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Estimating integrated information in bidirectional neuron-astrocyte communication

Physical Review E 2021年第2期103卷 022410-022410页

作者： Luis Abrego Susanna Gordleeva Oleg Kanakov Mikhail Krivonosov Alexey Zaikin Department of Mathematics University College London London United Kingdom Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Institute for Women's Health University College London London WC1E 6BT United Kingdom Centre for Analysis of Complex Systems Sechenov First Moscow State Medical University (Sechenov University) Moscow Russia

There is growing evidence that suggests the importance of astrocytes as elements for neural information processing through the modulation of synaptic transmission. A key aspect of this problem is understanding the impact of astrocytes in the information carried by compound events in neurons across time. In this paper, we investigate how the astrocytes participate in the information integrated by individual neurons in an ensemble through the measurement of “integrated information.” We propose a computational model that considers bidirectional communication between astrocytes and neurons through glutamate-induced calcium signaling. Our model highlights the role of astrocytes in information processing through dynamical coordination. Our findings suggest that the astrocytic feedback promotes synergetic influences in the neural communication, which is maximized when there is a balance between excess correlation and spontaneous spiking activity. The results were further linked with additional measures such as net synergy and mutual information. This result reinforces the idea that astrocytes have integrative properties in communication among neurons.

关键词： Neural information Neuronal networks Neuroscience

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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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Spatial computing in structured spiking neural networks with a robotic embodiment

arXiv

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arXiv 2021年

作者： Lobov, Sergey A. Mikhaylov, Alexey N. Berdnikova, Ekaterina S. Makarov, Valeri A. Kazantsev, Victor B. Lobachevsky State University of Nizhny Novgorod Gagarin Ave. 23 Nizhny Novgorod603950 Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Universitetskaya Str. 1 Innopolis420500 Russia Center For Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Instituto de Matemática Interdisciplinar F. de Ciencias Matemáticas Universidad Complutense de Madrid Madrid28040 Spain

One of the challenges of modern neurosciwence is creating a "living computer" based on neural networks grown in vitro. Such an artificial device is supposed to perform neurocomputational tasks and interact with the environment when embodied in a robot. Recent studies have identified the most critical challenge, the search for a neural network architecture to implement associative learning. This work proposes a model of modular architecture with spiking neural networks connected by unidirectional couplings. We show that the model enables training a neuro-robot according to Pavlovian conditioning. The robot's performance in obstacle avoidance depends on the ratio of the weights in inter-network couplings. We show that besides STDP, critical factors for successful learning are synaptic and neuronal competitions. We use the recently discovered shortest path rule to implement the synaptic competition. This method is ready for experimental testing. Strong inhibitory couplings implement the neuronal competition in the subnetwork responsible for the unconditional response. Empirical testing of this approach requires a technique for growing neural networks with a given ratio of excitatory and inhibitory neurons not available yet. An alternative is building a hybrid system with in vitro neural networks coupled through hardware memristive connections. © 2021, CC BY.

关键词： Neural networks

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On Choosing Structure for a Machine Learning-based Reaction Force Predictor for Walking Robots

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Journal of Physics: Conference Series 2021年第1期1831卷

作者： E Zalyaev S Savin A Salikhzyanov S Golousov Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis 420500 Russia Edge Vision Ltd. Innopolis 420500 Russia Technology Arrival Ltd Banbury London UK

This paper focuses on the topic of contact reaction prediction for walking robots, namely on the analysis of performances on different structures of the machine-learning-based predictors. Predicting reaction forces is important due to the fact that it can allow us to retrieve a simplified model of the contact scenario, as was proposed in the literature preciously. This allows to turn the problem of contact model identification into a data collection and processing problem. In order to do it effectively, both a data compression (feature extraction) and regression strategies might be used. This research provides an analysis of both with respect to the discussed problem.

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About the geometrical form of the underwater robot platform with non-parallel system the anchor-rope propulsion

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IOP Conference Series: Materials Science and Engineering 2021年第1期1129卷

作者： E S Briskin N G Sharonov Y V Kalinin I A Samozheev Volgograd State Technical University 28 Lenin Avenue Volgograd 400005 Russia Center for Technology Components of Robotics and Mechatronics Innopolis University 1 Universitetskaya Str. Innopolis 420500 Russia

The underwater mobile transport robot, which is a platform assembled from the polygonal pontoon modules, is considered. The system are used anchor-rope movers, which ensures high stability and energy efficiency of the entire system due to interaction with the bottom surface. The aim of the study is to select the geometric shape of the module according to such criteria as specific heat losses in the movers, the length of the step and the tension of the ropes. The formulated criteria characterize the energy consumption and the level of dynamic loads in the considered dynamic system, and the achievement of their optimal values contributes to the increase of autonomy. It takes into account not only the exchange rate resistance to movement, but also the influence of the current, which, depending on the direction, can both promote the movement of the platform and counteract it.

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

arXiv

引用

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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Model Predictive Path Integral Control for Car Driving with Autogenerated Cost map Based on Prior Map and Camera Image

Model Predictive Path Integral Control for Car Driving with ...

引用

International Conference on Intelligent Transportation

作者： Alexander Buyval Aidar Gabdullin Konstantin Sozykin Alexandr Klimchik Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Skolkovo Institute of Science and Technology (Skoltech) Center for Computational and Data-Intensive Science and Engineering (CDISE) Russia

ISBN: (纸本)9781538670255

Model Predictive Path Integral (MPPI) algorithm is an efficient approach to control an autonomous car. However, it requires a cost map. Preparing a cost map in advance is a task that can be completed only for a static closed unchanging environment, but impossible for real city conditions where the environment is dynamic. In this work, we present a method for automatic creation of the cost map based on map data and image from a camera mounted on a self-driving car. The described approach was tested on the Kia Soul car.

关键词： Cameras Automobiles Fires Roads Image segmentation Prediction algorithms Heuristic algorithms

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The research of a movement dynamics two-legged robot with spring-loaded weight

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IOP Conference Series: Materials Science and Engineering 2021年第1期1129卷

作者： D V Bordyugov E S Briskin N G Sharonov Department of Theoretical Mechanics Volgograd State Technical University 28 Lenin Avenue Volgograd 400005 Russia Center for Technology Components of Robotics and Mechatronics Innopolis University 1 Universitetskaya Str. Innopolis 420500 Russia

The movement of a two-legged robot which moves due to oscillations of a load with a given mass fixed on the robot body with the help of the springs, is considered in this research. By changing parameters, such as mass of the load, stiffness of the springs, size of the robot's body and the initial speed of movement, the various trajectories of movement are executed. In this article mathematical model and calculation results have been presented.

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Noise-induced artificial intelligence

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Physical Review Research 2022年第4期4卷 043069-043069页

作者： Alex Zhao Anastasia Ermolaeva Ekkehard Ullner Juergen Kurths Susanna Gordleeva Alexey Zaikin Department of Mathematics University College London London WC1E 6BT United Kingdom Department of Neurotechnology Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod 603022 Russia Institute for Complex Systems and Mathematical Biology Institute for Pure and Applied Mathematics and Department of Physics (SUPA) University of Aberdeen Aberdeen AB24 3UE United Kingdom Potsdam Institute for Climate Impact Research Telegraphenberg Potsdam D-14415 Germany World-Class Research Center Digital Biodesign and Personalized Healthcare Sechenov First Moscow State Medical University Moscow 119991 Russia Neuroscience Research Institute of Samara State Medical University Samara 443099 Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis 420500 Russia Institute for Women's Health University College London London WC1E 6BT United Kingdom

We show that unavoidable stochastic fluctuations are not only affecting information processing in a destructive or constructive way, but may even induce conditions necessary for the artificial intelligence itself. In this proof-of-principle paper we consider a model of a neuron-astrocyte network under the influence of multiplicative noise and show that information encoding (loading, storage, and retrieval of information patterns), one of the paradigmatic signatures of intelligent systems, can be induced by stochastic influence and astrocytes. Hence, astrocytes, recently proved to play an important role in memory and cognitive processing in mammalian brains, may play also an important role in the generation of a system's features providing artificial intelligence functions. Hence, one could conclude that intrinsic stochasticity is probably positively utilized by brains, not only to optimize the signal response but also to induce intelligence itself, and one of the key roles, played by astrocytes in information processing, could be dealing with noises.

关键词： Coupled oscillators Dynamics of networks Neuronal dynamics

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