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
• financování organizované 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

An optimum signal in the Ornstein-Uhlenbeck neuronal model is determined on the basis of interspike interval data. Two criteria are proposed for this purpose. The first, the classical one, is based on searching for maxima of the slope of the frequency transfer function. The second one uses maximum of the Fisher information, which is, under certain conditions, the inverse variance of the best possible estimator. The Fisher information is further normalized with respect to the time required to make the observation on which the signal estimation is performed. Three variants of the model are investigated. Beside the basic one, we use the version obtained by inclusion of the refractory period. Finally, we investigate such a version of the model in which signal and the input parameter of the model are in a nonlinear relationship. The results show that despite qualitative similarity between the criteria, there is substantial quantitative difference. As a common feature, we found that in the Ornstein-Uhlenbeck model with increasing noise the optimum signal decreases and the coding range gets broader.

• MeSH
• akční potenciály fyziologie   MeSH
• algoritmy MeSH
• financování organizované MeSH
• lidé MeSH
• membránové potenciály fyziologie   MeSH
• modely neurologické MeSH
• nervové vedení fyziologie   MeSH
• neurony fyziologie   MeSH
• refrakterní doba elektrofyziologická fyziologie   MeSH
• stochastické procesy MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH

Statistical properties of spike trains as well as other neurophysiological data suggest a number of mathematical models of neurons. These models range from entirely descriptive ones to those deduced from the properties of the real neurons. One of them, the diffusion leaky integrate-and-fire neuronal model, which is based on the Ornstein-Uhlenbeck (OU) stochastic process that is restricted by an absorbing barrier, can describe a wide range of neuronal activity in terms of its parameters. These parameters are readily associated with known physiological mechanisms. The other model is descriptive, Gamma renewal process, and its parameters only reflect the observed experimental data or assumed theoretical properties. Both of these commonly used models are related here. We show under which conditions the Gamma model is an output from the diffusion OU model. In some cases, we can see that the Gamma distribution is unrealistic to be achieved for the employed parameters of the OU process.

• MeSH
• difuze * MeSH
• kybernetika MeSH
• modely neurologické * MeSH
• neurony * MeSH
• stochastické procesy MeSH
• Publikační typ
• časopisecké články MeSH

The Ornstein-Uhlenbeck neuronal model is specified by two types of parameters. One type corresponds to the properties of the neuronal membrane, whereas the second type (local average rate of the membrane depolarization and its variability) corresponds to the input of the neuron. In this article, we estimate the parameters of the second type from an intracellular record during neuronal firing caused by stimulation (audio signal). We compare the obtained estimates with those from the spontaneous part of the record. As predicted from the model construction, the values of the input parameters are larger for the periods when neuron is stimulated than for the spontaneous ones. Finally, the firing regimen of the model is checked. It is confirmed that the neuron is in the suprathreshold regimen during the stimulation.

• MeSH
• akční potenciály fyziologie   MeSH
• akustická stimulace metody   MeSH
• časové faktory MeSH
• elektroencefalografie MeSH
• membránové potenciály fyziologie   MeSH
• modely neurologické MeSH
• morčata MeSH
• nervové dráhy fyziologie   MeSH
• neurony klasifikace   fyziologie   MeSH
• počítačové zpracování signálu MeSH
• reakční čas fyziologie   MeSH
• stochastické procesy MeSH
• zvířata MeSH
• Check Tag
• morčata MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Parameters in diffusion neuronal models are divided into two groups; intrinsic and input parameters. Intrinsic parameters are related to the properties of the neuronal membrane and are assumed to be known throughout the paper. Input parameters characterize processes generated outside the neuron and methods for their estimation are reviewed here. Two examples of the diffusion neuronal model, which are based on the integrate-and-fire concept, are investigated--the Ornstein--Uhlenbeck model as the most common one and the Feller model as an illustration of state-dependent behavior in modeling the neuronal input. Two types of experimental data are assumed-intracellular describing the membrane trajectories and extracellular resulting in knowledge of the interspike intervals. The literature on estimation from the trajectories of the diffusion process is extensive and thus the stress in this review is set on the inference made from the interspike intervals.

• MeSH
• modely neurologické MeSH
• neurony MeSH
• Publikační typ
• práce podpořená grantem MeSH
• přehledy MeSH

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.

• 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

The limits on maximum information that can be transferred by single neurons help us to understand how sensory and other information is being processed in the brain. In this paper we approximately calculate the information capacity of the perfect integrate-and-fire neuronal model in dependence on the stimulus range, assuming the simplest form of temporal coding scheme. We couple the information transfer with metabolic cost of neuronal activity and we find that the optimal information per metabolic cost ratios may occur for a relatively small stimulus range. This article is part of a Special Issue entitled Neural Coding.

• MeSH
• akční potenciály fyziologie   MeSH
• časové faktory MeSH
• Haplorrhini MeSH
• modely neurologické MeSH
• náhodné rozdělení MeSH
• neurony fyziologie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

We present a comparison of the intrinsic saturation of firing frequency in four simple neural models: leaky integrate-and-fire model, leaky integrate-and-fire model with reversal potentials, two-point leaky integrate-and-fire model, and a two-point leaky integrate-and-fire model with reversal potentials. "Two-point" means that the equivalent circuit has two nodes (dendritic and somatic) instead of one (somatic only). The results suggest that the reversal potential increases the slope of the "firing rate vs input" curve due to a smaller effective membrane time constant, but does not necessarily induce saturation of the firing rate. The two-point model without the reversal potential does not limit the voltage or the firing rate. In contrast to the previous models, the two-point model with the reversal potential limits the asymptotic voltage and the firing rate, which is the main result of this paper. The case of excitatory inputs is considered first and followed by the case of both excitatory and inhibitory inputs.

• MeSH
• akční potenciály fyziologie   MeSH
• fyzikální jevy MeSH
• membránové potenciály fyziologie   MeSH
• modely neurologické * MeSH
• neurony * fyziologie   MeSH
• Publikační typ
• časopisecké články MeSH

Due to known information processing capabilities of the brain, neurons are modeled at many different levels. Circuit theory is also often used to describe the function of neurons, especially in complex multi-compartment models, but when used for simple models, there is no subsequent biological justification of used parts. We propose a new single-compartment model of excitatory and inhibitory neuron, the capacitor-switch model of excitatory and inhibitory neuron, as an extension of the existing integrate-and-fire model, preserving the signal properties of more complex multi-compartment models. The correspondence to existing structures in the neuronal cell is then discussed for each part of the model. We demonstrate that a few such inter-connected model units are capable of acting as a chaotic oscillator dependent on fire patterns of the input signal providing a complex deterministic and specific response through the output signal. The well-known necessary conditions for constructing a chaotic oscillator are met for our presented model. The capacitor-switch model provides a biologically-plausible concept of chaotic oscillator based on neuronal cells.

• MeSH
• akční potenciály fyziologie   MeSH
• modely neurologické MeSH
• mozek metabolismus   MeSH
• neurony metabolismus   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• MeSH
• anatomické modely MeSH
• lidé MeSH
• mozečková jádra MeSH
• nucleus cochlearis MeSH
• počítačové zpracování signálu MeSH
• sluchová percepce MeSH
• sluchové korové centrum fyziologie   MeSH
• teoretické modely MeSH
• Check Tag
• lidé MeSH