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Adaptive neural models of queuing and timing in fluent action.

机译:流利动作中排队和计时的自适应神经模型。

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摘要

In biological cognition, specialized representations and associated control processes solve the temporal problems inherent in skilled action. Recent data and neural circuit models highlight three distinct levels of temporal structure: sequence preparation, velocity scaling, and state-sensitive timing. Short sequences of actions are prepared collectively in prefrontal cortex, then queued for performance by a cyclic competitive process that operates on a parallel analog representation. Successful acts like ball-catching depend on coordinated scaling of effector velocities, and velocity scaling, mediated by the basal ganglia, may be coupled to perceived time-to-contact. Making acts accurate at high speeds requires state-sensitive and precisely timed activations of muscle forces in patterns that accelerate and decelerate the effectors. The cerebellum may provide a maximally efficient representational basis for learning to generate such timed activation patterns.
机译:在生物认知中,专门的表示法和相关的控制过程解决了熟练动作固有的时间问题。最新的数据和神经回路模型强调了时间结构的三个不同层次:序列准备,速度定标和状态敏感的时序。简短的动作序列在前额叶皮层中共同准备,然后通过以并行模拟表示形式运行的循环竞争过程排队等待执行。像捉球这样的成功行为取决于效应器速度的协调缩放,并且由基底神经节介导的速度缩放可能与感知的接触时间有关。要使动作准确地进行高速运动,需要对状态敏感的肌肉力量进行精确定时的激活,以使效应器加速和减速。小脑可以为学习生成此类定时激活模式提供最大有效的表示基础。

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