ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

Nonlinear Dynamics and Neuroscience

Classification of brain activity using synolitic networks

Because the brain is an extremely complex hypernet of interacting macroscopic subnetworks, full-scale analysis of brain activity is a daunting task. Nevertheless, this task can be greatly simplified by analysing the correspondence between various patterns of macroscopic brain activity, for example, through functional magnetic resonance imaging (fMRI) scans, and the performance of particular cognitive tasks or pathological states.

Mathematical model for epileptic seizures detection on an EEG recording

Purpose of this study — analysis of the possibility of using convolutional neural networks as a model for detecting epileptic seizures on real EEG data.

Oscillatory characteristics in the brain activity of the newborns and their correlation with different gestational ages

The purpose of this study is to detect the characteristic features of the oscillatory electrical activity of the brain in early postnatal development, depending on the gestational age of newborns.

Marking stages of REM and non-REM sleep using recurrent analysis

The purpose of this study — to develop a simple technique for labeling sleep stages according to EEG data obtained from half-somnography recordings. To test the work of the method, it will be applied to three groups of subjects: conditionally healthy, patients with Parkinson’s disease, patients with sleep apnea.

Noise influence on recurrent neural network with nonlinear neurons

The purpose of this study is to establish the features of noise propagation and accumulation in a recurrent neural network using a simplified echo network as an example. In this work, we studied the influence of activation function of artificial neurons and the connection matrices between them.

Methods. We have considered white Gaussian noise sources. We used additive, multiplicative and mixed noise depending on how the noise is introduced into artificial neurons. The noise impact was estimated using the dispersion (variance) of the output signal.

Calculation of the cyclic characteristics of the electroencephalogram for investigation of the electrical activity of the brain

The purpose of the study is experimental verification of the proposed EEG analysis method based on the construction of a connectivity graph of the analyzed signal, in which the amplitudes are displayed by vertices, and their relative position relative to each other by arcs. The display of the EEG signal in the graph structure causes the appearance of cyclic structures with the possibility of calculating their numerical characteristics.

Ambient light at night causes desynchronization of rhythms in the sleep–wake switching model

The purpose of this study is to analyze the influence of the shape of the daily illumination profile on the synchronization of rhythms in the sleep–wake state switching model. Normally, the alternation of sleep and wakefulness of a person is synchronized with his circadian rhythm and with the 24-hour rhythm of illumination.

Methodology of the neurophysiological experiments with visual stimuli to assess foreign language proficiency

Aim of this study is to compare different experimental paradigms and to determine parameters suitable for conducting a neurophysiological experiment with visual stimuli to assess foreign language proficiency and providing further time series analysis of electrical brain activity to reveal specific biomarkers.

Methods. This paper explores the possibilities and limitations of various experimental studies using the metaanalysis paradigm. Statistical approaches are used to determine significance of the results.

Integrated information and its application for analysis of brain neuron activity

Purpose of this review is to consider the possibility to apply the integrated information theory to investigate the brain neural activity. Earlier was shown that the integrated information amount Ф quantifies a degree of a dynamic complexity of a system and able to predict a level of its success defined by classic observable benchmarks. For this reason, a question arises about the application of the integrated information theory to analyse changes in brain spiking activity due the acquisition of new experience.

Working memory capacity: the role of parameters of spiking neural network model

Purpose of this work is to study a computational model of working memory formation based on spiking neural network with plastic connections and to study the capacity of working memory depending on the time scales of synaptic facilitation and depression and the background excitation of the network.