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4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022

作者： Lebedeva, Albina Beltyukova, Anna Fedulina, Anastasiya Gerasimova, Svetlana Mishchenko, Mikhail Matveeva, Mariya Maltseva, Kseniya Belov, Alexey Mikhaylov, Alexey Pisarchik, Alexander Kazantsev, Victor University of Nizhny Novgorod Neurotechnologies Department N.I. Lobachevsky State Nizhny Novgorod Russia University of Nizhny Novgorod Faculty of Radiophysics N.I. Lobachevsky State Department of Theory of Oscillations and Automatic Control Nizhny Novgorod Russia Intitute of Neuroscience N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia Center for Neuroscience and Mashine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (数字)9781665463294

ISBN: (纸本)9781665463294

The development of promising neuromorphic compact devices (including micro- and nanochips) that reproduce the information and computational functions of the brain is currently one of the breakthrough areas of modern science and technology. Firstly, this is a promising direction in regenerative medicine - the creation of artificial neuromorphic implants that allow the regulation and modulation of electrically excitable cells, as well as replacing damaged areas of the brain. The use of a memristive structure and its ability to change and maintain internal resistance when voltage is applied and turned off. This makes the structure an almost perfect electronic analogue of synapses. Recent discoveries that nanoscale memristors can mimic the basic properties of synaptic plasticity have set the stage for the fusion of nanoelectronics and living neurons to create biohybrid neural networks (bNNs) interconnected by electronics-based plasticity elements. Thus, investigation the interaction of artificial neural networks and memristive connection with living brain neurons is actually problem in neuroscience and technologies. This study is devoted to the development of neuromorphic system for neuroprosthetics problems. © 2022 IEEE.

关键词： Memristors

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Neuromorphic system development based on adaptive neuronal network to modulate synaptic transmission in hippocampus 3

Neuromorphic system development based on adaptive neuronal n...

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

作者： Lebedeva, Albina Gerasimova, Svetlana Fedulina, Anastasiya Mishchenko, Mikhail Beltyukova, Anna Matveeva, Mariya Mikhaylov, Alexey Pisarchik, Alexander Kazantsev, Victor Neurotechnologies Department N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Theory of Oscillations and Automatic Control Faculty of Radiophysics N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665424745

The project presents fundamental research on the development of concept and basic components of physical circuit based on optoelectronic elements (neuromorphic generators, memristors, optical fibers). This network can replace some functions of brain’s neural networks (such as memory) through adaptive stimulation of neural interfaces at the cellular and cellular networks level. Developing learning system will perform the electrical stimulation of brain areas based on the neural activity recorded and will include electronic devices for recording and processing signals. In addition, this system will include electronic devices for generating electrical stimuli based on hardware models of neurons and memristive devices. The basic idea is to integrate an artificial network with living microcircuits of the brain. The artificial network is capable of adaptively generating electrical output signals and can stimulate a given area of the brain according to the recorded pattern of input signals. We suppose that such a network will be able to reproduce the functions of brain systems, based on the use of learning algorithms. It can be used to expand the functionality of these brain systems and to replace lost functions in pathology or trauma. © 2021 IEEE.

关键词： Memristors

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Integration technology for replacing damaged brain areas with artificial neuronal networks 4

Integration technology for replacing damaged brain areas wit...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Lebedeva, Albina Mishchenko, Mikhail Bardina, Polina Fedulina, Anastasiya Mironov, Andrey Zhuravleva, Zoia Gerasimova, Svetlana Mikhaylo, Alexey Pisarchik, Alexander Kazantsev, Victor Laboratory of Functional Imaging Department of Fundamental and Applied Research N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Theory of Oscillations and Automatic Control Faculty of Radiophysics N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neurotechnologies Department N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia

ISBN: (纸本)9781728172866

We present a novel integration technology for replacing damaged microcircuits in the rat brain with electronic neuronal networks. This technology will allow simulating important brain functions, such as memory, through the adaptive stimulating effects of neural interfaces at cellular and neuronal network levels. The proposed system includes electronic devices to register and process neuronal signals and to adaptively stimulate damaged brain areas according to the online algorithm decoding the registered neuronal activity. The stimulation algorithm is implemented in hardware neural network with memristive connections that allows both network learning and adaptive *** basic idea is to develop components of hardware neural network with memristive connections for adaptive generation of electrical signals to stimulate particular brain areas. Moreover, the uniqueness of the system is adaptive closed-loop connection between two hippocampal neuronal networks. Such a system is very promising for restoring functionality of damaged areas in the human brain to replace lost functions in patients with brain pathologies or trauma. © 2020 IEEE.

关键词： Brain

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Minimizing regret in bandit online optimization in unconstrained and constrained action spaces

arXiv

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arXiv 2018年

作者： Tatarenko, Tatiana Kamgarpour, Maryam Department of Control Theory and Robotics Tu Darmstadt Automatic Control Laboratory Eth Zürich

We consider online convex optimization with a zero-order oracle feedback. In particular, the decision maker does not know the explicit representation of the time-varying cost functions, or their gradients. At each time step, she observes the value of the cost function evaluated at her chosen action (zero-order oracle). The objective is to minimize the regret, that is, the difference between the sum of the costs she accumulates and that of the static optimal action had she known the sequence of cost functions a priori. We present a novel algorithm to minimize the regret in both unconstrained and constrained action spaces. Our algorithm hinges on a classical idea of one-point estimation of the gradients of the cost functions based on their observed values. However, our choice of the randomization introduced and consequently the proof techniques differ from those of past work. Letting T denote the number of queries of the zero-order oracle and n the problem dimension, the regret rate achieved is O(nT2/3) for both constrained and unconstrained action spaces. Moreover, we adapt the presented algorithm to the setting with two-point feedback and demonstrate that the adapted procedure achieves the theoretical lower bound on the regret of Θ(n√T). Copyright © 2018, The Authors. All rights reserved.

关键词： Cost functions

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control system design for micro AUV based on open source hardware

Control system design for micro AUV based on open source har...

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Wang, Chenguang Hu, Zhiqiang Yang, Yi Geng, Lingbo Wang, Ling Department of Control Theory and Engineering Shenyang Ligong University Shenyang Liaoning Province China Department of Ocean Robotics Shenyang Institute of Automation Shenyang Liaoning Province China Department of Electric Automation Shenyang Ligong University Shenyang Liaoning Province China

ISBN: (纸本)9781538680698

This paper mainly introduces the the control system of micro AUV based on open source hardware. The control system is the most important part of the AUV and its performance has a direct impact on the stability and reliability of AUV. Due to the small size and limited energy, micro AUV puts more restrictions on the volume and power consumption of the control system. In this paper, we choose the BeagleBone Black, the open source hardware, as the main controller, the rich peripheral interface of it facilitates data acquisition and centralized processing of the vehicle. Arduino, another type of open source hardware, is used as the node controller of the control system. Compared with the traditional hardware design, this architecture has the advantages of small volume, low power consumption, low cost, short period of development and maintenance, and it also had good performance in data processing and real-time control. The experimental results showed that the control system based on this architecture run effectively. © 2018 IEEE.

关键词： control systems

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Payoff-Based Approach to Learning Nash Equilibria in Convex Games

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IFAC-PapersOnLine 2017年第1期50卷 1508-1513页

作者： Tatarenko T. Kamgarpour M. Department of Control Theory and Robotics TU Darmstadt Germany Automatic Control Laboratory Swiss Federal Institute of Technology Zurich Switzerland

We consider multi-agent decision making, where each agent optimizes its cost function subject to constraints. Agents’ actions belong to a compact convex Euclidean space and the agents’ cost functions are coupled. We propose a distributed payoff-based algorithm to learn Nash equilibria in the game between agents. Each agent uses only information about its current cost value to compute its next action. We prove convergence of the proposed algorithm to a Nash equilibrium in the game leveraging established results on stochastic processes. The performance of the algorithm is analyzed with a numerical case study. © 2017

关键词： game theory Multi-agent decision making payoff-based algorithm

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Integration technology for replacing damaged brain areas with artificial neuronal networks

Integration technology for replacing damaged brain areas wit...

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

作者： Albina Lebedeva Mikhail Mishchenko Polina Bardina Anastasiya Fedulina Andrey Mironov Zoia Zhuravleva Svetlana Gerasimova Alexey Mikhaylo Alexander Pisarchik Victor Kazantsev Neurotechnologies Department Laboratory of Functional Imaging Department of Fundamental and Applied Research N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Theory of Oscillations and Automatic Control Faculty of Radiophysics N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neurotechnologies Department N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Functional Imaging Department of Fundamental and Applied Research N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Russia

ISBN: (数字)9781728172866

ISBN: (纸本)9781728172873

关键词： Hippocampus Biological neural networks Neurons Neuromorphics Memristors Optical fiber amplifiers Electrodes

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Learning generalized nash equilibria in a class of convex games

arXiv

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arXiv 2017年

作者： Tatarenko, Tatiana Kamgarpour, Maryam Department of Control Theory and Robotics TU Darmstadt Germany Automatic Control Laboratory ETH Zürich Switzerland

We consider multi-agent decision making where each agent optimizes its convex cost function subject to individual and coupling constraints. The constraint sets are compact convex subsets of a Euclidean space. To learn Nash equilibria, we propose a novel distributed payoff-based algorithm, where each agent uses information only about its cost value and the constraint value with its associated dual multiplier. We prove convergence of this algorithm to a Nash equilibrium, under the assumption that the game admits a strictly convex potential function. In the absence of coupling constraints, we prove convergence to Nash equilibria under significantly weaker assumptions, not requiring a potential function. Namely, strict monotonicity of the game mapping is sufficient for convergence. We also derive the convergence rate of the algorithm for strongly monotone game maps. Copyright © 2017, The Authors. All rights reserved.

关键词： Game theory

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Stochastic stability of potential function maximizers in continuous version of independent log-linear learning

Stochastic stability of potential function maximizers in con...

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European control Conference (ECC)

作者： Tatiana Tatarenko Department of Control Theory and Robotics TU Darmstadt Germany

This paper deals with game theoretic learning in multi-agent systems with continuous action sets. A potential game is considered, in which the potential function is the global objective function to be maximized in the system under consideration. The focus is on the independent log-linear learning procedure whose discrete version was firstly introduced. In this paper we provide the proof of the stochastic stability of potential function maximizers in case of continuous action games. It implies that an appropriate parameter setting guarantees the algorithm convergence to some potential function maximizer with a given in advanced probability p ∈ (0,1). To analyze the stationary distribution of the underlying continuous Markov chain, we develop here a new method that enables to approximate the initial continuous chain by chains whose long run properties can be established.

关键词： Games Markov processes Kernel Convergence Stability analysis Multi-agent systems Heuristic algorithms

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On local analysis of distributed optimization

On local analysis of distributed optimization

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American control Conference

作者： Tatiana Tatarenko Behrouz Touri Department of Control Theory and Robotics TU Darmstadt Germany Department of Electrical Computer and Energy Engineering University of Colorado Boulder USA

ISBN: (纸本)9781467386838

We study distributed non-convex optimization on a time-varying multiagent network. Each node has access to its own smooth local cost function, and the collective goal is to minimize the sum of these functions. The perturbed push-sum algorithm used for convex distributed optimization in [17] is considered and further studied in [8]. We generalize the result obtained in [8] to the case of non-convex functions. Under some additional assumptions on the gradients we prove the convergence of the distributed push-sum algorithm to the set of critical points of the objective function. Moreover, by utilizing perturbations on the update process, we show the almost sure convergence of the perturbed dynamics to a local minimum of the global objective function.

关键词： Optimization Convergence Linear programming Approximation algorithms Algorithm design and analysis Signal processing algorithms Markov processes

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