Browsing by Author "Borisenok, Sergey"

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Control of collective bursting in small Hodgkin-Haxley neuron clusters
(Communications Faculty of Sciences University of Ankara, 2018) Borisenok, Sergey; Ünal, Zeynep; Çatmabacak, Önder; AGÜ, Mühendislik Fakültesi, Elektrik & Elektronik Mühendisliği Bölümü;
The speed gradient-based control algorithm for tracking the membrane potential of Hodgkin-Huxley neurons is applied to their small clusters modeling the basic features of an epileptiform dynamics. One of the neurons plays a role of control element detecting the temporal hyper-synchronization among its network companions and switching their bursting behavior to resting. The ‘toy’ model proposed in the paper can serve as an algorithmic basement for developing special control elements at the scale of one or few cells that may work autonomously and are able to detect and suppress epileptic behavior in the networks of real biological neurons.
Control over performance of qubit-based sensors
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2018) Borisenok, Sergey; 0000-0002-1992-628X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
The extreme sensitivity of quantum systems towards the external perturbations and in the same time their ability to be strongly coupled to the measured target field makes them to be stable under the environmental noise. A high quality quantum sensor can be engineered even on the platform of a single trapped qubit. There is a variety of optimal and sub-optimal algorithms for effective control over the quantum system states. Here we discuss the opportunity to improve the efficiency of the external field quantum sensor based on a single qubit via its feedback tracking.
DETECTION AND CONTROL OF EPILEPTIFORM REGIME IN THE HODGKIN–HUXLEY ARTIFICIAL NEURAL NETWORKS VIA QUANTUM ALGORITHMS
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2022) Borisenok, Sergey; 0000-0002-1992-628X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
The problem of detection and the following suppression of epileptiform dynamics in artificial neural networks (ANN) still is a hot topic in modern theoretical and applied neuroscience. For the purpose of such modeling, the Hodgkin–Huxley (HH) elements are important due to the variety of their behavior such as resting, singular spikes, and spike trains and bursts. This dynamical spectrum of individual HH neurons can cause an epileptiform regime originated in the hyper-synchronization of the cell outcomes. Our model covers the detection and suppression of ictal behavior in a small ANN consisting of HH cells. The model follows our approach [Borisenok et al., 2018] for the HH neurons as a classical dynamical system driving the collective neural bursting, but here we use a quantum paradigm-based algorithm emulated with the pair of HH neurons. Such emulation becomes possible due to the complexity of the individual 4d HH dynamics. The linear chain of two HH neurons is connected to the rest of ANN and works autonomously. The first neuron plays a role of the detecting element for the hyper-synchronization in the ANN and the quantum algorithm emulator; while the second one works as a measuring element (emulation of the quantum measurement converting the signals into the classical domain) and the trigger for the feedback suppressing the epileptiform regime. We use here the speed gradient algorithm for controling the emulating neuron and discuss its pros and cons to compare with our classical model of epileptiform suppression.
Energy control in a quantum oscillator using coherent control and engineered environment
(PERGAMON-ELSEVIER SCIENCE, 2022) Pechen, Alexander N; Borisenok, Sergey; Fradkov, Alexander L.; 0000-0002-1992-628X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
We develop and analyze a new method for manipulation of energy in a quantum harmonic oscillator using coherent, e.g., electromagnetic, field and incoherent control. Coherent control is typically implemented by shaped laser pulse or tailored electromagnetic field. Incoherent control is implemented by engineered environment, whose mean number of excitations at the frequency of the oscillator is used as a control variable. An approach to coherent and incoherent controls design based on the speed gradient algorithms in general, finite and differential forms is proposed. It is proved that the differential form is able to completely manipulate the energy of the oscillator: an arbitrary energy can be achieved starting from any initial state of the oscillator. The key instrument which allows for complete energy manipulation in this case is the use of the engineered environment. A robustified speed-gradient control algorithm in differential form is also proposed. It is shown that the proposed robustified control algorithm ensures exponential stability of the closed loop system which is preserved for sampled-data control.
Ergotropy of bosonic quantum battery driven via repelling feedback algorithms
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2021) Borisenok, Sergey; Gürsey, Feza; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
Feedback algorithms can be efficiently applied to control the basic characteristics of quantum batteries (QBs): the ergotropy, the charging power, the storage capacity and others. We invent here two alternative approaches, target repeller and speed gradient feedback, to maximize the ergotropy for bosonic types of single-qubit based quantum batteries. We demonstrate the achievability of the control goal and discuss some pros and cons of both proposed algorithms. © 2021, Institute for Problems in Mechanical Engineering, Russian Academy of Sciences. All rights reserved.
Hodgkin-Huxley Nöronlarında Ani Yükseliş ve Fırlama Dinamiklerinin Kontrolü
(TUBİTAK, 2018) Borisenok, Sergey; 0000-0002-1992-628X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
Ani yükselen nöronları içeren ağlar, pek çok örüntü tanıma ve hesaplamalı nörobilim uygulamalarında önemli bir rol oynamaktadır. Modern deneysel bilim, biyolojik nöronların dinamiklerinin manipülasyonunda büyük bir ilerleme göstermektir. Fakat tek hücrenin ve kollektif ani yükseliş ve fırlama ile ilgili doğrusal olmayan davranışlarının kontrolünün matematiksel modellemesindeki teoretik algoritmaların geliştirilmesine ihtiyaç duymaktadır. Projenin amacı, biyolojik nöronları modelleyen dört boyutlu dinamik sistemlerin ani yükseliş ve fırlama dinamiklerini dizayn etmek için etkili matematiksel kontrol algoritmaları geliştirmektir. Bu amaç için, deneysel olarak en çok kabul edilen ve nöronların matematiksel modellemesi için gerçekçi olan dört boyutlu Hodgkin-Huxley (HH) doğrusal olmayan dinamik sistemi seçilmiştir. Membran aksiyon potansiyelleri sistem çıkışı olması rağmen, nöronal kümelerde dolaşan elektrik akımları kontrol sinyali olarak hizmet etmektir. HH modelindeki ani yükseliş rejimlerini tasarlamak ve sistemin dinamik davranışını üzerine yüklemek için, iki alternatif kontrol metodu kullanılır: hız gradyanı (HG) ve hedef çekicisi (HÇ) geribeslemeli kontrol. Son zamanlarda ispat ettiğimiz gibi, her iki metot dayankı-ve-yangın nöronların basitleştirilmiş iki boyutlu modellerinde dinamik davranışlarını kontrol etmek için yüksek verimlilik ve dayanıklılık göstermektedir. Bu projede teorik kontrol algoritmasının HG ve HÇ iki farklı formu, Hodgkin-Huxley nöron ağının aksiyon potansiyelini izlemek için tasarlanmıştır. Metot, tek nöron üzerinde aktif kontrol uygulayarak, seçilmiş nöron kümesi düzeni (doğrusal ve halka şeklinde nöron zinciri) için isteğe bağlı aniyükseliş (spike), ani yükseliş dizisi (spike train) ve fırlama (burst) şekillerinin üretilmesine izin verir. Projede geliştirilen algoritma küçük bir Hodgkin-Huxley nöron kümesi için epileptik yapıdaki toplu fırlamaları baskılamak için kullanılmaktadır. Böylece, proje biyolojik nöronların matematiksel modelleri için uygulanan kontrol teorisinde uygun bir yer edinebilir ve Hodgkin - Huxley nöronal ağlarının temel küme yapılarındaki isteğe bağlı ani yükseliş veya fırlama rejiminin etkin nesili için özgün bir algoritma geliştirebilir.
Parameter investigation of topological data analysis for EEG signals
(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 01.01.2021) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, Kutay; 0000-0002-0947-6166; 0000-0002-3891-935X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Topological data analysis (TDA) methods have become appealing in EEG signal processing, because they may help the scientists explore new features of complex and large amount of data by simplifying the process from a geometrical perspective. Time delay embedding is a common approach to embed EEG signals into the state space. Parameters of this embedding method are variable and the structure of the state space can be entirely different depending on their selection. Additionally, extracted persistent homologies of the state spaces depend on filtration level and the number of points used. In this study, we showed how to adapt false nearest neighbor (FNN) test to find out the suitable/optimal time embedding parameters (i.e., time delay and embedding dimension) for EEG signals, and compared their effects on different types of artefacts and motor intention waves that are commonly used in brain-computer interfaces. We extracted and compared persistent homologies of state spaces that were reconstructed with four different sets of parameters. Later, the effect of filtration level on extracted persistent homologies was compared, and statistical significance levels were computed between leftand right-hand movement imaginations. Finally, computational cost of the discussed methods was found, and the adaptability of this method to a real-time application was evaluated. We demonstrated that the discussed parameters of the TDA approach were highly crucial to extract true topological features of the EEG signals, and the adapted testing approaches depicted the applicability of this approach on real-time analysis of EEG signals.
Target attractor formed via fractional feedback control
(YILDIZ TECHNICAL UNIVYILDIZ CAMPUS, BESIKTAS, ISTANBUL 34349, TURKEY, 2021) Borisenok, Sergey; 0000-0002-1992-628X; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
We discuss here the stabilization problem for an ordinary differential equation (ODE) dynamical model. To make such a control, one can form a Kolesnikov’s subset attracting the phase trajectories to its neighborhood in the phase space via defining the appropriate feedback signal. Kolesnikov’s target attractor algorithm provides the exponential convergence, but at the same time it demands the permanent power supply pumping the energy to the system even if the control goal is achieved. To decrease the power cost of Kolesnikov’s control, we re-formulate the feedback in the form of Caputo’s fractional derivative. In this case the solution to the ODE together with the feedback control signal could be found with the Rida-Arafa method based on the generalized Mittag-Leffler function. We prove that for the certain constraints over the initial condition and the target stabilization level, the integer-dimensional Kolesnikov algorithm can be replaced with the fractional target attractor feedback to provide the minimal power cost.
Target Attractor Tracking of Relative Phase in Bosonic Josephson Junction
(AMER INST PHYSICS2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA, 2016) Borisenok, Sergey; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Borisenok, Sergey
The relative phase of Bosonic Josephson junction in the Josephson regime of Bose-Hubbard model is tracked via the target attractor ('synergetic') feedback algorithm with the inter-well coupling parameter presented as a control function. The efficiency of our approach is demonstrated numerically for Gaussian and harmonic types of target phases.
Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces
(IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA, 2018) Altindis, Fatih; Yilmaz, Bulent; Borisenok, Sergey; Icoz, Kutay; 0000-0002-0947-6166; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain computer interface (BC!) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPIex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions.