Integrated Information in the Spiking-Bursting Stochastic Model

This study presents a comprehensive analytic description in terms of the empirical "whole minus sum" version of Integrated Information in comparison to the "decoder based" version for the "spiking-bursting" discrete-time, discrete-state stochastic model, which was recently introduced to describe a specific type of dynamics in a neuron-astrocyte network. The "whole minus sum" information may change sign, and an interpretation of this transition in terms of "net synergy" is available in the literature. This motivates our particular interest to the sign of the "whole minus sum" information in our analytical consideration. The behavior of the "whole minus sum" and "decoder based" information measures are found to bear a lot of similarity, showing their mutual asymptotic convergence as time-uncorrelated activity is increased, with the sign transition of the "whole minus sum" information associated to a rapid growth in the "decoder based" information. The study aims at creating a theoretical base for using the spiking-bursting model as a well understood reference point for applying Integrated Information concepts to systems exhibiting similar bursting behavior (in particular, to neuron-astrocyte networks). The model can also be of interest as a new discrete-state test bench for different formulations of Integrated Information

Impact of Astrocytic Coverage of Synapses on the Short-Term Memory of a Computational Neuron-Astrocyte Network

Working memory refers to the capability of the nervous system to selectively retain short-term memories in an active state. The long-standing viewpoint is that neurons play an indispensable role and working memory is encoded by synaptic plasticity. Furthermore, some recent studies have shown that calcium signaling assists the memory processes and the working memory might be affected by the astrocyte density. Over the last few decades, growing evidence has also revealed that astrocytes exhibit diverse coverage of synapses which are considered to participate in neuronal activities. However, very little effort has yet been made to attempt to shed light on the potential correlations between these observations. Hence, in this article, we leverage a computational neuron–astrocyte model to study the short-term memory performance subject to various astrocytic coverage and we demonstrate that the short-term memory is susceptible to this factor. Our model may also provide plausible hypotheses for the various sizes of calcium events as they are reckoned to be correlated with the astrocytic coverage

Mammalian Brain As a Network of Networks

Acknowledgements AZ, SG and AL acknowledge support from the Russian Science Foundation (16-12-00077). Authors thank T. Kuznetsova for Fig. 6.Peer reviewedPublisher PD

Brain aging and garbage cleaning : Modelling the role of sleep, glymphatic system, and microglia senescence in the propagation of inflammaging

Brain aging is a complex process involving many functions of our body and described by the interplay of a sleep pattern and changes in the metabolic waste concentration regulated by the microglial function and the glymphatic system. We review the existing modelling approaches to this topic and derive a novel mathematical model to describe the crosstalk between these components within the conceptual framework of inflammaging. Analysis of the model gives insight into the dynamics of garbage concentration and linked microglial senescence process resulting from a normal or disrupted sleep pattern, hence, explaining an underlying mechanism behind healthy or unhealthy brain aging. The model incorporates accumulation and elimination of garbage, induction of glial activation by garbage, and glial senescence by over-activation, as well as the production of pro-inflammatory molecules by their senescence-associated secretory phenotype (SASP). Assuming that insufficient sleep leads to the increase of garbage concentration and promotes senescence, the model predicts that if the accumulation of senescent glia overcomes an inflammaging threshold, further progression of senescence becomes unstoppable even if a normal sleep pattern is restored. Inverting this process by "rejuvenating the brain" is only possible via a reset of concentration of senescent glia below this threshold. Our model approach enables analysis of space-time dynamics of senescence, and in this way, we show that heterogeneous patterns of inflammation will accelerate the propagation of senescence profile through a network, confirming a negative effect of heterogeneity

Noise-induced artificial intelligence

ACKNOWLEDGMENTS The study was supported by Medical Research Council Grant No. MR/R02524X/1. A.E., S.G., and A.Z. thank the Russian Science Foundation under Grant No. 22-12-00216. The work of A.E. was supported by RFBR according to Research Project No. 20-32-90151.Peer reviewedPublisher PD

Noise-induced artificial intelligence

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

Astrocytes mediate analogous memory in a multi-layer neuron-astrocyte network

Modeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input

TREC and KREC Levels as a Predictors of Lymphocyte Subpopulations Measured by Flow Cytometry

Primary immunodeficiency diseases (PID) is a heterogeneous group of disorders caused by genetic defects of the immune system, which manifests clinically as recurrent infections, autoimmune diseases, or malignancies. Early detection of other PID remains a challenge, particularly in older children due to milder and less specific symptoms, a low level of clinician PID awareness and poor provision of hospital laboratories with appropriate devices. T-cell recombination excision circles (TREC) and kappadeleting element recombination circle (KREC) in a dried blood spot and in peripheral blood using real-time polymerase chain reaction (PCR) are used as a tool for severe combined immune deficiency but not in PID. They represent an attractive and cheap target for a more extensive use in clinical practice. This study aimed to assess TREC/KREC correspondence with lymphocyte subpopulations, measured by flow cytometry and evaluate correlations between TREC/KREC, lymphocyte subpopulations and immunoglobulins. We carried out analysis of data from children assessed by clinical immunologists at Speransky Children’s Hospital, Moscow, Russia with suspected immunodeficiencies between May 2013 and August 2016. Peripheral blood samples were sent for TREC/KREC, flow cytometry (CD3, CD4, CD8, and CD19), IgA, IgM, and IgG analysis. A total of 839 samples were analyzed for using TREC assay and flow cytometry and 931 KREC/flow cytometry. TREC demonstrated an AUC of 0.73 (95% CI 0.70–0.76) for CD3, 0.74 (95% CI 0.71–0.77) for CD4 and 0.67 (95% CI 0.63–0.70) for CD8, respectively, while KREC demonstrated an AUC of 0.72 (95% CI 0.69–0.76) for CD19. Moderate correlation was found between the levels of TREC and CD4 (r = 0.55, p < 0.01) and KREC with CD19 (r = 0.56, p < 0.01). In this study, promising prediction models were tested. We found that TREC and KREC are able to moderately detect abnormal levels of individual lymphocyte subpopulations. Future research should assess associations between TREC/KREC and other lymphocyte subpopulations and approach TREC/KREC use in PID diagnosis