Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4215994
- author
- Picchini, Umberto LU ; Ditlevsen, Susanne ; De Gaetano, Andrea and Lansky, Petr
- publishing date
- 2008
- 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
- 2016-04-01 11:48:50
- date last changed
- 2022-01-26 18:35:47
@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 = {{https://lup.lub.lu.se/search/files/2653360/4215995}},
doi = {{10.1162/neco.2008.11-07-653}},
volume = {{20}},
year = {{2008}},
}