title: The parameters of the stochastic leaky integrate-and-fire neuronal model.
abstract: Five parameters of one of the most common neuronal models, the diffusion leaky integrate-and-fire model, also known as the Ornstein-Uhlenbeck neuronal model, were estimated on the basis of intracellular recording. These parameters can be classified into two categories. Three of them (the membrane time constant, the resting potential and the firing threshold) characterize the neuron itself. The remaining two characterize the neuronal input. The intracellular data were collected during spontaneous firing, which in this case is characterized by a Poisson process of interspike intervals. Two methods for the estimation were applied, the regression method and the maximum-likelihood method. Both methods permit to estimate the input parameters and the membrane time constant in a short time window (a single interspike interval). We found that, at least in our example, the regression method gave more consistent results than the maximum-likelihood method. The estimates of the input parameters show the asymptotical normality, which can be further used for statistical testing, under the condition that the data are collected in different experimental situations. The model neuron, as deduced from the determined parameters, works in a subthreshold regimen. This result was confirmed by both applied methods. The subthreshold regimen for this model is characterized by the Poissonian firing. This is in a complete agreement with the observed interspike interval data.
authors.affiliation: Institute of Physiology, Academy of Sciences of the Czech Republic, Prague, Czech Republic. lansky@biomed.cas.cz
authors.forename: Petr
authors.initials: P
authors.lastname: Lansky
authors.forename: Pavel
authors.initials: P
authors.lastname: Sanda
authors.forename: Jufang
authors.initials: J
authors.lastname: He
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001921:Brain / Q000502:physiology*; D002462:Cell Membrane / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D016012:Poisson Distribution; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes; D013569:Synapses / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology*