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Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal

Picchini, Umberto LU ; Ditlevsen, Susanne; De Gaetano, Andrea and Lansky, Petr (2008) In Neural Computation 20(11). p.2696-2714
Abstract
Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this article, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal... (More)
Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this article, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal input is split into fast (within-interval) and slow (between-intervals) components. A parameter estimation method is proposed, allowing the parameters to be estimated simultaneously over the entire data set. This increases the statistical power, and the average estimate over all ISIs will be improved in the sense of decreased variance of the estimator compared to previous approaches, where the estimation has been conducted separately on each individual ISI. The results obtained on real data show good agreement with classical regression methods. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
in
Neural Computation
volume
20
issue
11
pages
2696 - 2714
publisher
MIT Press
external identifiers
  • scopus:55749107079
ISSN
1530-888X
DOI
10.1162/neco.2008.11-07-653
language
English
LU publication?
no
id
1362eea7-7365-4760-9ae3-35a2aa3a56c1 (old id 4215994)
date added to LUP
2014-01-13 14:34:38
date last changed
2017-01-01 04:34:05
@article{1362eea7-7365-4760-9ae3-35a2aa3a56c1,
  abstract     = {Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this article, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal input is split into fast (within-interval) and slow (between-intervals) components. A parameter estimation method is proposed, allowing the parameters to be estimated simultaneously over the entire data set. This increases the statistical power, and the average estimate over all ISIs will be improved in the sense of decreased variance of the estimator compared to previous approaches, where the estimation has been conducted separately on each individual ISI. The results obtained on real data show good agreement with classical regression methods.},
  author       = {Picchini, Umberto and Ditlevsen, Susanne and De Gaetano, Andrea and Lansky, Petr},
  issn         = {1530-888X},
  language     = {eng},
  number       = {11},
  pages        = {2696--2714},
  publisher    = {MIT Press},
  series       = {Neural Computation},
  title        = {Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal},
  url          = {http://dx.doi.org/10.1162/neco.2008.11-07-653},
  volume       = {20},
  year         = {2008},
}