Biomedical subjects

R M Borisyuk

Publications and source records attributed to R M Borisyuk.

8 recordsLinked to original sources

PubMed2000

A model of theta rhythm production in the septal-hippocampal system and its modulation by ascending brain stem pathways.

Recent experimental observations have disclosed the existence of a septal-hippocampal feedback circuit, composed of medial septum diagonal band of Broca (ms-dbB) GABAergic projections to the inhibitory interneurons of the hippocampus, and hippocampal GABAergic projections to the ms-dbB, the major targets of which are the GABAergic septo-hippocampal projection cells. We propose that this feedback circuit provides the mechanism for the rhythmic suppression of interneuronal activity in the hippocampus, which is observed as low-level GABAergic-mediated theta activity. We also propose that this circuit may be the mechanism by which ascending brain stem pathways to the ms-dbB, in particular from the reticular formation, can influence hippocampal information processing in response to particular behavioral states, by exercising control over the level and frequency of theta activity in the hippocampus. In support of these proposals, we describe a minimal computational model of the feedback circuit which uses a set of four coupled differential equations describing the average dynamic activity of the populations of excitatory and inhibitory cells involved in the circuit. We demonstrate through simulations the inherently robust 4-6-Hz oscillatory dynamics of the circuit, and show that manipulation of internal connection strengths and external modulatory influences on this circuit changes the dynamics in a way which closely mimics corresponding manipulations in recent neurophysiological experiments investigating theta activity.

Animals↗

PubMed1998

The synchronization principle in modelling of binding and attention.

The paper presents some mathematical models in support of the hypothesis that there is a general principle of information processing at the levels of both preattention and attention. It is claimed that at both levels, information processing is based on the coherent (synchronous) activity of neurons, neural populations, and brain structures. The difference between the two levels is presumed to relate to how synchronization is realized. At the level of preattention, synchronization results from the self-organization of the cortical activity, while at the level of attention, synchronous activity is controlled by special structures that act as a central executive. Two types of oscillatory neural networks are developed to model preattention and attention phenomena. In the preattention modelling, we concentrate on the binding problem. To solve this problem, a two-layer network of neural oscillators is developed. The network is able to generate two-frequency envelope oscillations, in which the amplitude of high-frequency oscillations is modulated by a lower frequency. This network synchronizes regions of oscillatory activity at high and low frequencies according to the type of stimulation. Such a synchronization gives feature binding for both simple and complex stimuli. Networks of phase oscillators with a central element are used to described different dynamic behaviour associated with the attention focus formation and switching. For the input to the attention system represented by two stimuli, we give a complete description of conditions when a specific attention focus can be formed. The results are interpreted and discussed in terms of attention modelling. This includes the interpretation of psychological experiments on visual selective attention, the problem of attention focus formation and the possibility of spontaneous attention switching .

Attention↗

PubMed1997

Information coding on the basis of synchronization of neuronal activity.

Three types of oscillatory neural networks are considered and their dynamics are analyzed. The regime of chaotic coherent oscillations was found in a network of excitatory and inhibitory integrate-and-fire neurons with all-to-all connections. The regime of envelope oscillations was found in a chain of neural oscillators with local connections. The regime of partial synchronization of the central element with a group of peripheral oscillators under perturbation of the rest of the peripheral oscillators was found in the oscillatory neural network of phase oscillators. A possible use of the dynamical regimes for the information coding and processing is briefly discussed.

Models, Biological↗

PubMed1995

Dynamics and bifurcations of two coupled neural oscillators with different connection types.

In this paper we present an oscillatory neural network composed of two coupled neural oscillators of the Wilson-Cowan type. Each of the oscillators describes the dynamics of average activities of excitatory and inhibitory populations of neurons. The network serves as a model for several possible network architectures. We study how the type and the strength of the connections between the oscillators affect the dynamics of the neural network. We investigate, separately from each other, four possible connection types (excitatory-->excitatory, excitatory-->inhibitory, inhibitory-->excitatory, and inhibitory-->inhibitory) and compute the corresponding bifurcation diagrams. In case of weak connections (small strength), the connection of populations of different types lead to periodic in-phase oscillations, while the connection of populations of the same type lead to periodic anti-phase oscillations. For intermediate connection strengths, the networks can enter quasiperiodic or chaotic regimes, and can also exhibit multistability. More generally, our analysis highlights the great diversity of the response of neural networks to a change of the connection strength, for different connection architectures. In the discussion, we address in particular the problem of information coding in the brain using quasiperiodic and chaotic oscillations. In modeling low levels of information processing, we propose that feature binding should be sought as a temporally coherent phase-locking of neural activity. This phase-locking is provided by one or more interacting convergent zones and does not require a central ¿top level¿ subcortical circuit (e.g., the septo-hippocampal system). We build a two layer model to show that although the application of a complex stimulus usually leads to different convergent zones with high frequency oscillations, it is nevertheless possible to synchronize these oscillations at a lower frequency level using envelope oscillations. This is interpreted as a feature binding of a complex stimulus.

Animals↗

PubMed1994

In-phase and antiphase self-oscillations in a model of two electrically coupled pacemakers.

The dynamic behavior of a model of two electrically coupled oscillatory neurons was studied while the external polarizing current was varied. It was found that the system with weak coupling can demonstrate one of five stable oscillatory modes: (1) in-phase oscillations with zero phase shift; (2) antiphase oscillations with half-period phase shift; (3) oscillations with any fixed phase shift depending on the value of the external polarizing current; (4) both in-phase and antiphase oscillations for the same current value, where the oscillation type depends on the initial conditions; (5) both in-phase and quasiperiodic oscillations for the same current value. All of these modes were robust, and they persisted despite small variations of the oscillator parameters. We assume that similar regimes, for example antiphase oscillations, can be detected in neurophysiological experiments. Possible applications to central pattern generator models are discussed.

Animals↗

PubMed1992

Bifurcation analysis of a neural network model.

This paper describes the analysis of the well known neural network model by Wilson and Cowan. The neural network is modeled by a system of two ordinary differential equations that describe the evolution of average activities of excitatory and inhibitory populations of neurons. We analyze the dependence of the model's behavior on two parameters. The parameter plane is partitioned into regions of equivalent behavior bounded by bifurcation curves, and the representative phase diagram is constructed for each region. This allows us to describe qualitatively the behavior of the model in each region and to predict changes in the model dynamics as parameters are varied. In particular, we show that for some parameter values the system can exhibit long-period oscillations. A new type of dynamical behavior is also found when the system settles down either to a stationary state or to a limit cycle depending on the initial point.

Biological Clocks↗

PubMed1985

A new statistical method for identifying interconnections between neuronal network elements.

A new method is proposed to analyse dependencies in point processes, which takes into account specific character of neuronal activity. Simulation modelling of neuronal network revealed that the estimated weight of connection depends monotonically on the value of the model synaptic strength. In contrast to the crosscorrelation, the method allows for nonlinear interconnections and does not require point processes to be stationary and samples to be large. Examples are presented of the method's application to neurophysiological data analysis.

Animals↗

PubMedDate not supplied

Memorizing and recalling spatial-temporal patterns in an oscillator model of the hippocampus.

We describe the model of the hippocampus consisting of interactive oscillators with input from the entorhinal cortex (modulating the main information flow by a theta rhythm) and the septum (a theta rhythm generator). When interconnections between oscillators are allowed to strengthen in an adaptive way, the network can be trained using a series of lessons. This results in a connection matrix that memorizes the temporal sequence of inputs. Presenting one of the lessons to the trained network results in reproduction of the remainder of the sequence. In this paper, we create such a connection matrix, derive from it an appropriate Markov chain and simulate the chain to illustrate its dynamics.

Hippocampus↗