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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Vladimir Khorev Semen Kurkin Rositsa Paunova Drozdstoy Stoyanov Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Baltic Center for Artificial Intelligence and Neurotechnology Immanuel Kant Baltic Federal University Kaliningrad Russia Neurosciences Research Institute Samara State Medical University Samara Russia Plovdiv Medical University Plovdiv Bulgaria

In this work, we conducted the analysis of the functional magnetic resonance imaging data in healthy subjects and patients with major depressive disorder that were obtained during experimental research when subjects were resting. The signals that have undergone preprocessing and filtration were used to find the functional connectivity between the brain regions. Results demonstrate significant changes between functional connections based on the mutual information measure. We obtained five significantly changed connections, four of which increased more in cotrol group over patients.

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Chimera-like behavior in a heterogeneous Kuramoto model: The interplay between the attractive and repulsive coupling

arXiv

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arXiv 2020年

作者： Frolov, Nikita Maksimenko, Vladimir Majhi, Soumen Rakshit, Sarbendu Ghosh, Dibakar Hramov, Alexander Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis The Republic of Tatarstan420500 Russia Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India

Interaction within an ensemble of coupled nonlinear oscillators induces a variety of collective behaviors. One of the most fascinating is a chimera state which manifests the coexistence of spatially distinct populations of coherent and incoherent elements. Understanding of the emergent chimera behavior in controlled experiments or real systems requires a focus on the consideration of heterogeneous network models. In this study, we explore the transitions in a heterogeneous Kuramoto model under the monotonical increase of the coupling strength and specifically find that this system exhibits a frequency-modulated chimera-like pattern during the explosive transition to synchronization. We demonstrate that this specific dynamical regime originates from the interplay between (the evolved) attractively and repulsively coupled sub-populations. We also show that the above mentioned chimera-like state is induced under weakly non-local, small-world and sparse scale-free coupling and suppressed in globally coupled, strongly rewired and dense scale-free networks due to the emergence of the large-scale connections. Copyright © 2020, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Portable tracker for neurophysiological research of sport shooting

Portable tracker for neurophysiological research of sport sh...

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Saratov Fall Meeting 2021: Computational Biophysics and Nanobiophotonics

作者： Antipov, V.M. Badarin, A.A. Grubov, V.V. Kazantsev, V.B. Hramov, A.E. Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis 420500 Russia Laboratory of Advanced Methods for High-Dimensional Data Analysis Lobachevsky State University of Nizhni Novgorod 23 Gagarin ave. Nizhny Novgorod603950 Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University A. Nevskogo ul. 14 Kaliningrad236016 Russia Neurotechnology Deparment Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod603022 Russia

ISBN: (数字)9781510653702

ISBN: (纸本)9781510653696

In this work we present the development process of a wireless portable module. It is developed to record various characteristics during sport shooting, such as automatic detection of the moment of shot and barrel movement during aiming, taking into account the peculiarities of its use in neurophysiological research. We propose an approach allowing to synchronize devices in a wireless local network with high accuracy (synchronization accuracy was 2 ms), as well as a method of logging the recorded data at a sampling rate of up to 2 kHz on an onboard flash drive. © 2022 SPIE.

关键词： Neurophysiology

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Chaos and hyperchaos in the chain of quantum coherent elements

Chaos and hyperchaos in the chain of quantum coherent elemen...

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School on Dynamics of Complex Networks and their Application in Intellectual robotics (DCNAIR)

作者： Alexander Balanov Andrey Andreev Mark Fromhold Mark Greenaway Alexander Hramov Weibin Li Vladimir Makarov Alexandre Zagoskin Department of Physics Loughborough University Loughborough United Kingdom Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia School of Physics and Astronomy University of Nottingham Nottingham United Kingdom Department of Theoretical Physics and Quantum Technologies National University of Science and Technology MISIS Moscow Russia

ISBN: (数字)9781728172866

ISBN: (纸本)9781728172873

We study the chain of coupled quantum coherent systems with energy pumping and dissipation. Examples of such systems include the coupled Rydberg atoms with laser driving and spontaneous emission or electromagnetically driven artificial qubits with decoherence and dissipation. We found out that such system is able to demonstrate spontaneous onset of chaotic and even hyperchaotic oscillations, which are characterized by one or several positive Lyapunov exponents. Remarkably, the number of the positive Lyapunov exponents grows with the number of the chain elements. Hence a large chain is able to demonstrate highly irregular behavior. We investigate transition from regular to chaotic behavior and reveal the related instabilities.

关键词： Quantum mechanics Neuroscience Robots mechatronics Chaotic communication Control systems

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Detecting epileptic seizures using machine learning and interpretable features of human EEG

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The European Physical Journal Special Topics 2022年第5期232卷 673-682页

作者： Karpov, Oleg E. Afinogenov, Sergey Grubov, Vadim V. Maksimenko, Vladimir Korchagin, Sergey Utyashev, Nikita Hramov, Alexander E. National Medical and Surgical Center named after N. I. Pirogov Ministry of Healthcare of the Russian Federation Moscow Russia Faculty of Information Technology and Big Data Analysis Financial University under the Government of the Russian Federation Moscow Russia Baltic Center for Artificial Intelligence and Neurotechnology Immanuel Kant Baltic Federal University Kaliningrad Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

Epilepsy is a neurological disorder distinguished by sudden and unexpected seizures. To diagnose epilepsy, clinicians register the signals of brain electric activity (electroencephalograms, EEG) and extract segments with seizures. It enables characterizing their type and finding an onset zone, a brain area where they originate. This procedure requires manual EEG deciphering, which is slow and necessitates the assistance of machine learning (ML) algorithms. Traditionally, ML handles this issue in a supervised fashion, i.e., after the training on the representative data, it constructs a boundary in the feature space that separates classes. As the number of features grows, this boundary becomes complex and less generalized. The feature space of brain data is high dimensional. The standard recording includes 30 signals and 50 frequencies resulting in 1500 features. Using additional time-domain features may further enlarge the feature space. Thus, selecting appropriate features is a big part of the successful classification. The selection procedure relies on either a data-based mathematical approach (e.g., principal components, PCs) or the expert domain knowledge of data (explainable features, EFs). Here, we demonstrate the benefits of using EFs. For the EEG data of 30 epileptic patients, we trained a RandomForest algorithm using PCs and EFs. The feature importance analysis revealed that explainable features outperform principal components.

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Formation of working memory in a spiking neuron network accompanied by astrocytes

arXiv

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arXiv 2020年

作者： Gordleeva, Susanna Yu Tsybina, Yulia A. Krivonosov, Mikhail I. Ivanchenko, Mikhail V. Zaikin, Alexey A. Kazantsev, Victor B. Gorban, Alexander N. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Moscow Russia Institute for Women’s Health Department of Mathematics University College London London United Kingdom University of Leicester United Kingdom

We propose a biologically plausible computational model of working memory (WM) implemented by the spiking neuron network (SNN) interacting with a network of astrocytes. SNN is modelled by the synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons by chemicals diffused in the extracellular space. Calcium elevations occur in response to the increase of concentration of a neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strengths of synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organised by the application of a cue pattern representing the one from the memory set distorted by noise. We found that successful retrieval with level of the correlation between recalled pattern and ideal pattern more than 90% is possible for multi-item WM task. Having analysed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in SNN leading to the successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models. © 2020, CC-BY.

关键词： Neural networks

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Sensorless Control of Permanent Magnet Synchronous Motors based on Finite-Time Robust Flux Observer ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9270-9275页

作者： Anton Pyrkin Alexey Bobtsov Romeo Ortega Alexey Vedyakov Dmitry Cherginets Dmitry Bazylev Petranevsky Igor ITMO University Faculty of Control Systems and Robotics49 Kronverksky Pr. St. Petersburg 197101 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Laboratory “Control of Complex Systems” Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg 199178 Russia Laboratoire des Signaux et Systèmes CNRS-SUPELEC Plateau du Moulon 91192 Gif-sur-Yvette France

A sensorless control algorithm is developed based on novel finite-time robust flux observer for the non-salient permanent magnet synchronous motor (PMSM). Total flux equality and motor model are used to find the linear regression-like model with respect to the flux. Applying dynamic regressor extension and mixing method, we obtain two independent scalar equations and construct a finite-time flux observer. The flux estimate is used to reconstruct the rotor position and velocity with well-known trigonometric relation and phase-locked loop observer, respectively. To complete a sensorless control design, we pass these estimates to standard field orient control. The efficiency and robustness of the proposed approach are demonstrated through the set of numerical simulations.

关键词： Sensorless nonlinear observer flux observer nonlinear control systems synchronous motor

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Assymetry of coupling between the P3 and P4 electroencephalographic leads during the motions 3

Assymetry of coupling between the P3 and P4 electroencephalo...

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3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Khorev, Vladimir Zhuravlev, Maksim Borovkova, Ekaterina Ishbulatov, Yuri Gridnev, Vladimir Karavaev, Anatoly Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia Department of Open Systems Physics Saratov State University Russia Department of Dynamic Modeling and Biomedical Engineering Saratov State University Saratov Russia Saratov State University Saratov Branch Institute of RadioEngineering and Electronics Russian Academy of Sciences Saratov State Medical University Named after V. I. Razumovsky Saratov Russia Department of Innovative Cardiological Information Technology Institute of Cardiological Research Saratov State Medical University Saratov Russia Saratov Branch Institute of RadioEngineering and Elecrronics Russian Academy of Sciences Russia

Based on the coupling analysis we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during real and imaginary movements, and show a difference between the related EEG signals and the record... 详细信息

ISBN: (纸本)9781728124704

关键词： Electroencephalography

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A flux and speed observer for induction motors with unknown rotor resistance and load torque and no persistent excitation requirement

arXiv

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arXiv 2020年

作者： Pyrkin, Anton Bobtsov, Alexey Vedyakov, Alexey Ortega, Romeo Vediakova, Anastasiia Sinetova, Madina Faculty of Control Systems and Robotics ITMO University Kronverksky av. 49 Saint Petersburg197101 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Departamento Académico de Sistemas Digitales ITAM Ciudad de México Mexico Department of Computer Applications and Systems St.Petersburg State University 7/9 Universitetskaya nab. St. Petersburg199034 Russia

In this paper we address the problems of flux and speed observer design for voltage-fed induction motors with unknown rotor resistance and load torque. The only measured signals are stator current and control voltage. Invoking the recently reported Dynamic Regressor Extension and Mixing-Based Adaptive Observer (DREMBAO) we provide the first global solution to this problem. The proposed DREMBAO achieves asymptotic convergence under an excitation condition that is strictly weaker than persistent excitation. If the latter condition is assumed the convergence is exponential. Copyright © 2020, The Authors. All rights reserved.

关键词： Induction motors

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Bursting activity interplay in modular neural networks in vitro 3

Bursting activity interplay in modular neural networks in vi...

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3rd International Conference "Neurotechnologies and Neurointerfaces", CNN 2021

作者： Gladkov, Arseniy Pigareva, Yana Kolpakov, Vladimir Mukhina, Irina Bukatin, Anton Kazantsev, Victor Pimashkin, Alexey Cell Technology Department Central Research Laboratory Privolzhsky Research Medical University Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Renewable Energy Sources Alferov Saint-Petersburg National Research Academic University of the Russian Academy of Sciences Saint-Petersburg Russia Laboratory of Bio and Chemosensor Microsystems Institute for Analytical Instrumentation of the RAS Saint-Petersburg Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia NeurotechnologyDepartment Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia

ISBN: (纸本)9781665424745

Neural networks modularity is a major challenge to study spiking pattern propagation and information processing in the brain. Sparse and heterogeneous connectivity between network modules defines spiking activity generation and interplay. Detailed study of modular network complex connectivity and dynamics is complicated by inducing retrograde or closed-loop activity. In this study, we analyzed the spiking activity of spontaneous and stimulus-induced interplay between two cultured neuronal networks (source-target) coupled with unidirectional synaptic connections, which were formed by microfluidic microchannels. Inhibition of excitatory synaptic activity in a source module abolished retrograde and closed-loop activity in the interplay. We found characteristics of electrical stimulation applied to the intermodular connections, which evoked a network-wide response in a target module. The developed approach can be applied to study synaptic plasticity in modular networks in vitro and improvement of new methods of neurointerface design. © 2021 IEEE.

关键词： Microfluidic chips

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