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果蝇视觉飞行速度控制的行为系统辨识。

Behavioural system identification of visual flight speed control in Drosophila melanogaster.

机构信息

Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

出版信息

J R Soc Interface. 2011 Feb 6;8(55):171-85. doi: 10.1098/rsif.2010.0225. Epub 2010 Jun 4.

Abstract

Behavioural control in many animals involves complex mechanisms with intricate sensory-motor feedback loops. Modelling allows functional aspects to be captured without relying on a description of the underlying complex, and often unknown, mechanisms. A wide range of engineering techniques are available for modelling, but their ability to describe time-continuous processes is rarely exploited to describe sensory-motor control mechanisms in biological systems. We performed a system identification of visual flight speed control in the fruitfly Drosophila, based on an extensive dataset of open-loop responses previously measured under free flight conditions. We identified a second-order under-damped control model with just six free parameters that well describes both the transient and steady-state characteristics of the open-loop data. We then used the identified control model to predict flight speed responses after a visual perturbation under closed-loop conditions and validated the model with behavioural measurements performed in free-flying flies under the same closed-loop conditions. Our system identification of the fruitfly's flight speed response uncovers the high-level control strategy of a fundamental flight control reflex without depending on assumptions about the underlying physiological mechanisms. The results are relevant for future investigations of the underlying neuromotor processing mechanisms, as well as for the design of biomimetic robots, such as micro-air vehicles.

摘要

许多动物的行为控制涉及具有复杂感觉-运动反馈回路的复杂机制。建模允许捕获功能方面,而无需依赖于对底层复杂且通常未知的机制的描述。有各种各样的工程技术可用于建模,但它们描述时变过程的能力很少被用于描述生物系统中的感觉-运动控制机制。我们基于先前在自由飞行条件下测量的大量开环响应数据集,对果蝇的视觉飞行速度控制进行了系统识别。我们确定了一个具有六个自由参数的二阶欠阻尼控制模型,该模型很好地描述了开环数据的瞬态和稳态特性。然后,我们使用所识别的控制模型在闭环条件下预测视觉干扰后的飞行速度响应,并使用在相同闭环条件下自由飞行的果蝇进行的行为测量对模型进行验证。我们对果蝇飞行速度响应的系统识别揭示了基本飞行控制反射的高级控制策略,而无需依赖于对潜在生理机制的假设。这些结果对于未来对潜在的神经运动处理机制的研究以及对仿生机器人(如微型飞行器)的设计都具有重要意义。

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