Adaptive sensory-Motor Planning By Humans and machines

该项目将开发实时神经网络模型，用于复杂感觉运动技能的自适应控制和规划。这些模型对于理解人类运动规划和设计灵活的特征电路同样重要，这些电路用于产生多关节运动系统（如手臂和语音发音器）在可变速度下的同步运动。当前的研究将研究辅助电路的设计，这些电路能够快速适应新运动中出现的意外负载和惯性，并在这些新运动通过实践变得熟悉时慢慢学习预测正确的运动参数。此外，该项目将尝试分析如何将基本或原子运动组织成复杂的运动序列，以及如何通过学习使这些序列的控制自动化。此外，手眼协调将通过展示如何将眼-头系统所看的地方的表征关联转化为手-臂系统被命令移动的地方来分析。有关动作的学习时间和以身体为中心的协调系统的自组织问题也将进行分析。

The project will develop real-time neural network models for the adaptive control and planning of complex sensory-motor skills. Such models are equally important for understanding human movement planning and the design of flexible characterized circuits for generating synchronous movements of multi-joint motor systems, such as arms and speech articulators, at variable speeds. The current research will investigate the design of supplementary circuits that are capable of rapidly adapting to the unexpected loads and inertias that arise during novel movements, and of slowly learning to predict the correct movement parameters when these novel movements become familiar through practice. In addition, the project will attemp to analyze how elementary, or atomic, movements are organized into complex movement sequences, and how the controls for these sequences become automated through learning. Also, eye-hand coordination will be analyzed by showing how a representation of where the eye-head system is looking is associatively transformed into where the hand-arm system is commanded to move. Issues concerning the learned timing of actions and self-organization of a body-centered coordinated system will also be analyzed.