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I A Rybak

Publications and source records attributed to I A Rybak.

11 recordsLinked to original sources
PubMed2000

Computational modeling of the baroreflex arc: nucleus tractus solitarius.

In this study, we examine the utility of computational modeling in understanding nervous system function. We start by examining the reasons for, and major approaches to, computational modeling. We then chose a modeling approach and applied different variations to understanding nucleus tractus solitarius (NTS) neuronal responses to various baroreceptive stimuli. We examine the results in light of our objectives and with regard to the known parameters of the system under investigation. Our results demonstrate that modeling can be a useful tool in analysis of (and examination of underlying mechanisms for) NTS behavior on many levels.

Animals↗
PubMed1998

A model of attention-guided visual perception and recognition.

A model of visual perception and recognition is described. The model contains: (i) a low-level subsystem which performs both a fovea-like transformation and detection of primary features (edges), and (ii) a high-level subsystem which includes separated 'what' (sensory memory) and 'where' (motor memory) structures. Image recognition occurs during the execution of a 'behavioral recognition program' formed during the primary viewing of the image. The recognition program contains both programmed attention window movements (stored in the motor memory) and predicted image fragments (stored in the sensory memory) for each consecutive fixation. The model shows the ability to recognize complex images (e.g. faces) invariantly with respect to shift, rotation and scale.

Attention↗
PubMed1997

A neuro-mimetic dynamic scheduling algorithm for control: analysis and applications.

A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanisms by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.

Algorithms↗
PubMed1997

Modeling neural mechanisms for genesis of respiratory rhythm and pattern. I. Models of respiratory neurons.

The general objectives of our research, presented in this series of papers, were to develop a computational model of the brain stem respiratory neural network and to explore possible neural mechanisms that provide the genesis of respiratory oscillations and the specific firing patterns of respiratory neurons. The present paper describes models of single respiratory neurons that have been used as the elements in our network models of the central respiratory pattern generator presented in subsequent papers. The models of respiratory neurons were developed in the Hodgkin-Huxley style employing both physiological and biophysical data obtained from brain stem neurons in mammals. Two single respiratory neuron models were developed to match the two distinct firing behaviors of respiratory neurons described in vivo: neuron type I shows an adapting firing pattern in response to synaptic excitation, and neuron type II shows a ramp firing pattern during membrane depolarization after a period of synaptic inhibition. We found that a frequency ramp firing pattern can result from intrinsic membrane properties, specifically from the combined influence of calcium-dependent K(AHP)(Ca), low-threshold Ca(T) and K(A) channels. The neuron models with these ionic channels (type II) demonstrated ramp firing patterns similar to those recorded from respiratory neurons in vivo. Our simulations show that K(AHP)(Ca) channels in combination with high-threshold Ca(L) channels produce spike frequency adaptation during synaptic excitation. However, in combination with low-threshold Ca(T) channels, they cause a frequency ramp firing response after release from inhibition. This promotes a testable hypothesis that the main difference between the respiratory neurons that adapt (for example, early inspiratory, postinspiratory, and decrementing expiratory) and those that show ramp firing patterns (for example, ramp inspiratory and augmenting expiratory) consists of a ratio between the two types of calcium channels: Ca(L) channels predominate in the former and Ca(T) channels in the latter respiratory neuron types. We have analyzed the dependence of adapting and ramp firing patterns on maximal conductances of different ionic channels and values of synaptic drive. The effect of adjusting specific membrane conductances and synaptic interactions revealed plausible neuronal mechanisms that may underlie modulatory effects on respiratory neuron firing patterns and network performances. The results of computer simulation provide useful insight into functional significance of specific intrinsic membrane properties and their interactions with phasic synaptic inputs for a better understanding of respiratory neuron firing behavior.

Adaptation, Physiological↗
PubMed1997

Modeling neural mechanisms for genesis of respiratory rhythm and pattern. II. Network models of the central respiratory pattern generator.

The present paper describes several models of the central respiratory pattern generator (CRPG) developed employing experimental data and current hypotheses for respiratory rhythmogenesis. Each CRPG model includes a network of respiratory neuron types (e.g., early inspiratory; ramp inspiratory; late inspiratory; decrementing expiratory; postinspiratory; stage II expiratory; stage II constant firing expiratory; preinspiratory) and simplified models of lung and pulmonary stretch receptors (PSR), which provide feedback to the respiratory network. The used models of single respiratory neurons were developed in the Hodgkin-Huxley style as described in the previous paper. The mechanism for termination of inspiration (the inspiratory off-switch) in all models operates via late-I neuron, which is considered to be the inspiratory off-switching neuron. Several two- and three-phase CRPG models have been developed using different accepted hypotheses of the mechanism for termination of expiration. The key elements in the two-phase models are the early-I and dec-E neurons. The expiratory off-switch mechanism in these models is based on the mutual inhibitory connections between early-I and dec-E and adaptive properties of the dec-E neuron. The difference between the two-phase models concerns the mechanism for ramp firing patterns of E2 neurons resulting either from the intrinsic neuronal properties of the E2 neuron or from disinhibition from the adapting dec-E neuron. The key element of the three-phase models is the pre-I neuron, which acts as the expiratory off-switching neuron. The three-phase models differ by the mechanisms used for termination of expiration and for the ramp firing patterns of E2 neurons. Additional CRPG models were developed employing a dual switching neuron that generates two bursts per respiratory cycle to terminate both inspiration and expiration. Although distinctly different each model generates a stable respiratory rhythm and shows physiologically plausible firing patterns of respiratory neurons with and without PSR feedback. Using our models, we analyze the roles of different respiratory neuron types and their interconnections for the respiratory rhythm and pattern generation. We also investigate the possible roles of intrinsic biophysical properties of different respiratory neurons in controlling the duration of respiratory phases and timing of switching between them. We show that intrinsic membrane properties of respiratory neurons are integrated with network properties of the CRPG at three hierarchical levels: at the cellular level to provide the specific firing patterns of respiratory neurons (e.g., ramp firing patterns); at the network level to provide switching between the respiratory phases; and at the systems level to control the duration of inspiration and expiration under different conditions (e.g., lack of PSR feedback).

Adaptation, Physiological↗
PubMed1997

Modeling neural mechanisms for genesis of respiratory rhythm and pattern. III. Comparison of model performances during afferent nerve stimulation.

The goal of the present study was to evaluate the relative plausibility of the models of the central respiratory pattern generator (CRPG) proposed in our previous paper. To test the models, we compared changes in generated patterns with the experimentally observed alterations of the respiratory pattern induced by various stimuli applied to superior laryngeal (SLN), vagus and carotid sinus (CS) nerves. In all models, short-duration SLN simulation caused phase-resetting behavior consistent with experimental data. Relatively weak sustained SLN stimulation elicited a two-phase rhythm comprising inspiration and postinspiration whereas a stronger stimulation stopped oscillations in the postinspiratory phase ("postinspiratory apnea"). In all models, sustained vagus nerve stimulation produced postinspiratory apnea. A short vagal stimulus delivered during inspiration terminated this phase. The threshold for inspiratory termination decreased during the course of the inspiratory phase. The effects of short-duration vagal stimulation applied during expiration were different in different models. In model 1, stimuli delivered in the postinspiratory phase prolonged expiration whereas the late expiratory phase was insensitive to vagal stimulation. No insensitive period was found in model 2 because vagal stimuli delivered at any time during expiration prolonged this phase. Model 3 demonstrated a short period insensitive to vagal stimulation at the very end of expiration. When phasic CS nerve stimulation was applied during inspiration or the first half of expiration, the performances of all models were similar and consistent with experimental data: stimuli delivered at the beginning inspiration shortened this phase whereas stimuli applied in the middle or at the end of inspiration prolonged it and stimuli delivered in the first half of expiration prolonged the expiratory interval. Behavior of the models were different when CS stimuli were delivered during the late expiratory phase. In model 1, these stimuli were ineffective or shortened expiration initiating the next inspiration. Alternatively, in models 2 and 3, they caused a prolongation of expiration. Although all CRPG models demonstrated a number of plausible alterations in the respiratory pattern elicited by afferent nerve stimulation, the behavior of model 1 was most consistent with experimental data. Taking into account differences in the model architectures and employed neural mechanisms, we suggest that the concept of respiratory rhythmogenesis based on the essential role of postinspiratory neurons is more plausible than the concept employing specific functional properties of decrementing expiratory (dec-E) neurons and that the ramp firing pattern of the late expiratory neuron is more likely to reflect intrinsic properties than disinhibition from the dec-E neurons.

Afferent Pathways↗
PubMedDate not supplied

[reflection of stimulus orientation in reactions of visual cortex neurons].

A mathematical model of neuronal target structure with spatial anisotropy of lateral inhibition is discussed. The positions of the neuronal target to oriented sensory stimuli are investigated by computer simulation. It is suggested that visual stimuli orientation is coded in the late phase of dynamic responses of cortical neurons. This idea is in agreement with the data obtained in experiments on guinea pig visual cortex neurons.

Animals↗