Paper Info
Title
Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal.
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 letter, 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 on each individual ISI. The results obtained on real data show good agreement with classical regression methods.
Year
DOI
Venue
2008
10.1162/neco.2008.11-07-653
Neural Computation
Field
DocType
Volume
Statistical physics,Random variable,Mathematical optimization,Regression analysis,Models of neural computation,Stochastic modelling,Estimation theory,Artificial neural network,Statistics,Statistical power,Mathematics,Estimator
Journal
20
Issue
ISSN
Citations 
11
0899-7667
6
PageRank 
References 
Authors
1.15
9
4
Name
Order
Citations
PageRank
Umberto Picchini192.99
Susanne Ditlevsen2577.84
Andrea De Gaetano361.15
Petr Lansky412515.94