Efficient Transmission of Subthreshold Signals in Complex Networks of Spiking Neurons

We investigate the efficient transmission and processing of weak, subthreshold signals in a realistic neural medium in the presence of different levels of the underlying noise. Assuming Hebbian weights for maximal synaptic conductances—that naturally balances the network with excitatory and inhibitory synapses—and considering short-term synaptic plasticity affecting such conductances, we found different dynamic phases in the system. This includes a memory phase where population of neurons remain synchronized, an oscillatory phase where transitions between different synchronized populations of neurons appears and an asynchronous or noisy phase. When a weak stimulus input is applied to each neuron, increasing the level of noise in the medium we found an efficient transmission of such stimuli around the transition and critical points separating different phases for well-defined different levels of stochasticity in the system. We proved that this intriguing phenomenon is quite robust, as it occurs in different situations including several types of synaptic plasticity, different type and number of stored patterns and diverse network topologies, namely, diluted networks and complex topologies such as scale-free and small-world networks. We conclude that the robustness of the phenomenon in different realistic scenarios, including spiking neurons, short-term synaptic plasticity and complex networks topologies, make very likely that it could also occur in actual neural systems as recent psycho-physical experiments suggest.The authors acknowledge support from the Spanish Ministry of economy and competitiveness under the project FIS2013-43201-P

Instability of attractors in auto–associative networks with bio–inspired fast synaptic noise

We studied auto–associative networks in which synapses are noisy on a time scale much shorter that the one for the neuron dynamics. In our model a presynaptic noise causes postsynaptic depression as recently ob- served in neurobiological systems. This results in a nonequilibrium condi- tion in which the network sensitivity to an external stimulus is enhanced. In particular, the fixed points are qualitatively modified, and the system may easily scape from the attractors. As a result, in addition to pattern recognition, the model is useful for class identification and categorization.MCyT and FEDER (project No. BFM2001- 2841 and Ram´on y Cajal contract

Emergence and interpretation of oscillatory behaviour similar to brain waves and rhythms

Electroencephalography (EEG) monitors -by either intrusive or noninvasive electrodes-time and frequency variations and spectral content of voltage fluctuations or waves, known as brain rhythms, which in some way uncover activity during both rest periods and specific events in which the subject is under stimulus. This is a useful tool to explore brain behavior, as it complements imaging techniques that have a poorer temporal resolution. We here approach the understanding of EEG data from first principles by numerical simulating and studying a networked model of excitatory and inhibitory neurons which generates a variety of comparable waves. In fact, we thus numerically reproduce oscillatory behavior similar to alpha, beta, gamma and other rhythms as observed by EEG recordings, and identify the details of the respectively involved complex phenomena, including a precise relationship between an input and the collective response to it. It ensues the potentiality of our model to better understand actual brain oscillatory activity in normal and pathological situations, and we also describe kind of stochastic resonance phenomena which could be useful to locate main qualitative changes of brain activity in (e.g.) humans. (C) 2019 Elsevier B.V. All rights reserved.We acknowledge the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion"(AEI) for financial support under grant FIS2017-84256-P (FEDER funds)

Chimera states in hybrid coupled neuron populations

Here we study the emergence of chimera states, a recently reported phenomenon referring to the coexistence of synchronized and unsynchronized dynamical units, in a population of Morris-Lecar neurons which are coupled by both electrical and chemical synapses, constituting a hybrid synaptic architecture, as in actual brain connectivity. This scheme consists of a nonlocal network where the nearest neighbor neurons are coupled by electrical synapses, while the synapses from more distant neurons are of the chemical type. We demonstrate that peculiar dynamical behaviors, including chimera state and traveling wave, exist in such a hybrid coupled neural system, and analyze how the relative abundance of chemical and electrical synapses affects the features of chimera and different synchrony states (i.e. incoherent, traveling wave and coherent) and the regions in the space of relevant parameters for their emergence. Additionally, we show that, when the relative population of chemical synapses increases further, a new intriguing chaotic dynamical behavior appears above the region for chimera states. This is characterized by the coexistence of two distinct synchronized states with different amplitude, and an unsynchronized state, that we denote as a chaotic amplitude chimera. We also discuss about the computational implications of such state. (c) 2020 Elsevier Ltd. All rights reserved.MU acknowledges Bulent Ecevit University Research Foundation, Turkey under Project No. BAP2018-39971044-01. JJT acknowledges the Spanish Ministry for Science and Technology and the "Agencia Espanola de Investigacion, Spain'' (AEI) for financial support under grant FIS2017-84256-P (FEDER funds). AC acknowledges financial support from the Scientific and Technological Research Council of Turkey (TUBITAK) BIDEB-2214/A International Research Fellowship Program, and the hospitality of the Institute Carlos I for Theoretical and Computational Physics at University of Granada

Dirac synchronization is rhythmic and explosive

G.B. acknowledges funding from the Alan Turing Institute and from Royal Society IEC \NSFC\191147. J.J.T. acknowledges financial support from the Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_00173. This work is also part of the Project of I+D+i Ref. PID2020-113681GB-I00, financed by MICIN/AEI/10.13039/ 501100011033 and FEDER “A way to make Europe". This research utilized Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT. https://doi.org/10.5281/zenodo. 438045.Topological signals defined on nodes, links and higher dimensional simplices define the dynamical state of a network or of a simplicial complex. As such, topological signals are attracting increasing attention in network theory, dynamical systems, signal processing and machine learning. Topological signals defined on the nodes are typically studied in network dynamics, while topological signals defined on links are much less explored. Here we investigate Dirac synchronization, describing locally coupled topological signals defined on the nodes and on the links of a network, and treated using the topological Dirac operator. The dynamics of signals defined on the nodes is affected by a phase lag depending on the dynamical state of nearby links and vice versa. We show that Dirac synchronization on a fully connected network is explosive with a hysteresis loop characterized by a discontinuous forward transition and a continuous backward transition. The analytical investigation of the phase diagram provides a theoretical understanding of this topological explosive synchronization. The model also displays an exotic coherent synchronized phase, also called rhythmic phase, characterized by non-stationary order parameters which can shed light on topological mechanisms for the emergence of brain rhythms.Alan Turing Institute and from Royal Society IEC \NSFC\191147. J.J.TConsejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_00173Project of I+D+i Ref. PID2020-113681GB-I00, financed by MICIN/AEI/10.13039/ 501100011033 and FEDER “A way to make Europe"QMUL Research-I

Dirac synchronization is rhythmic and explosive

G.B. acknowledges funding from the Alan Turing Institute and from Royal Society IEC \NSFC\191147. J.J.T. acknowledges financial support from the Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_00173. This work is also part of the Project of I+D+i Ref. PID2020-113681GB-I00, financed by MICIN/AEI/10.13039/ 501100011033 and FEDER “A way to make Europe". This research utilized Queen Mary’s Apocrita HPC facility, supported by QMUL Research-IT. https://doi.org/10.5281/zenodo. 438045.Topological signals defined on nodes, links and higher dimensional simplices define the dynamical state of a network or of a simplicial complex. As such, topological signals are attracting increasing attention in network theory, dynamical systems, signal processing and machine learning. Topological signals defined on the nodes are typically studied in network dynamics, while topological signals defined on links are much less explored. Here we investigate Dirac synchronization, describing locally coupled topological signals defined on the nodes and on the links of a network, and treated using the topological Dirac operator. The dynamics of signals defined on the nodes is affected by a phase lag depending on the dynamical state of nearby links and vice versa. We show that Dirac synchronization on a fully connected network is explosive with a hysteresis loop characterized by a discontinuous forward transition and a continuous backward transition. The analytical investigation of the phase diagram provides a theoretical understanding of this topological explosive synchronization. The model also displays an exotic coherent synchronized phase, also called rhythmic phase, characterized by non-stationary order parameters which can shed light on topological mechanisms for the emergence of brain rhythms.Alan Turing Institute and from Royal Society IEC \NSFC\191147. J.J.TConsejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_00173Project of I+D+i Ref. PID2020-113681GB-I00, financed by MICIN/AEI/10.13039/ 501100011033 and FEDER “A way to make Europe"QMUL Research-I

Geometry, Topology and Simplicial Synchronization

Simplicial synchronization reveals the role that topology and geometry have in determining the dynamical properties of simplicial complexes. Simplicial network geometry and topology are naturally encoded in the spectral properties of the graph Laplacian and of the higher-order Laplacians of simplicial complexes. Here we show how the geometry of simplicial complexes induces spectral dimensions of the simplicial complex Laplacians that are responsible for changing the phase diagram of the Kuramoto model. In particular, simplicial complexes displaying a non-trivial simplicial network geometry cannot sustain a synchronized state in the infinite network limit if their spectral dimension is smaller or equal to four. This theoretical result is here verified on the Network Geometry with Flavor simplicial complex generative model displaying emergent hyperbolic geometry. On its turn simplicial topology is shown to determine the dynamical properties of the higher- order Kuramoto model. The higher-order Kuramoto model describes synchronization of topological signals, i.e. phases not only associated to the nodes of a simplicial complexes but associated also to higher-order simplices, including links, triangles and so on

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality

EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness

We here study a network of synaptic relations mingling excitatory and inhibitory neuron nodes that displays oscillations quite similar to electroencephalogram (EEG) brain waves, and identify abrupt variations brought about by swift synaptic mediations. We thus conclude that corresponding changes in EEG series surely come from the slowdown of the activity in neuron populations due to synaptic restrictions. The latter happens to generate an imbalance between excitation and inhibition causing a quick explosive increase of excitatory activity, which turns out to be a (first-order) transition among dynamic mental phases. Moreover, near this phase transition, our model system exhibits waves with a strong component in the so-called delta-theta domain that coexist with fast oscillations. These findings provide a simple explanation for the observed delta-gamma and theta-gamma modulation in actual brains, and open a serious and versatile path to understand deeply large amounts of apparently erratic, easily accessible brain data.Spanish Ministry of Science and TechnologyAgencia Española de Investigación (AEI)FEDER - FIS2017-84256-PConsejería de Conocimiento, Investigación Universidad, Junta de Andalucía and European Regional Development Funds, Spain - SOMM17/6105/UGR Y A-FQM-175-UGR18Consejería de Transformación Económica, Industria, Conocimiento y Universidades, Junta de Andalucía and European Regional Development Funds, Ref. P20_0017

Física Estadística de procesos marcovianos : estudio de redes de neuronas y sistemas afines

En esta memoria se presenta un modelo cinético de red de neuronas en el que la intensidad de los acoplamientos sinápticos varia con el tiempo en una escala del orden p(1-p)-1 comparada con la escala en la que varían las neuronas. Describimos algunos resultados exactos y de campo medio para p--- 0. Entre estos incluimos, por ejemplo, el modelo de hopfield con fluctuaciones aleatorias de las sinapsis, de forma que las neuronas se acoplan entre si, en promedio, de acuerdo a una regla de aprendizaje tipo hebb. Las consecuencias de tales fluctuaciones se analizan con detalle para diferentes elecciones de la probabilidad de transición elemental y de la distribución de fluctuaciones, incluyendo el caso de sinapsis asimétricas. Se presenta también un modelo reticular de sistema magnético desordenado, que incluye difusión rápida y aleatoria de impurezas. Esta se modela mediante una competición de dinámicas que lleva al sistema a una situación fuera del equilibrio.Tesis Univ. Granada. Departamento de Física Modern