Biomedical subjects

Roman Borisyuk

Publications and source records attributed to Roman Borisyuk.

5 recordsLinked to original sources

PubMed2006

Oscillations and waves in the models of interactive neural populations.

The dynamics of activity in interactive neural populations is simulated by the networks of Wilson-Cowan oscillators. Two extreme cases of connection architectures in the networks are considered: (1) 1D and 2D regular and homogeneous grids with local connections and (2) sparse random coupling. Propagating waves in the network have been found under the stationary external input and the regime of partial synchronization has been obtained for the periodic input. It has been shown that in the case of random coupling about 60% of neural populations demonstrate oscillatory activity and some of these oscillations are synchronous. The role of different types of dynamics in information processing is discussed. In particular, we discuss the regime of partial synchronization in the context of cortical microcircuits.

Cell Communication↗

PubMed2006

An oscillatory neural model of multiple object tracking.

An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.

Animals↗

PubMed2004

A theory of epineuronal memory.

How can a brain maintain stable memories and behaviors when its underlying electrical and chemical structures are constantly changing? We investigate this stability problem thinking that the state variables (e.g. voltages, ionic currents, etc.) are governed by a complex system that itself is closely regulated. Regulation of the network is through its operating environment, which is described by parameters. We study a standard neural network model, but one whose parameters are governed by a mnemonic landscape function. Parameter configurations are attracted to local maxima of this landscape, which represent memorized parameter configurations. The operating environment changes slowly (relative to the state variables) guided by the mnemonic landscape function, and this provides a quasi-static operating environment for the network. We describe shaping the mnemonic landscape, how it acts as a probability density function to guide slow parameter dynamics, and how the parameters shape the network output. Simulations of memory registration, activity patterns, and sequential recall are described.

Brain↗

PubMedDate not supplied

Oscillatory activity in the neural networks of spiking elements.

We study the dynamics of activity in the neural networks of enhanced integrate-and-fire elements (with random noise, refractory periods, signal propagation delay, decay of postsynaptic potential, etc.). We consider the networks composed of two interactive populations of excitatory and inhibitory neurons with all-to-all or random sparse connections. It is shown by computer simulations that the regime of regular oscillations is very stable in a broad range of parameter values. In particular, oscillations are possible even in the case of very sparse and randomly distributed inhibitory connections and high background activity. We describe two scenarios of how oscillations may appear which are similar to Andronov-Hopf and saddle-node-on-limit-cycle bifurcations in dynamical systems. The role of oscillatory dynamics for information encoding and processing is discussed.

Action Potentials↗

PubMedDate not supplied

Object selection by an oscillatory neural network.

We describe a new solution to the problem of consecutive selection of objects in a visual scene by an oscillatory neural network with the global interaction realised through a central executive element (central oscillator). The frequency coding is used to represent greyscale images in the network. The functioning of the network is based on three main principles: (1) the synchronisation of oscillators via phase-locking, (2) adaptation of the natural frequency of the central oscillator, and (3) resonant increase of the amplitudes of the oscillators which work in-phase with the central oscillator. Examples of network simulations are presented to show the reliability of the results of consecutive selection of objects under conditions of constant and varying brightness of the objects.

Biological Clocks↗