Looking for synergies between the equilibrium point hypothesis and internal models.
Looking for synergies between the equilibrium point hypothesis and internal models.
复制标题
寻找平衡点假设和内部模型之间的协同作用。
DOI：
10.1123/mcj.14.3.e31
复制
发表时间：
2010
期刊：
影响因子：
1.1
通讯作者：
Kording,KonradP
中科院分区：
文献类型：
--
作者：
Shapiro,MarkB;Kording,KonradP
神经系统需要控制复杂的身体在复杂的世界中的运动，这个世界由于感觉和运动噪音而受到不确定性。在这一期中，Mark Latash支持一个解决这些问题的框架--分层均衡点假说(我们在这里称之为HEPH)。根据这一假设，通过让中央控制器指定参考体配置中的移位来创建运动。在本文中，我们发现讨论其他方法是有用的，特别是侧重于估计的方法(例如内部模型，IMS)和侧重于给定估计的方法(例如最优控制，OC)1.神经系统如何实施估计(神经IM)和神经系统如何实施控制(神经控制，NC)已经提出了具体的建议。EPH是NC的一个版本，它表明运动系统通过集中调整脊柱本体感觉反馈的参数来简化控制问题。分布式分级控制的思想似乎已经在许多社区出现，以解决控制的复杂性问题。我们将简要审查不同方法之间的差异，并强调可能的协同作用。
The nervous system needs to control movement of a complex body in a complex world subject to uncertainty due to sensory and motor noise. In this issue, Mark Latash argues in favor of a framework that addresses these problems–the Hierarchical Equilibrium Point Hypothesis (as we call it here, HEPH). According to this hypothesis, movement is created by having a central controller specify a shift in the reference body configuration. This timevarying reference body configuration is passed on to lower level (eg, limb) controllers, and finally to the lowest level controllers which set the thresholds of the tonic stretch reflex of individual muscles as assumed in the lambda model.In the context of this paper we find it useful to discuss other approaches, in particular approaches that focus on estimation (eg Internal models, IMs) and approaches that focus on control given estimations (eg Optimal control, OC) 1. Specific proposals have been put forward of how the nervous system may implement estimation (neural IM) and proposals of how the nervous system may implement control (neural Control, nC). The EPH is a version of nC which suggests that the motor system simplifies the control problem by centrally adjusting parameters of the spinal proprioceptive feedback. The idea of distributed hierarchical control seems to have emerged in many communities to solve the problem of control complexity. We will briefly review the differences across approaches and highlight possible synergies.