DEFM - Artículoshttps://hdl.handle.net/10481/310192024-03-29T01:55:57Z2024-03-29T01:55:57ZSpectral signatures of symmetry-breaking dynamical phase transitionsHurtado Gutiérrez, RubénHurtado Fernández, Pablo IgnacioPérez Espigares, Carloshttps://hdl.handle.net/10481/856662023-11-14T13:06:47ZSpectral signatures of symmetry-breaking dynamical phase transitions
Hurtado Gutiérrez, Rubén; Hurtado Fernández, Pablo Ignacio; Pérez Espigares, Carlos
The research leading to these results has re-
ceived funding from the fellowship FPU17/02191
and from the Projects of I+D+i Ref. PID2020-
113681GB-I00, Ref. PID2021-128970OA-I00 and
Ref. FIS2017-84256-P, Ref. A-FQM-175-UGR18,
Ref. P20_00173 and Ref. A-FQM-644-UGR20 fi-
nanced by the Spanish Ministerio de Ciencia, In-
novación y Universidades and European Regional
Development Fund, Junta de Andalucía-Consejería
de Economía y Conocimiento 2014-2020. We are
also grateful for the the computing resources and
related technical support provided by PROTEUS,
the supercomputing center of Institute Carlos I in
Granada, Spain.
Less is different: Why sparse networks with inhibition differ from complete graphsMereles Menesse, Gustavo EduardoKinouchi, Osamehttps://hdl.handle.net/10481/852752023-10-26T11:10:51ZLess is different: Why sparse networks with inhibition differ from complete graphs
Mereles Menesse, Gustavo Eduardo; Kinouchi, Osame
In neuronal systems, inhibition contributes to stabilizing dynamics and regulating pattern formation. Through developing mean-field theories of neuronal models, using complete graph networks, inhibition is commonly viewed as one "control parameter" of the system, promoting an absorbing phase transition. Here, we show that, for low connectivity sparse networks, inhibition weight is not a control parameter of the absorbing transition. We present analytical and simulation results using generic stochastic integrate-and-fire neurons that, under specific restrictions, become other simpler stochastic neuron models common in literature, which allows us to show that our results are valid for those models as well. We also give a simple explanation about why the inhibition role depends on topology, even when the topology has a dimensionality greater than the critical one. The absorbing transition independence of the inhibitory weight may be an important feature of a sparse network, as it will allow the network to maintain a near-critical regime, self-tuning average excitation, but at the same time have the freedom to adjust inhibitory weights for computation, learning, and memory, exploiting the benefits of criticality.
G.M. would like to thank the Programa Nacional de Becas de Postgrados en el Exterior "Don Carlos Antonio Lopez" (BECAL), Paraguay for the financial support to his doctoral studies in the Physics and Mathematics Program of the University of Granada and CAPES for financial support during the Master's studies in the FAMB program of the FFCLRPUSP. O.K. acknowledges support from CNAIPS-USP and CNPq, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico.
Quasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamicsBarrios Morales, Guillermo GabrielDi Santo, SerenaMuñoz Martínez, Miguel Ángelhttps://hdl.handle.net/10481/849092023-10-09T22:11:11ZQuasiuniversal scaling in mouse-brain neuronal activity stems from edge-of-instability critical dynamics
Barrios Morales, Guillermo Gabriel; Di Santo, Serena; Muñoz Martínez, Miguel Ángel
The brain is in a state of perpetual reverberant neural activity, even in the absence of specific tasks or stimuli. Shedding light on the origin and functional significance of such a dynamical state is essential to understanding how the brain transmits, processes, and stores information. An inspiring, albeit controversial, conjecture proposes that some statistical characteristics of empirically observed neuronal activity can be understood by assuming that brain networks operate in a dynamical regime with features, including the emergence of scale invariance, resembling those seen typically near phase transitions. Here, we present a data-driven analysis based on simultaneous high-throughput recordings of the activity of thousands of individual neurons in various regions of the mouse brain. To analyze these data, we construct a unified theoretical framework that synergistically combines a phenomenological renormalization group approach and techniques that infer the general dynamical state of a neural population, while designing complementary tools. This strategy allows us to uncover strong signatures of scale invariance that are “quasiuniversal” across brain regions and experiments, revealing that all the analyzed areas operate, to a greater or lesser extent, near the edge of instability
A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive PanelsRuiz-Cabello Núñez, Miguel DavidMartín Valverde, Antonio JesúsRubio Bretones, Amelia ConsueloGascón Bravo, AlbertoGonzález García, Salvadorhttps://hdl.handle.net/10481/845312023-09-20T12:34:06ZA Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
Ruiz-Cabello Núñez, Miguel David; Martín Valverde, Antonio Jesús; Rubio Bretones, Amelia Consuelo; Gascón Bravo, Alberto; González García, Salvador
Efficiently modeling thin features using the finite-difference time-domain (FDTD) method involves a considerable reduction in the spatial mesh size. However, in real-world scenarios, such reductions can lead to unaffordable memory and CPU requirements. In this manuscript, we present two stable and efficient techniques in FDTD to handle narrow apertures on conductive thin panels. One technique employs conformal methods, while the other utilizes subgridding methods. We validate their performance compared to the classical Gilbert-Holland model and present experimental results in reverberation environments to shed light on these models' actual confidence margins in real electromagnetic compatibility (EMC) scenarios.
Funded by the European Union under GA no 101101961-HECATE. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or Clean Aviation Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them. The project is supported by the Clean Aviation Joint Undertaking and its Members and, by Spanish Ministry of Science and Innovation (MICINN) under projects eSAFE-UAV PID2019-106120RB-C32; PID2019-106120RB-C33.
Depinning in the quenched Kardar-Parisi-Zhang class. I. Mappings, simulations, and algorithmMukerjee, GauthierBonachela, Juan A.Muñoz Martínez, Miguel ÁngelJörg Wiese, Kayhttps://hdl.handle.net/10481/840432023-10-09T22:23:01ZDepinning in the quenched Kardar-Parisi-Zhang class. I. Mappings, simulations, and algorithm
Mukerjee, Gauthier; Bonachela, Juan A.; Muñoz Martínez, Miguel Ángel; Jörg Wiese, Kay
Depinning of elastic systems advancing on disordered media can usually be described by the quenched
Edwards-Wilkinson equation (qEW). However, additional ingredients such as anharmonicity and forces that
cannot be derived from a potential energy may generate a different scaling behavior at depinning. The most
experimentally relevant is the Kardar-Parisi-Zhang (KPZ) term, proportional to the square of the slope at each
site, which drives the critical behavior into the so-called quenched KPZ (qKPZ) universality class.We study this
universality class both numerically and analytically: by using exact mappings we show that at least for d = 1, 2
this class encompasses not only the qKPZ equation itself, but also anharmonic depinning and a well-known class
of cellular automata introduced by Tang and Leschhorn.We develop scaling arguments for all critical exponents,
including size and duration of avalanches. The scale is set by the confining potential strength m2. This allows
us to estimate numerically these exponents as well as the m-dependent effective force correlator (w), and its
correlation length ρ := (0)/|
(0)|. Finally, we present an algorithm to numerically estimate the effective
(m-dependent) elasticity c, and the effective KPZ nonlinearity λ. This allows us to define a dimensionless
universal KPZ amplitude A := ρλ/c, which takes the value A = 1.10(2) in all systems considered in d = 1.
This proves that qKPZ is the effective field theory for all these models. Our work paves the way for a deeper
understanding of depinning in the qKPZ class, and in particular, for the construction of a field theory that we
describe in a companion paper.