Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland.
Biorobotics Laboratory, EPFL, Lausanne, Switzerland.
Nat Methods. 2022 May;19(5):620-627. doi: 10.1038/s41592-022-01466-7. Epub 2022 May 11.
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.
动物行为源自神经网络动态、肌肉骨骼特性和物理环境之间的相互作用。要了解这些元素之间的相互作用，需要开发具有综合性和形态逼真的神经机械模拟。在这里，我们介绍了NeuroMechFly，这是一个针对广泛研究的生物——黑腹果蝇的基于数据的模型。NeuroMechFly 结合了四个独立的计算模块：基于物理的模拟环境、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，我们首先根据行走和梳理过程中的真实三维运动学测量结果，定义了腿的最小自由度。然后，我们展示了如何通过在模拟器中重放这些行为，预测否则无法测量的扭矩和接触力。最后，我们利用 NeuroMechFly 的完整神经机械能力来发现神经网络和肌肉参数，这些参数可以驱动优化速度和稳定性的运动步态。因此，NeuroMechFly 可以帮助我们更好地理解行为是如何从复杂的神经机械系统及其物理环境之间的相互作用中产生的。
