Biomedical subjects

G N Borisyuk

Publications and source records attributed to G N Borisyuk.

4 recordsLinked to original sources

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↗

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↗