IntroductionAdaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.MethodsResults for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent.Results and discussionOur analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.

中文翻译：

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适应和学习作为神经反馈任务奖励最大化的策略

简介据观察，适应和学习有助于获得新的运动技能，并被用作应对不断变化的环境的策略。然而，在执行目标导向的运动任务时，很难确定每个因素的相对贡献。本研究探讨了在旋转扰动的影响下，在具有连续视觉反馈的中心向外触及任务期间神经活动的动态。方法比较了两个非人类灵长类动物 (NHP) 受试者执行的脑机接口 (BCI) 任务的结果到执行类似任务的强化学习代理的模拟。我们对基线活动进行了表征，并将其与引入不同幅度的旋转扰动后的活动进行了比较。我们采用主成分分析 (PCA) 来分析 NHP BCI 任务中驱动光标的尖峰活动以及强化学习代理的神经网络的激活。结果和讨论我们的分析表明，对于 NHP 和强化学习来说，代理，任务相关的神经流形与任务同构。然而，对于 NHP 来说，流形在很大程度上保留了所探索的所有旋转扰动，并且神经活动的适应发生在该流形内，因为通过以取消所述旋转的角度模式重新分配神经空间区域来补偿旋转。相反，重新训练强化学习代理以在旋转后达到目标会导致底层神经流形的实质性修改。我们的研究结果表明，NHP 以定量精确的方式调整其现有的神经动态库，以解释不同程度的扰动，并且它们这样做的方式消除了广泛学习的需要。