In order to understand how olfactory stimuli are encoded and processed in the brain, it is important to build a computational model for olfactory receptor neurons (ORNs). Here, we present a simple and reliable mathematical model of a moth ORN generating spikes. The model incorporates a simplified description of the chemical kinetics leading to olfactory receptor activation and action potential generation. We show that an adaptive spike threshold regulated by prior spike history is an effective mechanism for reproducing the typical phasic-tonic time course of ORN responses. Our model reproduces the response dynamics of individual neurons to a fluctuating stimulus that approximates odorant fluctuations in nature. The parameters of the spike threshold are essential for reproducing the response heterogeneity in ORNs. The model provides a valuable tool for efficient simulations of olfactory circuits.

• Klíčová slova
• adaptive threshold, integrate-and-fire model, olfactory receptor neuron,
• MeSH
• akční potenciály fyziologie   MeSH
• biologické modely MeSH
• čichové buňky účinky léků   fyziologie   MeSH
• elektrofyziologické jevy MeSH
• fyziologická adaptace * MeSH
• můry fyziologie   MeSH
• sexuální lákadla farmakologie   MeSH
• zvířata MeSH
• Check Tag
• mužské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• sexuální lákadla MeSH

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.

• MeSH
• akční potenciály fyziologie   MeSH
• buněčná membrána fyziologie   MeSH
• lidé MeSH
• mozek fyziologie   MeSH
• nervové dráhy fyziologie   MeSH
• nervový přenos fyziologie   MeSH
• neuronové sítě * MeSH
• neurony fyziologie   MeSH
• počítačové zpracování signálu MeSH
• Poissonovo rozdělení MeSH
• stochastické procesy MeSH
• synapse fyziologie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Diffusion processes have been extensively used to describe membrane potential behavior. In this approach the interspike interval has a theoretical counterpart in the first-passage-time of the diffusion model employed. Since the mathematical complexity of the first-passage-time problem increases with attempts to make the models more realistic it seems useful to compare the features of different models in order to highlight their relative performance. In this paper we compare the Feller and Ornstein-Uhlenbeck models under three different criteria derived from the level of information available about their parameters. We conclude that the Feller model is preferable when complete knowledge of the characterizing parameters is assumed. On the other hand, when only limited information about the parameters is available, such as the mean firing time and the histogram shape, no advantage arises from using this more complex model.

• MeSH
• difuze MeSH
• membránové potenciály fyziologie   MeSH
• modely neurologické * MeSH
• neuronové sítě MeSH
• neurony fyziologie   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH

The effect of a variable initial value is examined in Stein's stochastic neuronal model with synaptic reversal potentials under the conditions of a constant threshold and a constant input. The moments of the interspike interval distribution are presented as the functions of the initial depolarization which ranges from inhibitory reversal potential to the threshold potential. Normal, exponential and transformed Gamma distributions are tested for the initial value of depolarization. The coefficient of variation is shown to be greater than one when the initial depolarization is sufficiently above the resting level. An interpretation of this result in the terms of spatial facilitation is offered. The effect of a random initial value is found to be most pronounced for the neurons depolarized to a near threshold level.

• MeSH
• matematika MeSH
• membránové potenciály MeSH
• modely neurologické * MeSH
• neurony fyziologie   MeSH
• synapse fyziologie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH