Internal Models in Biological Control.

IF 11.2 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Annual Review of Control Robotics and Autonomous Systems Pub Date : 2019-05-01 DOI:10.1146/annurev-control-060117-105206
Daniel McNamee, Daniel M Wolpert
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Abstract

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

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生物控制中的内部模型。
最优反馈控制和贝叶斯推理等理性原则支撑着一个概率框架,该框架解释了生物感应运动控制中的一系列经验现象。为了促进符合这些理论的灵活、稳健行为的优化,构建运动系统和环境动态内部模型的能力至关重要。在这一理论形式的背景下,我们回顾了此类内部模型所发挥的计算作用,以及它们在大脑中实现的神经和行为证据。
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来源期刊
CiteScore
28.30
自引率
2.20%
发文量
25
期刊介绍: The Annual Review of Control, Robotics, and Autonomous Systems offers comprehensive reviews on theoretical and applied developments influencing autonomous and semiautonomous systems engineering. Major areas covered include control, robotics, mechanics, optimization, communication, information theory, machine learning, computing, and signal processing. The journal extends its reach beyond engineering to intersect with fields like biology, neuroscience, and human behavioral sciences. The current volume has transitioned to open access through the Subscribe to Open program, with all articles published under a CC BY license.
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