Learning Cycle-Linear Hybrid Automata for Excitable Cells
Learning Cycle-Linear Hybrid Automata for Excitable Cells
复制标题
学习可兴奋细胞的循环线性混合自动机
DOI：
10.1007/978-3-540-71493-4_21
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
S. Smolka
中科院分区：
文献类型：
--
作者：
R. Grosu;S. Mitra;P. Ye;E. Entcheva;I. Ramakrishnan;S. Smolka
我们展示了如何自动学习称为循环线性混合自动机（CLHA）的混合自动机类，以便对可兴奋细胞的行为进行建模。这些细胞的主要目的是放大和传播称为动作电位（AP）的电信号，充当生物体的“生物晶体管”。我们提出的学习算法包括以下三个阶段：（1）训练集中AP的几何分析用于识别每个AP对应的线性混合自动机的模式和切换逻辑。 (2) 对于每种模式，使用改进的 Prony 方法来学习相关线性流的系数。 (3) 再次使用修改后的 Prony 方法来学习在每个周期的基础上调整前两个阶段获得的线性混合自动机的模式动态和切换逻辑的函数。我们的结果表明，学习的 CLHA 能够成功捕获 AP 形态和其他重要的可兴奋细胞特性，例如不应性和恢复性，达到规定的近似误差。我们的方法完全在 MATLAB 中实现，据我们所知，为 EC 提供了迄今为止最准确的近似模型。
We show how to automatically learn the class of Hybrid Automata called Cycle-Linear Hybrid Automata (CLHA) in order to model the behavior of excitable cells. Such cells, whose main purpose is to amplify and propagate an electrical signal known as the action potential (AP), serve as the "biologic transistors" of living organisms. The learning algorithm we propose comprises the following three phases: (1) Geometric analysis of the APs in the training set is used to identify, for each AP, the modes and switching logic of the corresponding Linear Hybrid Automata. (2) For each mode, the modified Prony's method is used to learn the coefficients of the associated linear flows. (3) The modified Prony's method is used again to learn the functions that adjust, on a per-cycle basis, the mode dynamics and switching logic of the Linear Hybrid Automata obtained in the first two phases. Our results show that the learned CLHA is able to successfully capture AP morphology and other important excitable-cell properties, such as refractoriness and restitution, up to a prescribed approximation error. Our approach is fully implemented in MATLAB and, to the best of our knowledge, provides the most accurate approximation model for ECs to date.