Ciencias,UNAM

ANALYSIS OF AN AUTONOMOUS PHASE MODEL FOR NEURONAL PARABOLIC BURSTING

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dc.contributor.author RINZEL, J
dc.contributor.author CARRILLO, H
dc.contributor.author Baer, SM
dc.date.accessioned 2011-01-22T10:28:32Z
dc.date.available 2011-01-22T10:28:32Z
dc.date.issued 1995
dc.identifier.issn 0303-6812
dc.identifier.uri http://hdl.handle.net/11154/3052
dc.description.abstract An understanding of the nonlinear dynamics of bursting is fundamental in unraveling structure-function relations in nerve and secretory tissue. Bursting is characterized by alternations between phases of rapid spiking and slowly varying potential. A simple phase model is developed to study endogenous parabolic bursting, a class of burst activity observed experimentally in excitable membrane. The phase model is motivated by Rinzel and Lee's dissection of a model for neuronal parabolic bursting (J. Math. Biol. 25, 653-675 (1987)). Rapid spiking is represented canonically by a one-variable phase equation that is coupled bi-directionally to a two-variable slow system, The model is analyzed in the slow-variable phase plane? using quasi steady-state assumptions and formal averaging, We derive a reduced system to explore where the full model exhibits bursting, steady-states, continuous and modulated spiking. The relative speed of activation and inactivation of the slow variables strongly influences the burst pattern as well as other dynamics. We find conditions of the bistability of solutions between continuous spiking and bursting. Although the phase model is simple, we demonstrate that it captures many dynamical features of more complex biophysical models. en_US
dc.language.iso en en_US
dc.title ANALYSIS OF AN AUTONOMOUS PHASE MODEL FOR NEURONAL PARABOLIC BURSTING en_US
dc.type Article en_US
dc.identifier.idprometeo 3217
dc.source.novolpages 33(3):309-333
dc.subject.wos Biology
dc.subject.wos Mathematical & Computational Biology
dc.description.index WoS: SCI, SSCI o AHCI
dc.subject.keywords EXCITABLE MEMBRANE
dc.subject.keywords BURSTING OSCILLATIONS
dc.subject.keywords NEURONAL MODELING
dc.relation.journal Journal of Mathematical Biology

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