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

Network response synchronization enhanced by synaptic plasticity

Synchronization of neural network response on spatially localized periodic stimulation was studied. The network consisted of synaptically coupled spiking neurons with spike-timing-dependent synaptic plasticity (STDP). Network connectivity was defined by time evolving matrix of synaptic weights. We found that the steady-state spatial pattern of the weights could be rearranged due to locally applied external periodic stimulation. A method for visualization of synaptic weights as vector field was introduced to monitor the evolving connectivity matrix. We demonstrated that changes in the vector field and associated weight rearrangements underlay an enhancement of synchronization range

Social stress drives the multi-wave dynamics of COVID-19 outbreaks

The dynamics of epidemics depend on how people's behavior changes during an outbreak. At the beginning of the epidemic, people do not know about the virus, then, after the outbreak of epidemics and alarm, they begin to comply with the restrictions and the spreading of epidemics may decline. Over time, some people get tired/frustrated by the restrictions and stop following them (exhaustion), especially if the number of new cases drops down. After resting for a while, they can follow the restrictions again. But during this pause the second wave can come and become even stronger then the first one. Studies based on SIR models do not predict the observed quick exit from the first wave of epidemics. Social dynamics should be considered. The appearance of the second wave also depends on social factors. Many generalizations of the SIR model have been developed that take into account the weakening of immunity over time, the evolution of the virus, vaccination and other medical and biological details. However, these more sophisticated models do not explain the apparent differences in outbreak profiles between countries with different intrinsic socio-cultural features. In our work, a system of models of the COVID-19 pandemic is proposed, combining the dynamics of social stress with classical epidemic models. Social stress is described by the tools of sociophysics. The combination of a dynamic SIR-type model with the classical triad of stages of the general adaptation syndrome, alarm-resistance-exhaustion, makes it possible to describe with high accuracy the available statistical data for 13 countries. The sets of kinetic constants corresponding to optimal fit of model to data were found. They characterize the ability of society to mobilize efforts against epidemics and maintain this concentration over time, and can further help in the development of strategies specific to a particular society.Comment: Minor corrections, enriched discussion and extended bibliograph

A Human-Computer Interface based on Electromyography Command-Proportional Control

Surface electromyographic (sEMG) signals represent a superposition of the motor unit action potentials that can be recorded by electrodes placed on the skin. Here we explore the use of an easy wearable sEMG bracelet for a remote interaction with a computer by means of hand gestures. We propose a human-computer interface that allows simulating “mouse” clicks by separate gestures and provides proportional control with two degrees of freedom for flexible movement of a cursor on a computer screen. We use an artificial neural network (ANN) for processing sEMG signals and gesture recognition both for mouse clicks and gradual cursor movements. At the beginning the ANN goes through an optimized supervised learning using either rigid or fuzzy class separation. In both cases the learning is fast enough and requires neither special measurement devices nor specific knowledge from the end-user. Thus, the approach enables building of low-budget user-friendly sEMG solutions