瑞士洛桑联邦理工学院Pavan Ramdya课题组开发出一种成年黑腹果蝇的神经力学模型。这一研究成果于2022年5月11日在线发表在国际学术期刊《自然—方法学》上。

研究人员提出了NeuroMechFly，一个广泛研究的生物体（黑腹果蝇）的数据驱动的模型。NeuroMechFly结合了四个独立的计算模块：一个基于物理学的模拟环境、一个生物力学外骨骼、肌肉模型和神经网络控制器。为了实现用例，研究人员首先从行走和梳理期间的真实三维运动学测量中定义腿部的最小自由度。然后，研究人员展示了通过在模拟器中重放这些行为，从而可以预测原本无法测量的扭矩和接触力。最后，研究人员利用NeuroMechFly的全部神经机械能力来发现神经网络和肌肉参数，进而驱动为速度和稳定性而优化的运动步态。因此，NeuroMechFly可以增加人们对行为如何从复杂的神经机械系统和其物理环境之间的相互作用中产生的理解。

据介绍，动物行为产生于神经网络动力学、肌肉骨骼特性和物理环境之间的互动。获取和理解这些元素之间的相互作用需要开发综合的、形态学上真实的神经机械模拟。

附：英文原文

Title: NeuroMechFly, a neuromechanical model of adult Drosophila melanogaster

Author: Lobato-Rios, Victor, Ramalingasetty, Shravan Tata, zdil, Pembe Gizem, Arreguit, Jonathan, Ijspeert, Auke Jan, Ramdya, Pavan

Issue&Volume: 2022-05-11

Abstract: Animal behavior emerges from an interaction between neural network dynamics, musculoskeletal properties and the physical environment. Accessing and understanding the interplay between these elements requires the development of integrative and morphologically realistic neuromechanical simulations. Here we present NeuroMechFly, a data-driven model of the widely studied organism, Drosophila melanogaster. NeuroMechFly combines four independent computational modules: a physics-based simulation environment, a biomechanical exoskeleton, muscle models and neural network controllers. To enable use cases, we first define the minimum degrees of freedom of the leg from real three-dimensional kinematic measurements during walking and grooming. Then, we show how, by replaying these behaviors in the simulator, one can predict otherwise unmeasured torques and contact forces. Finally, we leverage NeuroMechFly’s full neuromechanical capacity to discover neural networks and muscle parameters that drive locomotor gaits optimized for speed and stability. Thus, NeuroMechFly can increase our understanding of how behaviors emerge from interactions between complex neuromechanical systems and their physical surroundings.

DOI: 10.1038/s41592-022-01466-7

Source: https://www.nature.com/articles/s41592-022-01466-7