Optimal feedback control to describe multiple representations of primary motor cortex neurons
Optimal feedback control to describe multiple representations of primary motor cortex neurons
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DOI：
10.1007/s10827-017-0650-z
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发表时间：
2017-06
影响因子：
1.2
通讯作者：
Y. Ueyama
中科院分区：
文献类型：
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作者：
Y. Ueyama
初级运动皮层（M1）神经元对与运动控制相关的几个参数进行调节，最近有报道称M1在反馈控制中很重要。然而，目前还不清楚M1神经元如何编码信息来控制肌肉骨骼系统。在这项研究中，我们研究了M1的基础上最佳反馈控制（OFC）理论，这是一个合理的假设神经运动控制的计算机制。我们模拟了一个等长扭矩产生任务，该任务需要在肌肉骨骼系统中调节关节扭矩并将其保持在所需水平，该肌肉骨骼系统在物理上受到肌肉的约束，其作用是拉动而不是推动。然后，在约束条件下，使用优化方法计算反馈控制器。在存在神经运动噪声（称为信号依赖性噪声）的情况下，感觉反馈增益被调谐到外部运动输出，例如手部力量，就像M1神经元的群体反应。此外，M1神经元的偏好方向（PD）的分布可以通过反馈增益来预测。因此，我们认为，神经活动在M1是优化的肌肉骨骼系统。此外，如果反馈控制器表示在M1中，OFC可以描述M1的多个表示，不仅包括PD的分布，而且还包括神经元群体的响应。
Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predictedviafeedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.