title: Improving noise resistance of intrinsic rhythms in a square-wave burster model.
abstract: The square-wave burster (Wang and Rinzel, 2003) is a class of autonomous bursting cells that share a bifurcation structure. It is known that this class of cells is involved in the generation of various life-supporting rhythms. In our research to realize an electronic circuit that mimics the rhythm generating mechanism in the square-wave burster, our circuit experimentally exhibited severe fluctuations in its rhythmic activity. We have found a noise-sensitive region in the phase portrait of the ideal model and have proposed modifications of the model that can reduce this fluctuation. A possible modification to ionic-conductance neuron models (Kohno and Aihara, 2011) was inspired by them. This modification, however, cannot be applied to a group of square-wave bursters, including the Butera-Rinzel-Smith model (Butera et al., 1999; Del Negro et al., 2001), which is a model of the pre-Bötzinger complex bursting neuron that plays a crucial role in the generation of respiration rhythms, because this modification premises that the slow dynamics originates from an activation gate variable of a hyperpolarizing ionic current. However, in some square-wave bursters, they are controlled by an inactivation gate variable of a depolarizing ionic current. In this study, we proposed a similar modification with a completely different mechanism that can be applied to this group of square-wave bursters. In the presence of noises, the modified Butera-Rinzel-Smith model can generate rhythmic activity that is more stable and similar to biological observations than the original model. The mechanisms underlying this modification are explained with noisy bifurcation diagrams.
authors.affiliation: Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan. kohno@sat.t.u-tokyo.ac.jp
authors.forename: Takashi
authors.initials: T
authors.lastname: Kohno
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006706:Homeostasis / Q000502:physiology*; D017136:Ion Transport / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010507:Periodicity