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2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)最新文献

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Integrated equations reconstruction from scalar time series of periodically driven phase-locked loop system 周期驱动锁相环系统的标量时间序列积分方程重构
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923120
M. Kornilov, M. Sysoeva, V. Matrosov, I. Sysoev
{"title":"Integrated equations reconstruction from scalar time series of periodically driven phase-locked loop system","authors":"M. Kornilov, M. Sysoeva, V. Matrosov, I. Sysoev","doi":"10.1109/DCNA56428.2022.9923120","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923120","url":null,"abstract":"This work aims to develop a technique for identification of the equations of the phase-locked loop system with band pass filter under periodic external driving using a scalar time series. To provide high resistance against measurement noise we suggest to reconstruct a time-integrated model rather than the original system. The proposed approach was shown to be much more efficient than previously developed ones, in particular, it allows robust identification of the system and precise enough estimation of some its parameters even when 10% from RMS measurement noise is present.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123777931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantitative analysis of spike-wave discharge patterns in pentylenetetrazole rat model 戊四氮唑大鼠模型尖波放电模式的定量分析
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923081
A. S. Ershova, E. Suleymanova, A. Grishchenko, L. Vinogradova, I. Sysoev
{"title":"Quantitative analysis of spike-wave discharge patterns in pentylenetetrazole rat model","authors":"A. S. Ershova, E. Suleymanova, A. Grishchenko, L. Vinogradova, I. Sysoev","doi":"10.1109/DCNA56428.2022.9923081","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923081","url":null,"abstract":"The paper reveals differences and similarities in the spike-wave discharges in the hemispheres of rats exposed to pentylenetetrazole (PTZ). PTZ rats are often considered as model of both epilepsy and spreading depression. We propose a simple automated discharges detection approach to process dual channel time series of nine animals. Its sensitivity and specificity were examined on the basis of more than 1500 discharges in both hemispheres separately. According to the results of the analysis, in five out of nine animals the distribution of discharges was different in right and left hemispheres at $mathrm{a}plt 10^{-5}$ significance level. It proves that response to systemic PTZ administration can be symmetric over brain in some animals and asymmetric in others, revealing that individual predisposition of animals is a fundamental problem of cross-hemisphere analysis.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131556241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Experimental Study of Robust Control Law Designed for Synchronization of Electrical Generator Network 发电机网络同步鲁棒控制律的实验研究
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923212
I. Furtat, P. Gushchin, Nguyen Ba Huy
{"title":"Experimental Study of Robust Control Law Designed for Synchronization of Electrical Generator Network","authors":"I. Furtat, P. Gushchin, Nguyen Ba Huy","doi":"10.1109/DCNA56428.2022.9923212","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923212","url":null,"abstract":"This paper describes an experimental study of a novel control scheme proposed for synchronization of multi-machine power system with unknown parameters, environmental influence and noise in measurements. The proposed algorithm is based on use the linear low-pass filter and delays that allow one to decrease the influence of measurement noise and guaranty grid synchronization. Experimental studies have shown efficiency of the proposed algorithm in various emergency modes.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"66 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131649429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Neural network technology for quality control of the milk packaging process 牛奶包装过程质量控制的神经网络技术
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923283
M. S. Chvanova, Ilya A. Bakalets
{"title":"Neural network technology for quality control of the milk packaging process","authors":"M. S. Chvanova, Ilya A. Bakalets","doi":"10.1109/DCNA56428.2022.9923283","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923283","url":null,"abstract":"The food industry needs to improve production processes. Including the use of the potential of computer vision systems for visual control and monitoring of the quality of food packaging processes. The article discusses the use of a neural network to detect signs of damage to the packaging areas of pasteurized milk.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127615786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A system for diagnosing the psychophysiological state of a person based on the control of nonlinear characteristics of cardiorespiratory interaction 一种基于控制心肺相互作用非线性特征的人的心理生理状态诊断系统
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923174
A. N. Hramkov, E. Borovkova, Elizaveta S. Dubinkina, V. Ponomarenko, Yurii M. Ishbulatov, M. Prokhorov
{"title":"A system for diagnosing the psychophysiological state of a person based on the control of nonlinear characteristics of cardiorespiratory interaction","authors":"A. N. Hramkov, E. Borovkova, Elizaveta S. Dubinkina, V. Ponomarenko, Yurii M. Ishbulatov, M. Prokhorov","doi":"10.1109/DCNA56428.2022.9923174","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923174","url":null,"abstract":"Purpose of the work is to study changes in the characteristics of cardiorespiratory interaction for diagnosing changes in the psychophysiological state of a person. We analyzed the records of 30 healthy subjects aged 18 to 24 years. The values of the average time interval between heart contractions, the specific phase coherence of respiratory signals and RR-intervals were evaluated in the work. As a result, it was shown that the coherence of respiration signals and RR intervals changes during stress tests, and the methods used in the work can be used to create a stress diagnostic system.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133793320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Estimation of Periodic Force Parameters Using a Spiking Neural Network 基于脉冲神经网络的周期性力参数估计
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923195
M. Prokhorov, E. Navrotskaya, D. Kulminskiy, V. Ponomarenko
{"title":"Estimation of Periodic Force Parameters Using a Spiking Neural Network","authors":"M. Prokhorov, E. Navrotskaya, D. Kulminskiy, V. Ponomarenko","doi":"10.1109/DCNA56428.2022.9923195","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923195","url":null,"abstract":"We propose a method for estimating the parameters of an external periodic force using a spiking network of neuronlike oscillators. The method is based on the assessment of the amplitude and frequency of the external force by the number of spikes generated by the network over a certain fixed time in response to an external periodic stimulus. We demonstrate the efficacy of our method both numerically and experimentally using a network of coupled nonidentical neuronlike FitzHugh-Nagumo oscillators.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130788514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Memristive model of the Fitzhugh-Nagumo neuronal oscillator Fitzhugh-Nagumo神经元振荡器的记忆模型
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923075
Ivan Kipelkin, S. Gerasimova, Davud Guseinov, D. Pavlov, V. Vorontsov, A. Mikhaylov, V. Kazantsev
{"title":"Memristive model of the Fitzhugh-Nagumo neuronal oscillator","authors":"Ivan Kipelkin, S. Gerasimova, Davud Guseinov, D. Pavlov, V. Vorontsov, A. Mikhaylov, V. Kazantsev","doi":"10.1109/DCNA56428.2022.9923075","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923075","url":null,"abstract":"We propose a mathematical model of the Fitzhugh-Nagumo neuron employing memristor-based nonlinearity. The model implements excitable and oscillatory regimes of neuron-like firing. We obtain and analyze various dynamical modes of the memristor-based FitzHugh-Nagumo neuron.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131200207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Synaptic scaling as an essential component of Hebbian learning 突触缩放是Hebbian学习的重要组成部分
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923054
Vasilisa Y. Stepasyuk, V. A. Makarov, S. Lobov, V. Kazantsev
{"title":"Synaptic scaling as an essential component of Hebbian learning","authors":"Vasilisa Y. Stepasyuk, V. A. Makarov, S. Lobov, V. Kazantsev","doi":"10.1109/DCNA56428.2022.9923054","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923054","url":null,"abstract":"Hebbian plasticity is a prominent learning mechanism for brain neural networks. However, its formal definition based on the time-matching of pre and postsynaptic activity can lead to a saturation of synaptic weights. On the one hand, the so-called forgetting function formally allows bounding the synaptic weights, but its biological basis remains unclear. On the other hand, biological neurons exhibit homeostatic plasticity, particularly synaptic scaling, which helps a neuron control (scale) the synaptic effectiveness across the synapses. This work proposes a mathematical model of Hebbian learning with synaptic scaling in a spiking neuron. Numerical simulations show that this biologically justified model exhibits behavior similar to the standard model with the forgetting function. We illustrate the results in a test-bed problem of learning frequency patterns by a high-dimensional neuron.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132222521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Neuronal intermittent synchronization enhanced by astrocytes 星形胶质细胞增强神经元间歇同步
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923142
S. Makovkin, M. Ivanchenko, S. Gordleeva
{"title":"Neuronal intermittent synchronization enhanced by astrocytes","authors":"S. Makovkin, M. Ivanchenko, S. Gordleeva","doi":"10.1109/DCNA56428.2022.9923142","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923142","url":null,"abstract":"In this paper, a study was presented how the interaction of slow modulating astrocytic signaling on fast synaptic transmission between neurons controls fluctuations in the network of hippocampal interneurons that receive input signals from pyramidal cells. The work demonstrates that astrocytic control of signal transmission between neurons improves the synchronization of oscillations and expands the area of synchronization of oscillations in the found parameters of synaptic conduction between synapses of interneurons. The amplification of synaptic transmission caused by astrocytes significantly increases the synchronization of network oscillations in a wide range of parameters of the mathematical model.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"242 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133648890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data 长短时记忆人工神经网络、合成数据和微调如何改进原始脑电图数据的分类
2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA) Pub Date : 2022-09-14 DOI: 10.1109/DCNA56428.2022.9923206
A. Nasybullin, V. Maksimenko, S. Kurkin
{"title":"How Long short-term memory artificial neural network, synthetic data, and fine-tuning improve the classification of raw EEG data","authors":"A. Nasybullin, V. Maksimenko, S. Kurkin","doi":"10.1109/DCNA56428.2022.9923206","DOIUrl":"https://doi.org/10.1109/DCNA56428.2022.9923206","url":null,"abstract":"In this paper, we discuss a Machine Learning pipeline for the classification of EEG data. We propose a combination of synthetic data generation, long short-term memory artificial neural network (LSTM), and fine-tuning to solve classification problems for experiments with implicit visual stimuli, such as the Necker cube with different levels of ambiguity. The developed approach increased the quality of the classification model of raw EEG data.","PeriodicalId":110836,"journal":{"name":"2022 6th Scientific School Dynamics of Complex Networks and their Applications (DCNA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125031210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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