Персона: Сбоев, Александр Георгиевич
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Институт ядерной физики и технологий
Цель ИЯФиТ и стратегия развития - создание и развитие научно-образовательного центра мирового уровня в области ядерной физики и технологий, радиационного материаловедения, физики элементарных частиц, астрофизики и космофизики.
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Александр Георгиевич
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- ПубликацияТолько метаданныеA Comparison of Two Variants of Memristive Plasticity for Solving the Classification Problem of Handwritten Digits Recognition(2022) Davydov, Y.; Rybka, R.; Vlasov, D.; Serenko, A.; Sboev, A.; Сбоев, Александр Георгиевич© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Nowadays, the task of creating and training spiking neural networks (SNN) is extremely relevant due to their high energy efficiency achieved by implementing such networks via neuromorphic hardware. Especially interesting is the possibility of building SNNs based on memristors, which have properties that potentially allow them to be used as analog synapses. With that in mind, it seems relevant to study spike networks built upon plasticity rules that correspond to the experimentally observed nonlinear laws of conductivity change in memristors. Earlier it was shown that spiking neural networks trained with a biologically inspired local STDP (Spike-Timing-Dependent Plasticity) rule are capable of solving classification problems successfully. In addition, it was also demonstrated that classification problems can also be solved with spiking neural networks operating with a plasticity rule that models the change in conductivity in nanocomposite (NC) memristors. This paper presents a continuation of the study of the applicability of memristive plasticity rules on the handwritten digit recognition problem. Two types of memristive plasticity are compared: for nanocomposite and PPX memristors. It is shown that both models can successfully solve the classification problem, and the key differences between them are identified.
- ПубликацияТолько метаданныеProbabilistic Spiking Neural Network with Correlation-Based Memristive Synaptic Update(2025) Kunitsyn, D.; Sboev, A.; Davydov, Y.; Rybka, R.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович
- ПубликацияТолько метаданныеEnsembling SNNs with STDP Learning on Base of Rate Stabilization for Image Classification(2021) Serenko, A.; Sboev, A.; Rybka, R.; Vlasov, D.; Сбоев, Александр Георгиевич; Рыбка, Роман Борисович© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.In spite of a number of existing spiking neural network models for image classification, it still remains relevant from both methodological and practical points of view to develop a model as simple as possible, while at the same time applicable to classification tasks with various types of data, be it real vectors or images. Our previous work proposed a simple spiking network with Spike-Timing-Dependent-Plasticity (STDP) learning for solving real-vector classification tasks. In this paper, that method is extended to image recognition tasks and enhanced by aggregating neurons into ensembles. The network comprises one layer of neurons with STDP-plastic inputs receiving pixels of input images encoded with spiking rates. This work considers two approaches for aggregating neurons’ output activities within an ensemble: by averaging their output spiking rates (i.e. averaging outputs before decoding spiking rates into class labels) and by voting with decoded class labels. Ensembles aggregated by output frequencies are shown to achieve a significant accuracy increase up to 95% (by F1-score) for the Optdigits handwritten digit dataset, and is comparable with conventional machine learning approaches.
- ПубликацияТолько метаданныеOn Solving Classification Tasks Using Spiking Neural Network with Memristive Plasticity and Correlation-Based Learning(2025) Sboev, A.; Kunitsyn, D.; Davydov, Y.; Vlasov, D.; Сбоев, Александр Георгиевич
- ПубликацияТолько метаданныеNeural Network Method for Determining the Direction of a Person's Gaze based on a Web Camera Image Analysis(2022) Skorokhodov, M. S.; Sboev, A. G.; Moloshnikov, I. A.; Rybka, R. B.; Сбоев, Александр ГеоргиевичDetermining the direction of a person s gaze improves the accuracy of the voice control systems, which are relevant in the actively developing voice assistant creation field. This paper proposes a neural network method for determining the gaze direction based on the web camera image analysis. In the course of the paper, a corpus of data was collected and marked up with preprocessed data from the web camera and the point of view direction on the monitor screen. A neural network model was built based on fully connected and convolutional layers. The created neural network model for determining the gaze direction demonstrated an improvement of 13% in pixel error on the monitor screen compared to the existing open-source solutions. The created neural network model was implemented in the voice control system of a mobile robot, which facilitated minimization of the ambiguity in the analysis of movement commands towards objects.
- ПубликацияТолько метаданныеModeling the dynamics of spiking networks with memristor-based STDP to solve classification tasks(2021) Vlasov, D.; Rybka, R.; Davydov, Y.; Serenko, A.; Sboev, A.; Сбоев, Александр Георгиевич© 2021 by the authors. Licensee MDPI, Basel, Switzerland.The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches.
- ПубликацияОткрытый доступПРОГРАММА МОДЕЛИРОВАНИЯ ОБУЧЕНИЯ СПАЙКОВОЙ СЕТИ С STDP С КОРРЕЛЯЦИОННЫМ КОДИРОВАНИЕМ ВХОДНЫХ ДАННЫХ(Федеральное государственное бюджетное учреждение «Национальный исследовательский центр «Курчатовский институт», 2022) Серенко, А. В.; Сбоев, А. Г.; Рыбка, Р. Б.; Сбоев, Александр Георгиевич; Рыбка, Роман БорисовичПрограмма реализует решение типовых классификационных задач ирисов Фишера и Висконсинского рака груди путём обучения спайковой нейронной сети с синаптической пластичностью Spike-Timing-Dependent Plasticity (STDP). Входные данные кодируются взаимной корреляцией входных спайковых последовательностей. Результат классификации декодируется путём сравнения корреляции выходных последовательностей спайков со входными на тестовой выборке с распределением этих корреляций на обучающей выборке.
- ПубликацияТолько метаданныеOn the accuracy of Covid-19 forecasting methods in Russia for two years(2022) Moloshnikov, I. A.; Sboev, A. G.; Naumov, A. V.; Zavertyaev, S. V.; Rybka, R. B.; Сбоев, Александр ГеоргиевичThe effectiveness of predicting the dynamics of the coronavirus pandemic for Russia as a whole and for Moscow is studied for a two-year period beginning March 2020. The comparison includes well-proven population models and statistic methods along with a new data-driven model based on the LSTM neural network. The latter model is trained on a set of Russian regions simultaneously, and predicts the total number of cases on the 14-day forecast horizon. Prediction accuracy is estimated by the mean absolute percent error (MAPE). The results show that all the considered models, both simple and more complex, have similar efficiency. The lowest error achieved is 18% MAPE for Moscow and 8% MAPE for Russia.
- ПубликацияТолько метаданныеActor-Critic Spiking Neural Network with RSTDP Actor Learning and TD-LTP Critic Learning(2024) Tihomirov, Y.; Rybka, R.; Serenko, A.; Sboev, A. G.; Сбоев, Александр Георгиевич
- ПубликацияОткрытый доступНейросетевое моделирование и машинное обучение на основе экспериментальных и наблюдательных данных(НИЦ "Курчатовский институт", 2021) Сбоев, А. Г.; Сбоев, Александр Георгиевич; Кудряшов, Н. .