{"title":"生物控制中的内部模型。","authors":"Daniel McNamee, Daniel M Wolpert","doi":"10.1146/annurev-control-060117-105206","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29961,"journal":{"name":"Annual Review of Control Robotics and Autonomous Systems","volume":"2 ","pages":"339-364"},"PeriodicalIF":11.2000,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520231/pdf/emss-79233.pdf","citationCount":"0","resultStr":"{\"title\":\"Internal Models in Biological Control.\",\"authors\":\"Daniel McNamee, Daniel M Wolpert\",\"doi\":\"10.1146/annurev-control-060117-105206\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":29961,\"journal\":{\"name\":\"Annual Review of Control Robotics and Autonomous Systems\",\"volume\":\"2 \",\"pages\":\"339-364\"},\"PeriodicalIF\":11.2000,\"publicationDate\":\"2019-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6520231/pdf/emss-79233.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Control Robotics and Autonomous Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-control-060117-105206\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Control Robotics and Autonomous Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1146/annurev-control-060117-105206","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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.