Short-term memory in neuron-astrocyte network

In this paper, we study the role of astrocyte-induced modulation of signal transmission in network of synaptically coupled spiking neurons in the mechanisms of the short-term memory formation. We show, that due to the local spatial synchronization of signaling in the neural network induced by the astrocyte during the duration of the Ca 2+ signal, the proposed neuron-astrocyte network can operate as a temporal Hopfield network

Modelling working memory in neuron-astrocyte network

Working memory is one of the most intriguing brain function phenomena that enables storage and recognition of several information patterns simultaneously in the form of coherent activations of specific brain circuitries. These patterns can be recalled and, if physiologically (cognitively) significant, further transferred to long term storages by cortical circuits. In the paper, we show how the working memory can be effectively organized by a multiscale network model composed of spiking neurons accompanied by an astrocytic network. The latter serves as the temporal storage of information patterns that can be manipulated (relearned, retrieved, transferred) during astrocytic calcium activation. In turn, the activation of the astrocyte network is possible when coherent firing occurs in corresponding sites of the neuronal layer. We study the role of interplay of the astrocyte-induced modulation of signal transmission in neural network and the Hebbian synaptic plasticity in the working memory organization. We show that modulation of synaptic communication caused by astrocytes does not exclude but rather complements Hebbian synaptic plasticity, and they can perfectly work in parallel. We believe this model is a significant step towards confirming the importance of non-neuron species (e.g. astrocytes) in the formation and sustainability of cognitive functions of the brain

Estimating integrated information in bidirectional neuron-astrocyte communication

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

Astrocyte-induced positive integrated information in neuron-astrocyte ensembles

Integrated information is a quantitative measure from information theory of how tightly all parts of a system are interconnected in terms of information exchange. In this study we show that astrocytes, playing an important role in regulation of information transmission between neurons, may contribute to a generation of positive integrated information in neuronal ensembles. Analytically and numerically we show that the presence of astrocytic regulation of neurotransmission may be essential for this information attribute in neuroastrocytic ensembles. Moreover, the proposed “spiking-bursting” mechanism of generating positive integrated information is shown to be generic and not limited to neuron-astrocyte networks and is given a complete analytic description

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks—e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called “seven pillars of aging” combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging

Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks—e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called “seven pillars of aging” combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research

Transcranial Magnetic Stimulation as a Method for Preparing the Human Condition for Imaginary Motor Activity

Сформулирована гипотеза, что с помощью транскраниальной магнитной стимуляции (ТМС), воздействующей на определенную зону головного мозга, возможно подготовить состояние человека к совершению воображаемой двигательной активности, что должно выражаться в улучшении характеристик, отражающих успешность выполнения моторного воображения. Целями представленного исследования являются проверка данной гипотезы и изучение с применением ЭЭГ механизмов и закономерностей влияния ТМС на выполнение моторного воображения (МВ). Выявлено, что ТМС левой стороны дорсолатерального префронтального кортекса приводит к увеличению скорости МВ.We have formulated the hypothesis that using transcranial magnetic stimulation (TMS), influencing a particular zone of a brain, it is possible to prepare a condition of the person to performance of an imaginary motor activity that should be expressed in the improvement of the characteristics reflecting the success of the performance of motor imagination (MI). The goal of the presented study is to verify this hypothesis and to study with the use of EEG the mechanisms and regularities of TMS influence the performance of MI. We have revealed that TMS of the left side of the dorsolateral prefrontal cortex leads to an increase in the rate of MI.Работа выполнена при поддержке РНФ (грант № 21‑72‑10121). Семен Куркин благодарит совет по грантам Президента РФ за поддержку работ, связанных с разработкой методов восстановления источников (МД‑1921.2020.9)

DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES

Aim - to develop a hardware-software complex with combined command-proportional control of robotic devices based on electromyography (EMG) and electroencephalography (EEG) signals. Materials and methods. EMG and EEG signals are recorded using our original units. The system also supports a number of commercial EEG and EMG recording systems, such as NVX52 (MCS ltd, Russia), DELSYS Trigno (Delsys Inc, USA), MYO Thalmic (Thalmic Labs, Canada). Raw signals undergo preprocessing and feature extraction. Then features are fed to classifiers. The interpretation unit controls robotic devices on the base of classified EEG- and EMG-patterns and muscle effort estimation. The number of controlled devices includes mobile robot LEGO NXT Mindstorms (LEGO, Denmark), humanoid robot NAO (Aldebaran, France) and exoskeleton Ilia Muromets (UNN, Russia). Results. We have developed and tested an interface combining command and proportional control based on EMG signals. We have determined the parameters providing optimal characteristics of classification accuracy of EMG patterns, as well as the speed and accuracy of proportional control. Also we have developed and tested a BCI interface based on motor imagined patterns. Both EMG and EEG interfaces are included into hardware and software system. The system combines outputs of the interfaces and sends commands to a robotic device. Conclusion. We have developed and approved the hardware-software system on the basis of the combined command-proportional EMG and EEG control of external robotic devices