瑞士洛桑联邦理工学院Pavan Ramdya等研究人员合作发现，NeuroMechFly v2可模拟成年果蝇的具身传感运动控制。该项研究成果于2024年11月12日在线发表在《自然—方法学》杂志上。

研究人员表明，NeuroMechFly v2扩展了果蝇神经机械建模的范围，通过支持视觉、嗅觉、上行运动反馈和能够使用腿部附着力导航的复杂地形，使得模拟更加全面。研究人员通过构建生物启发的控制器来展示了其能力，这些控制器利用上行反馈进行路径整合和头部稳定。在添加视觉和嗅觉后，研究人员使用强化学习训练控制器执行多模态导航任务。

最后，研究人员展示了更具生物现实性的建模，包括复杂的气味羽流导航和使用连通体约束的视觉网络进行果蝇追随。NeuroMechFly可以加速神经系统解释模型的发现，并帮助开发基于机器学习的控制器，用于自主人工代理和机器人系统。

据介绍，发现动物行为控制的原理，需要实验与神经机械模型之间紧密的对话。这些模型主要用于研究运动控制，较少强调大脑与运动系统在层级传感-运动控制中的协同作用。

附：英文原文

Title: NeuroMechFly v2: simulating embodied sensorimotor control in adult Drosophila

Author: Wang-Chen, Sibo, Stimpfling, Victor Alfred, Lam, Thomas Ka Chung, zdil, Pembe Gizem, Genoud, Louise, Hurtak, Femke, Ramdya, Pavan

Issue&Volume: 2024-11-12

Abstract: Discovering principles underlying the control of animal behavior requires a tight dialogue between experiments and neuromechanical models. Such models have primarily been used to investigate motor control with less emphasis on how the brain and motor systems work together during hierarchical sensorimotor control. NeuroMechFly v2 expands Drosophila neuromechanical modeling by enabling vision, olfaction, ascending motor feedback and complex terrains that can be navigated using leg adhesion. We illustrate its capabilities by constructing biologically inspired controllers that use ascending feedback to perform path integration and head stabilization. After adding vision and olfaction, we train a controller using reinforcement learning to perform a multimodal navigation task. Finally, we illustrate more bio-realistic modeling involving complex odor plume navigation, and fly–fly following using a connectome-constrained visual network. NeuroMechFly can be used to accelerate the discovery of explanatory models of the nervous system and to develop machine learning-based controllers for autonomous artificial agents and robots.

DOI: 10.1038/s41592-024-02497-y

Source: https://www.nature.com/articles/s41592-024-02497-y