We propose to use the chimera-like state for stimulus classification in a spiking neural network of bistable HH neurons. As a stimulus, we use an external pulsed current applied to the network. Additive noise makes the neurons nonidentical so that the external pulse switches only a part of the neurons from the resting to the oscillatory state depending on the pulse amplitude. For classification, we use the neural network and two output neurons. The network is trained on two external pulses with different amplitudes to adjust coupling strengths between neurons in the main network and output neurons. We investigate influence of inhibitory coupling between output neurons on classification of input signal with different amplitudes.
The analysis of neurophysiological mechanisms responsible for motor imagery is essential for the development of brain-computer interfaces. The carried out magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery: kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas. For classification of the brain states associated with motor imagery, we used the hierarchical cluster analysis and a popular type of artificial neural networks called multilayer perceptron. The application of machine learning techniques allows us to classify motor imagery in raising right and left arms with an average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.
In this paper we numerically simulate a two-layer network of coupled Hodgkin-Huxley neurons for modulating a processing visual perception by the human brain. We investigate the influence of the external stimulus amplitude on the dynamics of second layer neurons. We discover coherent resonance phenomenon in the system: there is an area of external stimulus amplitude when both SNR and characteristic correlation time are maximal. We also analyze the influence of internal noise amplitude on the system dynamics.
We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.
We have considered time-frequency and spatio-temporal structure of electrical brain activity, associated with real and imaginary movements based on the multichannel EEG recordings. We have found that along with wellknown effects of event-related desynchronization (ERD) in α/μ – rhythms and β – rhythm, these types of activity are accompanied by the either ERS (for real movement) or ERD (for imaginary movement) in low-frequency δ – band, located mostly in frontal lobe. This may be caused by the associated processes of decision making, which take place when subject is deciding either perform the movement or imagine it. Obtained features have been found in untrained subject which it its turn gives the possibility to use our results in the development of brain-computer interfaces for controlling anthropomorphic robotic arm.
In the paper we propose an approach based on artificial neural networks for recognition of different human brain states associated with distinct visual stimulus. Based on the developed numerical technique and the analysis of obtained experimental multichannel EEG data, we optimize the spatiotemporal representation of multichannel EEG to provide close to 97% accuracy in recognition of the EEG brain states during visual perception. Different interpretations of an ambiguous image produce different oscillatory patterns in the human EEG with similar features for every interpretation. Since these features are inherent to all subjects, a single artificial network can classify with high quality the associated brain states of other subjects.
In this paper we study the spiking behaviour of a neuronal network consisting of Rulkov elements. We find that the regularity of this behaviour maximizes at a certain level of environment noise. This effect referred to as coherence resonance is demonstrated in a random complex network of Rulkov neurons. An external stimulus added to some of neurons excites them, and then activates other neurons in the network. The network coherence is also maximized at the certain stimulus amplitude.
This paper considers the possibility of classification of electroencephalogram (EEG) and electromyogram (EMG) signals corresponding to different phases of sleep and wakefulness of mice by the means of artificial neural networks. A feed-forward artificial neural network based on multilayer perceptron was created and trained on the data of one of the rodents. The trained network was used to read and classify the EEG and EMG data corresponding to different phases of sleep and wakefulness of the same mouse and other mouse. The results show a good recognition quality of all phases for the rodent on which the training was conducted (80–99%) and acceptable recognition quality for the data collected from the same mouse after a stroke.
In this paper we study the spiking behaviour of a neuronal network consisting of 100 Rulkov elements coupled to each other with randomly chosen coupling strength. We find periodical grouping forming in the signal from all neurons in the network. We discovered the phenomenon of coherent resonance when signal-to-noise ration takes the maximum value at certain values of such parameters as number of neurons in the system, number of stimulated neurons, amplitude of external stimulus and amplitude of internal noise.
In the present research we studied the cognitive processes, associated with the perception of ambiguous images using the multichannel MEG recordings. Using the wavelet transformation, we considered the dynamics of the neural network of brain in different frequency bands, including high (up to 100 Hz) frequency gamma-waves. Along with the time-frequency analysis of single MEG traces, the interactions between remote brain regions, associated with the perception, were also taken into consideration. As the result, the new features of bistable visual perception were observed and the effect of image ambiguity was analyzed.
Present paper is devoted to the study of intermittency during the perception of bistable Necker cube image being a good example of an ambiguous object, with simultaneous measurement of EEG. Distributions of time interval lengths corresponding to the left-oriented and right-oriented cube perception have been obtain. EEG data have been analyzed using continuous wavelet transform and it was shown that the destruction of alpha rhythm with accompanying generation of high frequency oscillations can serve as a marker of Necker cube recognition process.
We study the appearance, development and depression of the alpha-rhythm in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. The new method based on continuous wavelet transform allows to estimate the energy contribution of various components, including the alpha rhythm, in the general dynamics of the electrical activity of the projections of various areas of the brain. The decision-making process by observe ambiguous images is characterized by specific oscillatory alfa-rhytm patterns in the multi-channel EEG data. We have shown the repeatability of detected principles of the alpha-rhythm evolution in a data of group of 12 healthy male volunteers.
Characteristics of intermittency during the perception of ambiguous images have been studied in the case the Necker cube image has been used as a bistable object for demonstration in the experiments, with EEG being simultaneously measured. Distributions of time interval lengths corresponding to the left-oriented and right-oriented Necker cube perception have been obtain. EEG data have been analyzed using continuous wavelet transform which was shown that the destruction of alpha rhythm with accompanying generation of high frequency oscillations can serve as a electroencephalographical marker of Necker cube recognition process in human brain.
In the paper we study the appearance of the complex patterns in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. A new method based on the calculation of the maximum energy component for the continuous wavelet transform (skeletons) is proposed. Skeleton analysis allows us to identify specific patterns in the EEG data set, appearing in the perception of ambiguous objects. Thus, it becomes possible to diagnose some cognitive processes associated with the concentration of attention and recognition of complex visual objects. The article presents the processing results of experimental data for 6 male volunteers.
We focus into the development of an efficient method to control multistable systems in the presence of noise.
The method is based on the addition of an external control force in the form of a slow harmonic modulation with
properly chosen frequency and amplitude. Although noise is usually a non desirable condition, it has been recently
demostrated that in coupled systems cooperative effects of the periodic force and noise are produced. This
enhancement phenomena in the response of the deterministic equations is interpreted as stochastic resonance.
Our main purpose is to study deterministic resonances between the different solutions of the multistable system
and the external modulation. Then, all efforts will concentrate in the enhancement of the control method produced by noise.
The possibility of secure communication with chaos is demonstrated experimentally with two simple unidirectionally coupled electronic circuits. A traditional approach has been used to synchronize the two chaotic systems. We also study, both numerically and experimentally, the dynamic of the systems in a wide range of the control parameter. The bifurcation diagrams represent a complex behaviour whish varied from periodic orbits to chaos of the Rossler and Shilnikov types. The results of numerical simulations are in a good agreement with experiments.
KEYWORDS: Control systems, Modulation, Beam controllers, Chaos, Gas lasers, Semiconductor lasers, Laser systems engineering, Crystals, Feedback control, Complex systems
The existing methods for controlling laser dynamics are considered and classified depending on a type of the control and on a control goal. I describe the most important, in my opinion, theoretical and experimental results which can help in solving some real fundamental and technological problems.
An erbium-doped fiber laser is shown to operate as a bistable or multistable nonlinear system under harmonic modulation of the diode pump laser. Phase- and frequency-dependent states are demonstrated both experimentally and in numerical simulations through codimensional-one and codimensional-two bifurcation diagrams in the parameter space of the modulation frequency and amplitude. In particular, generalized bistability results in doubling of saddle-node bifurcation lines where different coexisting attractors born. The laser model describes well all experimental features.
The control implies a slow small harmonic modulation with properly chosen frequency and amplitude to the available laser parameter, for example, to the loss, pumping, or cavity detuning. This type of the control results in exiting phenomena such as nonlinear parametric resonances, a shift of bifurcation points (period-doubling and saddle-node), deformation of boundaries of attractors and even their destruction.
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