000130064 001__ 130064
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000130064 0247_ $$2doi$$a10.1016/j.cnsns.2019.105047
000130064 0248_ $$2sideral$$a114703
000130064 037__ $$aART-2020-114703
000130064 041__ $$aeng
000130064 100__ $$0(orcid)0000-0002-8089-343X$$aBarrio, R.$$uUniversidad de Zaragoza
000130064 245__ $$aNumerical detection of patterns in CPGs: Gait patterns in insect movement
000130064 260__ $$c2020
000130064 5060_ $$aAccess copy available to the general public$$fUnrestricted
000130064 5203_ $$aThe study of the synchronization patterns of small neuron networks that control different biological processes has become a growing discipline. This paper is focused on numerical techniques to detect patterns in Central Pattern Generators (CPGs). We develop two techniques that can be used directly in general CPG models: a lateral phase lag analysis based on a graphic representation of some Poincaré maps, and a quasi-Monte Carlo sweeping with an optimized classification of the different patterns. As test example we consider a CPG of insect movement consisting of six coupled neurons following the model developed by Ghigliazza and Holmes (2004) for motoneurons in cockroaches. Previous studies in literature analyzed reduced models of dimension two obtained using phase resetting curves and averaging theory. This approach introduces a lot of simplifications that do not cover numerous non-symmetric patterns. We present an analysis of the complete model developed by combining the two proposed techniques, showing symmetric and non-symmetric patterns coexisting for different parameter values, and how the dominant patterns evolve to the tripod movement.
000130064 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E22-17R$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/E24-17R$$9info:eu-repo/grantAgreement/ES/MICINN/PGC2018-096026-B-I00$$9info:eu-repo/grantAgreement/ES/MICINN/MTM2016-77642-C2-2-P$$9info:eu-repo/grantAgreement/ES/UZ/CUD2019-CIE-04
000130064 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000130064 590__ $$a4.26$$b2020
000130064 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b11 / 108 = 0.102$$c2020$$dQ1$$eT1
000130064 591__ $$aMATHEMATICS, APPLIED$$b5 / 265 = 0.019$$c2020$$dQ1$$eT1
000130064 591__ $$aPHYSICS, MATHEMATICAL$$b3 / 55 = 0.055$$c2020$$dQ1$$eT1
000130064 591__ $$aPHYSICS, FLUIDS & PLASMAS$$b2 / 34 = 0.059$$c2020$$dQ1$$eT1
000130064 591__ $$aMECHANICS$$b23 / 135 = 0.17$$c2020$$dQ1$$eT1
000130064 592__ $$a1.159$$b2020
000130064 593__ $$aApplied Mathematics$$c2020$$dQ1
000130064 593__ $$aNumerical Analysis$$c2020$$dQ1
000130064 593__ $$aModeling and Simulation$$c2020$$dQ1
000130064 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000130064 700__ $$0(orcid)0000-0002-1184-5901$$aLozano, Á.
000130064 700__ $$0(orcid)0000-0003-3426-105X$$aRodríguez, M.$$uUniversidad de Zaragoza
000130064 700__ $$0(orcid)0000-0002-5701-1670$$aSerrano, S.$$uUniversidad de Zaragoza
000130064 7102_ $$12005$$2595$$aUniversidad de Zaragoza$$bDpto. Matemática Aplicada$$cÁrea Matemática Aplicada
000130064 773__ $$g82 (2020), 105047 [20 pp.]$$pCommun. nonlinear sci. numer. simul.$$tCommunications in Nonlinear Science and Numerical Simulation$$x1007-5704
000130064 8564_ $$s5973842$$uhttps://zaguan.unizar.es/record/130064/files/texto_completo.pdf$$yPostprint
000130064 8564_ $$s1913495$$uhttps://zaguan.unizar.es/record/130064/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
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000130064 951__ $$a2024-01-22-15:24:40
000130064 980__ $$aARTICLE