000126830 001__ 126830
000126830 005__ 20250619084224.0
000126830 0247_ $$2doi$$a10.1016/j.chaos.2023.113547
000126830 0248_ $$2sideral$$a134143
000126830 037__ $$aART-2023-134143
000126830 041__ $$aeng
000126830 100__ $$aFaci-Lázaro, Sergio$$uUniversidad de Zaragoza
000126830 245__ $$aDynamical and topological conditions triggering the spontaneous activation of Izhikevich neuronal networks
000126830 260__ $$c2023
000126830 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126830 5203_ $$aUnderstanding the dynamic behavior of neuronal networks in silico is crucial for tackling the analysis of their biological counterparts and making accurate predictions. Of particular importance is determining the structural and dynamical conditions necessary for a neuronal network to activate spontaneously, transitioning from a quiescent ensemble of neurons to a network-wide coherent burst. Drawing from the versatility of the Master Stability Function, we have applied this formalism to a system of coupled neurons described by the Izhikevich model to derive the required conditions for activation. These conditions are expressed as a critical effective coupling
, grounded in both topology and dynamics, above which the neuronal network will activate. For regular spiking neurons, average connectivity and noise play a significant role in their ability to activate. We have tested these conditions against numerical simulations of in silico networks, including both synthetic topologies and a biologically-realistic spatial network, showing that the theoretical conditions are well satisfied. Our findings indicate that neuronal networks readily meet the criteria for spontaneous activation, and that this capacity is weakly dependent on the microscopic details of the network as long as average connectivity and noise are sufficiently strong.
000126830 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-113582GB-I00$$9info:eu-repo/grantAgreement/ES/MCINN/FEDER/PID2019--108842GB-C21$$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL
000126830 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126830 590__ $$a5.3$$b2023
000126830 592__ $$a1.349$$b2023
000126830 591__ $$aPHYSICS, MATHEMATICAL$$b2 / 60 = 0.033$$c2023$$dQ1$$eT1
000126830 593__ $$aApplied Mathematics$$c2023$$dQ1
000126830 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b7 / 135 = 0.052$$c2023$$dQ1$$eT1
000126830 593__ $$aMathematical Physics$$c2023$$dQ1
000126830 591__ $$aPHYSICS, MULTIDISCIPLINARY$$b18 / 112 = 0.161$$c2023$$dQ1$$eT1
000126830 593__ $$aStatistical and Nonlinear Physics$$c2023$$dQ1
000126830 593__ $$aPhysics and Astronomy (miscellaneous)$$c2023$$dQ1
000126830 593__ $$aMathematics (miscellaneous)$$c2023$$dQ1
000126830 594__ $$a13.2$$b2023
000126830 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126830 700__ $$aSoriano, Jordi
000126830 700__ $$0(orcid)0000-0003-0698-6555$$aMazo, Juan José
000126830 700__ $$0(orcid)0000-0001-5204-1937$$aGómez-Gardeñes, Jesús$$uUniversidad de Zaragoza
000126830 7102_ $$12003$$2395$$aUniversidad de Zaragoza$$bDpto. Física Materia Condensa.$$cÁrea Física Materia Condensada
000126830 773__ $$g172 (2023), 113547 [7 pp.]$$pChaos, solitons fractals$$tChaos, Solitons and Fractals$$x0960-0779
000126830 8564_ $$s1971985$$uhttps://zaguan.unizar.es/record/126830/files/texto_completo.pdf$$yVersión publicada
000126830 8564_ $$s2658295$$uhttps://zaguan.unizar.es/record/126830/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126830 909CO $$ooai:zaguan.unizar.es:126830$$particulos$$pdriver
000126830 951__ $$a2025-06-19-08:41:30
000126830 980__ $$aARTICLE