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首页> 外文期刊>Journal of Computational Neuroscience >Correlations in state space can cause sub-optimal adaptation of optimal feedback control models
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Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

机译：状态空间中的相关性可能导致最优反馈控制模型的次优自适应

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Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.
机译：小脑中的自适应内部模型显然有助于控制我们的运动。长期以来，人们一直以为这个内部模型实现了自适应逆模型并生成了运动指令，但是最近许多人都拒绝了这个想法，而是赞成正向模型假设。从理论上讲，前向模型可以预测到达运动期间的即将到来状态，因此运动皮层可以生成适当的运动命令。此过程的最新计算模型依赖于控制理论的最佳反馈控制（OFC）框架。 OFC是描述电机控制的强大工具，它没有描述自适应。有些人认为仅对正向模型的适应就可以解释运动适应，但这被普遍认为过于简单。然而，自适应最优控制器难以实现。一个合理的选择是允许向前模型调整以“重新调整”控制器。我们的模拟表明，正如预期的那样，仅向前模型自适应在达到受力场干扰的运动期间不会产生最佳轨迹。但是，他们还表明，从正向模型重新优化控制器可能不是最佳选择。这是因为，在具有状态相关性或冗余性的系统中，准确的预测需要与最佳控制不同的信息。我们发现，将噪声添加到与人类数据中发现的噪声匹配的运动中足以解决此问题。但是，由于用于控制实际运动的状态空间比我们的简单模拟要复杂得多，因此不能忽略相关性对正向模型对控制器重新适应的影响。

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