CAREER: Sensory Guidance of Locomotion: From Neurons to Newton's Laws

职业：运动的感觉引导：从神经元到牛顿定律

• 批准号: 0845749
• 负责人: Noah Cowan
• 金额: $ 50万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-03-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845749&HistoricalAwards=false
• 关键词: CAREER; Sensory; Guidance; Locomotion; Neurons

How do nervous systems transform sensory signals into motor commands to guide locomotion? To address this question, this research examines a class of sensory guided locomotion tasks uniquely amenable to computational and engineering analyses: sensorimotor stabilization tasks. In these tasks, animals automatically modulate muscle commands to drive sensory signals (e.g. electrosensory, tactile, visual) to desired equilibria or limit cycles.Specifically, this research examines three remarkably divergent animal locomotor behaviors: multisensory control of swimming in weakly electric knifefish, high-speed antenna-based wall following in cockroaches, and visual yaw control in fruit flies. While performing sensory guided locomotion tasks, these animals are subjected to behaviorally naturalistic perturbations that lead to rich transient dynamics during recovery. Mechanical signals (e.g. positions, forces) and neural activity (e.g. action potentials) during recovery to these perturbations are used to validate (or refute) specific closed-loop sensorimotor control models. These models identify the roles of mechanics versus neural computation in the stability and performance of each behavior. The same approach to modeling, analysis, and experimentation is vetted in a biomorphic robot, establishing an experimental baseline in a highly controlled context, as well as enabling the translation of biological control strategies to a robotic platform.This research, disseminated broadly through both the engineering and biological literatures, lays a scientific foundation on which to develop biomorphic robots for critical applications such as disaster recovery, space exploration, and security. Longer term, the unified approach to biological and robotic modeling developed in this project may lead to enhanced neural prostheses and brain--machine interfaces.

神经系统如何将感觉信号转化为运动命令来引导运动？为了解决这个问题，本研究探讨了一类感觉引导运动的任务，唯一适合计算和工程分析：感觉运动稳定任务。在这些任务中，动物自动调节肌肉命令，以驱动感官信号（如电，触觉，视觉）所需的平衡或极限cycles.Specifically，本研究探讨了三个显着不同的动物运动行为：多感官控制游泳弱电动刀鱼，高速天线为基础的墙壁以下的蟑螂，和视觉偏航控制果蝇。在执行感官引导的运动任务时，这些动物受到行为上自然主义的扰动，导致恢复过程中丰富的瞬态动力学。机械信号（如位置，力）和神经活动（如动作电位）在恢复这些扰动被用来验证（或反驳）特定的闭环感觉运动控制模型。这些模型确定了力学与神经计算在每种行为的稳定性和性能中的作用。同样的建模、分析和实验方法在生物形态机器人中得到验证，在高度受控的环境中建立了实验基线，并将生物控制策略转化为机器人平台。这项研究通过工程和生物文献广泛传播，为开发用于灾难恢复、太空探索和安全从长远来看，该项目中开发的生物和机器人建模的统一方法可能会导致增强的神经假体和脑机接口。