Petr Lansky

Competitive and Noncompetitive Odorant Interactions in the Early Neural Coding of Odorant Mixtures

Most olfactory receptor neurons (ORNs) express a single type of olfactory receptor that is differentially sensitive to a wide variety of odorant molecules. The diversity of possible odorant-receptor interactions raises challenging problems for the coding of complex mixtures of many odorants, which make up the vast majority of real world odors. Pure competition, the simplest kind of interaction, arises when two or more agonists can bind to the main receptor site, which triggers receptor activation, although only one can be bound at a time. Noncompetitive effects may result from various mechanisms, including agonist binding to another site, which modifies the receptor properties at the main binding site. Here, we investigated the electrophysiological responses of rat ORNs in vivo to odorant agonists and their binary mixtures and interpreted them in the framework of a quantitative model of competitive interaction between odorants. We found that this model accounts for all concentration-response curves obtained with single odorants and for about half of those obtained with binary mixtures. In the other half, the shifts of curves along the concentration axis and the changes of maximal responses with respect to model predictions, indicate that noncompetitive interactions occur and can modulate olfactory receptors. We conclude that, because of their high frequency, the noncompetitive interactions play a major role in the neural coding of natural odorant mixtures. This finding implies that the CNS activity caused by mixtures will not be easily analyzed into components, and that mixture responses will be difficult to generalize across concentration.

A review of the methods for signal estimation in stochastic diffusion leaky integrate-and-fire neuronal models

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.

The parameters of the stochastic leaky integrate-and-fire neuronal model

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.

Neuronal coding and spiking randomness.

Fast information transfer in neuronal systems rests on series of action potentials, the spike trains, conducted along axons. Methods that compare spike trains are crucial for characterizing different neuronal coding schemes. In this paper we review recent results on the notion of spiking randomness, and discuss its properties with respect to the rate and temporal coding schemes. This method is compared with other widely used characteristics of spiking activity, namely the variability of interspike intervals, and it is shown that randomness and variability provide two distinct views. We demonstrate that estimation of spiking randomness from simulated and experimental data is capable of capturing characteristics that would otherwise be difficult to obtain with conventional methods.

Single-unit analysis of the spinal dorsal horn in patients with neuropathic pain.

Summary Despite the key role played by the dorsal horn of the spinal cord in pain modulation, single-unit recordings have only been performed very rarely in this structure in humans. The authors report the results of a statistical analysis of 64 unit recordings from the human dorsal horn. The recordings were done in three groups of patients: patients with deafferentation pain resulting from brachial plexus avulsion, patients with neuropathic pain resulting from peripheral nerve injury, and patients with pain resulting from disabling spasticity. The patterns of neuronal activities were compared among these three groups. Nineteen neurons were recorded in the dorsal horns of five patients undergoing DREZotomy for a persistent pain syndrome resulting from peripheral nerve injury (i.e., nondeafferented dorsal horns), 31 dorsal horn neurons were recorded in nine patients undergoing DREZotomy for a persistent pain syndrome resulting from brachial plexus avulsion (i.e., deafferented dorsal horns), and 14 neurons were recorded in eight patients undergoing DREZotomy for disabling spasticity. These groups were compared in terms of mean frequency, coefficient of variation of the discharge, other properties of the neuronal discharge studied by the nonparametric test of Wald–Wolfowitz, and the possible presence of bursts. The coefficient of variation tended to be higher in the deafferented dorsal horn group than in the other two groups. Two neurons displaying burst activity could be recorded, both of which belonged to the deafferented dorsal horn group. A significant difference was found in term of neuronal behavior between the peripheral nerve trauma group and the other groups: The brachial plexus avulsion and disabling spasticity groups were very similar, including various types of neuronal behavior, whereas the peripheral nerve lesion group included mostly neurons with “nonrandom” patterns of discharge (i.e., with serial dependency of interspike intervals).

Efficient olfactory coding in the pheromone receptor neuron of a moth.

The concept of coding efficiency holds that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical characteristics of their natural stimulus. Encouraged by the successful invocation of this principle to predict how neurons encode natural auditory and visual stimuli, we attempted its application to olfactory neurons. The pheromone receptor neuron of the male moth Antheraea polyphemus, for which quantitative properties of both the natural stimulus and the reception processes are available, was selected. We predicted several characteristics that the pheromone plume should possess under the hypothesis that the receptors perform optimally, i.e., transfer as much information on the stimulus per unit time as possible. Our results demonstrate that the statistical characteristics of the predicted stimulus, e.g., the probability distribution function of the stimulus concentration, the spectral density function of the stimulation course, and the intermittency, are in good agreement with those measured experimentally in the field. These results should stimulate further quantitative studies on the evolutionary adaptation of olfactory nervous systems to odorant plumes and on the plume characteristics that are most informative for the ‘sniffer’. Both aspects are relevant to the design of olfactory sensors for odour-tracking robots.

Does the dose-solubility ratio affect the mean dissolution time of drugs?

AbstractPurpose. To present a new model for describing drug dissolution. On the basis of the new model to characterize the dissolution profile by the distribution function of the random dissolution time of a drug molecule, which generalizes the classical first order model. Methods. Instead of assuming a constant fractional dissolution rate, as in the classical model, it is considered that the fractional dissolution rate is a decreasing function of the dissolved amount controlled by the dose-solubility ratio. The differential equation derived from this assumption is solved and the distribution measures (half-dissolution time, mean dissolution time, relative dispersion of the dissolution time, dissolution time density, and fractional dissolution rate) are calculated. Finally, instead of monotonically decreasing the fractional dissolution rate, a generalization resulting in zero dissolution rate at time origin is introduced. Results. The behavior of the model is divided into two regions defined by q, the ratio of the dose to the solubility level: q < 1 (complete dissolution of the dose, dissolution time) and q > 1 (saturation of the solution, saturation time). The singular case q = 1 is also treated and in this situation the mean as well as the relative dispersion of the dissolution time increase to infinity. The model was successfully fitted to data(l). Conclusions. This empirical model is descriptive without detailed physical reasoning behind its derivation. According to the model, the mean dissolution time is affected by the dose-solubility ratio. Although this prediction appears to be in accordance with preliminary application, further validation based on more suitable experimental data is required.

Estimation of time-dependent input from neuronal membrane potential

The set of firing rates of the presynaptic excitatory and inhibitory neurons constitutes the input signal to the postsynaptic neuron. Estimation of the time-varying input rates from intracellularly recorded membrane potential is investigated here. For that purpose, the membrane potential dynamics must be specified. We consider the Ornstein-Uhlenbeck stochastic process, one of the most common single-neuron models, with time-dependent mean and variance. Assuming the slow variation of these two moments, it is possible to formulate the estimation problem by using a state-space model. We develop an algorithm that estimates the paths of the mean and variance of the input current by using the empirical Bayes approach. Then the input firing rates are directly available from the moments. The proposed method is applied to three simulated data examples: constant signal, sinusoidally modulated signal, and constant signal with a jump. For the constant signal, the estimation performance of the method is comparable to that of the traditionally applied maximum likelihood method. Further, the proposed method accurately estimates both continuous and discontinuous time-variable signals. In the case of the signal with a jump, which does not satisfy the assumption of slow variability, the robustness of the method is verified. It can be concluded that the method provides reliable estimates of the total input firing rates, which are not experimentally measurable.

REVIEW ARTICLE: Neuronal coding and spiking randomness

Fast information transfer in neuronal systems rests on series of action potentials, the spike trains, conducted along axons. Methods that compare spike trains are crucial for characterizing different neuronal coding schemes. In this paper we review recent results on the notion of spiking randomness, and discuss its properties with respect to the rate and temporal coding schemes. This method is compared with other widely used characteristics of spiking activity, namely the variability of interspike intervals, and it is shown that randomness and variability provide two distinct views. We demonstrate that estimation of spiking randomness from simulated and experimental data is capable of capturing characteristics that would otherwise be difficult to obtain with conventional methods.

Similarity of interspike interval distributions and information gain in a stationary neuronal firing

The Kullback–Leibler (KL) information distance is proposed for judging similarity between two different interspike interval (ISI) distributions. The method is applied by a comparison of four common ISI descriptors with an exponential model which is characterized by the highest entropy. Under the condition of equal mean ISI values, the KL distance corresponds to information gain coming from the state described by the exponential distribution to the state described by the chosen ISI model. It has been shown that information can be transmitted changing neither the spike rate nor coefficient of variation (CV). Furthermore the KL distance offer an indication of the exponentiality of the chosen ISI descriptor (or data): the distance is zero if, and only if, the ISIs are distributed exponentially. Finally an application on experimental data coming from the olfactory sensory neurons of rats is shown.