论文标题
对三联节律神经系统中控制信号的敏感性:通过无限局部时序响应曲线进行比较机械分析
Sensitivity to control signals in triphasic rhythmic neural systems: a comparative mechanistic analysis via infinitesimal local timing response curves
论文作者
论文摘要
具有不同耦合属性和单个单位动力学的模型神经网络可能会产生相似的活动模式。但是,这些相似的模式可能对参数变化的响应方式有所不同,特别是对代表控制信号的输入的调整。在这项工作中,我们分析了三种模型神经网络中的局部输入所产生的响应,这些响应在文献中已被识别为产生强大的三相节奏的能力:耦合快速慢振荡器,近乎骨斜斜振荡器和阈值线性网络网络。 Triphasic节奏,每个阶段都由相应的神经元亚组的激活延长激活,然后快速过渡到另一个阶段,代表了在一系列对生存至关重要的中心模式发生器中观察到的基本活性模式,包括呼吸,运动和喂养。为了进行分析,我们扩展了最近开发的局部时序响应曲线(LTRC),这使我们能够表征由于扰动引起的时序效应,并通过模型特定的动态系统分析来补充LTRC方法。有趣的是,我们观察到在不同的模型类别中相似的扰动的不同影响。因此,这项工作提供了一个分析框架,用于研究非线性动力学系统中振荡的控制,并可能有助于指导模型的选择,以研究未来研究表现出三重节奏节奏活动的系统。
Similar activity patterns may arise from model neural networks with distinct coupling properties and individual unit dynamics. These similar patterns may, however, respond differently to parameter variations and, specifically, to tuning of inputs that represent control signals. In this work, we analyze the responses resulting from modulation of a localized input in each of three classes of model neural networks that have been recognized in the literature for their capacity to produce robust three-phase rhythms: coupled fast-slow oscillators, near-heteroclinic oscillators, and threshold-linear networks. Triphasic rhythms, in which each phase consists of a prolonged activation of a corresponding subgroup of neurons followed by a fast transition to another phase, represent a fundamental activity pattern observed across a range of central pattern generators underlying behaviors critical to survival, including respiration, locomotion, and feeding. To perform our analysis, we extend the recently developed local timing response curve (lTRC), which allows us to characterize the timing effects due to perturbations, and we complement our lTRC approach with model-specific dynamical systems analysis. Interestingly, we observe disparate effects of similar perturbations across distinct model classes. Thus, this work provides an analytical framework for studying control of oscillations in nonlinear dynamical systems, and may help guide model selection in future efforts to study systems exhibiting triphasic rhythmic activity.