Saggio Maria Luisa, Spiegler Andreas, Bernard Christophe, Jirsa Viktor K
INS, Institut de Neurosciences des Systèmes, Inserm, Aix Marseille Univ, Marseille, France.
J Math Neurosci. 2017 Dec;7(1):7. doi: 10.1186/s13408-017-0050-8. Epub 2017 Jul 25.
Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a model that, appropriately tuned, can display several types of bursting behaviors. The model contains two subsystems acting at different time scales. For the fast subsystem we use the planar unfolding of a high codimension singularity. In its bifurcation diagram, we locate paths that underlie the right sequence of bifurcations necessary for bursting. The slow subsystem steers the fast one back and forth along these paths leading to bursting behavior. The model is able to produce almost all the classes of bursting predicted for systems with a planar fast subsystem. Transitions between classes can be obtained through an ultra-slow modulation of the model's parameters. A detailed exploration of the parameter space allows predicting possible transitions. This provides a single framework to understand the coexistence of diverse bursting patterns in physical and biological systems or in models.
爆发是在各种物理和生物系统中发现的一种现象。例如,在神经科学中,爆发被认为在神经系统中信息传递的方式中起着关键作用。在这项工作中,我们提出了一个经过适当调整就能展示几种类型爆发行为的模型。该模型包含两个在不同时间尺度上起作用的子系统。对于快速子系统,我们使用高余维奇点的平面展开。在其分岔图中,我们找到了构成爆发所需正确分岔序列基础的路径。慢速子系统沿着这些路径来回引导快速子系统,从而导致爆发行为。该模型能够产生几乎所有为具有平面快速子系统的系统预测的爆发类别。类别之间的转变可以通过对模型参数的超慢调制来实现。对参数空间的详细探索有助于预测可能的转变。这为理解物理和生物系统或模型中不同爆发模式的共存提供了一个单一框架。