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Interactions within the Spatially-distributed Cortical Neural Network during Visual Attentional Tasks

Interactions within the Spatially-distributed Cortical Neura...

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International Conference on Computational Intelligence and Networks (CINE)

作者： Alexander E. Hramov Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Component Innopolis University

ISBN: (数字)9781728156880

ISBN: (纸本)9781728156897

Many physiological experiments demonstrate that a cognitive performance during sustained attention tasks is not maintained at a constant level. Such behavioral lowfrequency variations are connected with dynamics of attentionrelated cortical neuronal networks. At the same time neurophysiological reason of a such behavior remains unclear. In this talk we analyze the neuronal interactions in alpha and beta bands by means of 31-channels EEG processing. As a visual attention task we use a binary classification of a large number of presented images - ambiguous Necker cubes. Analyzing of response time of subjects indicates that processing of similar tasks takes both long and short reaction time. Provided analysis based on complex network theory reveals strong correlation between response time and duration of formation of a functional frontoparietal functional network associated with perceptual decision-making process. Having considered a functional topology during a prestimulus period, we find that lower reaction times are subserved by a strong prestimulus functional connectivity over a somatosensory and sensorimotor areas of cortex. Finally, we show that high reaction times are dominant during the beginning of experiment and suppose that this time is required for a prestimulus template formation. Since similar visual tasks are presented several times during the experiment, brain uses their templates to perform them faster and effectively.

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Approach to collaborative BCI for enhancing human-to-human interaction in shared visual task

Approach to collaborative BCI for enhancing human-to-human i...

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Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Grubov, Vadim V. Maksimenko, Vladimir A. Center for Technologies in Robotics and Mechatronics Component Innopolis University 1 Universitetskaya Str Innopolis420500 Russia Saratov State Medical University 137 Bolshaja Sadovaja Str Saratov410000 Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

In the present study we aimed to find specific characteristic based on brain activity, that can be used to evaluate attention and, thus, can be used in brain-computer interface. We introduced a characteristic based on prestimulus beta-rhythm activity and proposed an approach to collaborative BCI aimed to enhance human-to-human interaction while performing shared visual task. We also described general setup for such BCI and its possible application in long task of classifying ambiguous visual stimuli with varying degrees of ambiguity by a group of people. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Brain

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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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Development a cross-loop during adaptive stimulation of hippocampal neural networks by an artificial neural network 4

Development a cross-loop during adaptive stimulation of hipp...

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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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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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