Cognitive Science Program, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
Cognitive Science Program, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA.
Philos Trans R Soc Lond B Biol Sci. 2018 Sep 10;373(1758):20170374. doi: 10.1098/rstb.2017.0374.
With 302 neurons and a near-complete reconstruction of the neural and muscle anatomy at the cellular level, is an ideal candidate organism to study the neuromechanical basis of behaviour. Yet despite the breadth of knowledge about the neurobiology, anatomy and physics of , there are still a number of unanswered questions about one of its most basic and fundamental behaviours: forward locomotion. How the rhythmic pattern is generated and propagated along the body is not yet well understood. We report on the development and analysis of a model of forward locomotion that integrates the neuroanatomy, neurophysiology and body mechanics of the worm. Our model is motivated by experimental analysis of the structure of the ventral cord circuitry and the effect of local body curvature on nearby motoneurons. We developed a neuroanatomically grounded model of the head motoneuron circuit and the ventral nerve cord circuit. We integrated the neural model with an existing biomechanical model of the worm's body, with updated musculature and stretch receptors. Unknown parameters were evolved using an evolutionary algorithm to match the speed of the worm on agar. We performed 100 evolutionary runs and consistently found electrophysiological configurations that reproduced realistic control of forward movement. The ensemble of successful solutions reproduced key experimental observations that they were not designed to fit, including the wavelength and frequency of the propagating wave. Analysis of the ensemble revealed that head motoneurons SMD and RMD are sufficient to drive dorsoventral undulations in the head and neck and that short-range posteriorly directed proprioceptive feedback is sufficient to propagate the wave along the rest of the body.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling at cellular resolution'.
秀丽隐杆线虫拥有 302 个神经元，在细胞水平上近乎完整地重建了其神经网络和肌肉解剖结构，是研究行为的神经力学基础的理想候选生物。尽管人们对秀丽隐杆线虫的神经生物学、解剖学和物理学有了广泛的了解，但关于其最基本和基本的行为之一——向前运动，仍有一些未解决的问题。节奏模式如何沿着身体生成和传播，目前还不太清楚。我们报告了一个向前运动模型的开发和分析，该模型整合了线虫的神经解剖、神经生理学和身体力学。我们的模型是受对腹索电路结构的实验分析以及局部身体曲率对附近运动神经元的影响的启发。我们开发了一个基于神经解剖学的头部运动神经元回路和腹神经索回路模型。我们将神经模型与线虫身体的现有生物力学模型集成在一起，更新了肌肉和拉伸感受器。使用进化算法来匹配在琼脂上的线虫速度，我们对未知参数进行了进化。我们进行了 100 次进化运行，始终找到了可以复制真实控制向前运动的电生理配置。成功解决方案的集合再现了关键的实验观察结果，这些结果并不是为了适应它们而设计的，包括传播波的波长和频率。对集合的分析表明，头部运动神经元 SMD 和 RMD 足以驱动头部和颈部的背腹波动，并且短程向后的本体感受反馈足以沿身体的其余部分传播波。本文是“从连接组到行为：以细胞分辨率建模”讨论会议的一部分。
