Event Abstract Back to Event Poincare return mapping for models of elliptic neurons Jeremy Wojcik1* and Andrey Shilnikov1 1 Georgia State University, Department of Mathematics and Statistics and the Neuroscience Institute, United States We propose a novel computer assisted method for the effective construction and accurate examination of families of Poincaré return mappings for voltage maxima in models of elliptic bursting neurons. Such busters are adequately described by dynamical systems with two characteristic time scales: slow and fast. A feature of a slow-fast dynamical model is that its solutions stay close to the so-called slow motion manifolds, comprised of equilibria and limit cycles of its fast subsystem. In the context of neurodynamics they are respectively called quiescent and tonic spiking equilibria. We reveal the topology of the manifolds in the phase space of an elliptic neuron model to define the Poincaré mappings for oscillatory dynamics of the fast membrane potential. The algorithms discussed allow us to create a full family of Poincare mappings to get insight into driving forces on the dynamics of a model in question over an entire parameter range. Of special interests are these mappings for nonlocal bifurcations at transitions between states of the model. This includes mechanisms of transitions between tonic spiking and bursting, between quiescence and tonic spiking, bifurcations of bursting, the emergence of various mixed mode oscillations, and their interactions with bursting and quiescence etc. The examination of nonlocal bifurcation at these transitions is accomplished through reduction of the multidimensional model to a one-dimensional voltage next amplitude mapping: T: Vn ?Vn+1. Such mappings allow us to identify and study bifurcations and complex dynamics of the model at such transitions. We note that the reduction to next amplitude mappings for maximal values of oscillatory voltage alone is highly advantageous as the behavior of any neuron model are manifest through the evolution of accumulative voltage dynamics which can only be accessed in experimental studies in most cases. We examine the mathematical Fitzhugh-Nagumo-Rinzel model, a bursting modification of the classical Hodgkin-Huxley model, as well as the Terman-Rubin model for the external segment of globus pallidus. We begin by creation of the respective slow motion manifolds through parameter continuation techniques. Next we are then able to extrapolate a set of maxima's, for each limit cycle of the manifold, to be used as initial conditions for the solution set for a given parameter. We then find the next maxima for the set of initial conditions creating a next amplitude map. Varying the parameter yields a family of next amplitude maps that allow for the identification of periodic and chaotic attractors, as well system repellers and homoclinic solutions that globally organize the dynamics of the model. We are then able to predict and/or identify the mechanisms for state transitions in the models, before the actual transition. Conference: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences, Atlanta , United States, 5 Mar - 7 Mar, 2010. Presentation Type: Poster Presentation Topic: Posters Citation: Wojcik J and Shilnikov A (2010). Poincare return mapping for models of elliptic neurons. Front. Neurosci. Conference Abstract: 2010 South East Nerve Net (SENN) and Georgia/South Carolina Neuroscience Consortium (GASCNC) conferences. doi: 10.3389/conf.fnins.2010.04.00088 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 18 Mar 2010; Published Online: 18 Mar 2010. * Correspondence: Jeremy Wojcik, Georgia State University, Department of Mathematics and Statistics and the Neuroscience Institute, Atlanta, United States, jwojcik1@gsu.edu Login Required This action requires you to be registered with Frontiers and logged in. 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