Fast-Slow Bursters in the Unfolding of a High Codimension Singularity and the Ultra-slow Transitions of Classes.

IF 2.3 4区 医学 Q1 Neuroscience
Journal of Mathematical Neuroscience Pub Date : 2017-12-01 Epub Date: 2017-07-25 DOI:10.1186/s13408-017-0050-8
Maria Luisa Saggio, Andreas Spiegler, Christophe Bernard, Viktor K Jirsa
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引用次数: 60

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

高协维奇点展开中的快慢爆发子和类的超慢跃迁。
爆发是一种存在于各种物理和生物系统中的现象。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,我们提出了一个模型,适当调整,可以显示几种类型的爆发行为。该模型包含两个作用于不同时间尺度的子系统。对于快速子系统,我们采用高协维奇点的平面展开。在它的分岔图中,我们找到了在爆发所需的正确分岔序列之下的路径。缓慢的子系统引导快速的子系统沿着这些路径来回移动,从而导致爆炸行为。对于具有平面快速子系统的系统,该模型能够产生几乎所有类型的爆炸。类之间的转换可以通过模型参数的超慢调制来实现。对参数空间的详细探索可以预测可能的过渡。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的框架。
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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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