Szczecinski Nicholas S, Martin Joshua P, Bertsch David J, Ritzmann Roy E, Quinn Roger D
Case Western Reserve University, 10900 Euclid Ave., Cleveland, Ohio, USA 44106.
Bioinspir Biomim. 2015 Nov 18;10(6):065005. doi: 10.1088/1748-3190/10/6/065005.
Praying mantises hunt by standing on their meso- and metathoracic legs and using them to rotate and translate (together, 'pivot') their bodies toward prey. We have developed a neuromechanical software model of the praying mantis Tenodera sinensis to use as a platform for testing postural controllers that the animal may use while hunting. Previous results showed that a feedforward model was insufficient for capturing the diversity of posture observed in the animal (Szczecinski et al 2014 Biomimetic and Biohybrid Syst. 3 296-307). Therefore we have expanded upon this model to make a flexible controller with feedback that more closely mimics the animal. The controller actuates 24 joints in the legs of a dynamical model to orient the head and translate the thorax toward prey. It is controlled by a simulation of nonspiking neurons assembled as a highly simplified version of networks that may exist in the mantid central complex and thoracic ganglia. Because of the distributed nature of these networks, we hypothesize that descending commands that orient the mantis toward prey may be simple direction-of-intent signals, which are turned into motor commands by the structure of low-level networks in the thoracic ganglia. We verify this through a series of experiments with the model. It captures the speed and range of mantid pivots as reported in other work (Yamawaki et al 2011 J. Insect Physiol. 57 1010-6). It is capable of pivoting toward prey from a variety of initial postures, as seen in the animal. Finally, we compare the model's joint kinematics during pivots to preliminary 3D kinematics collected from Tenodera.
螳螂通过用中胸和后胸的腿站立，并利用这些腿旋转和移动（一起称为“枢转”）身体来捕食猎物。我们开发了中华大刀螳的神经机械软件模型，将其作为一个平台来测试这种动物在捕食时可能使用的姿势控制器。先前的结果表明，前馈模型不足以捕捉该动物观察到的姿势多样性（Szczecinski等人，2014年，《仿生与生物混合系统》3 296 - 307）。因此，我们在此模型的基础上进行了扩展，制作了一个具有反馈的灵活控制器，使其更接近地模拟该动物。该控制器驱动动态模型腿部的24个关节，以使头部定向并将胸部移向猎物。它由非尖峰神经元的模拟控制，这些神经元组装成高度简化的网络版本，可能存在于螳螂的中央复合体和胸神经节中。由于这些网络的分布式性质，我们假设使螳螂朝向猎物定向的下行命令可能是简单的意图方向信号，这些信号通过胸神经节中低级网络的结构转化为运动命令。我们通过对该模型进行一系列实验来验证这一点。它捕捉到了其他研究中报道的螳螂枢转的速度和范围（Yamawaki等人，2011年，《昆虫生理学杂志》57 1010 - 6）。它能够从各种初始姿势向猎物枢转，就像在动物身上看到的那样。最后，我们将模型在枢转过程中的关节运动学与从中华大刀螳收集的初步3D运动学进行了比较。
