem a good compromise between mathematical tractability and an improved realism. They indeed allow to relax the renewal hypothesis, typical of one-dimensional models, without introducing too strong mathematical difficulties. Here, we pursue the analysis of the two-compartment model studied by Lansky and Rodriguez (Phys D 132:267–286, 1999), aiming of introducing some specific mathematical results used together with simulation techniques. With the aid of these methods, we investigate dependency properties of ISIs for different values of the model parameters. We show that an increase of the input increases the strength of the dependence between successive ISIs.
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http://http://link.springer.com/article/10.1007/s00422-012-0536-0/fulltext.html
Two-compartment neural model; ISI dependency properties
Elisa Benedetto; Laura Sacerdote
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/129385
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