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Munther Dahleh. Saxena, Shreya, 1988- Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-03-02T22:22:41Z 2018-03-02T22:22:41Z 2017 2017 http://hdl.handle.net/1721.1/114006 1023862488 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 125-133). The generation of fast movements during sensorimotor control is fundamentally limited by the biophysics of neural activity and the physiological dynamics of the muscles involved. Yet, the limiting factors and the corresponding tradeoffs have not been rigorously quantified. We use feedback control principles to identify limitations in the ability of the sensorimotor control system to track intended fast periodic movements. We show that (i) a linear model for movement generation fails to predict known undesirable phenomena encountered in the regime of fast movements, and (ii) the theory of pulsatile control of movement generation allows us to correctly characterize fundamental limitations in this regime. This thesis identifies the fastest periodic movement possible for given musculoskeletal and neuronal dynamics, which has far-reaching implications in sensorimotor control. The use of neuronal decoders in the Brain Machine Interface setting is discussed; we introduce a real-time decoder of neuronal activity, and derive conditions for its stability in the presence of feedback. The framework developed in this thesis allows us to characterize the effect of compromised neural and physiological activity on movement, and guide the design of corresponding therapeutic measures. by Shreya Saxena. Ph.D. 133 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Moving fast : neural constraints in closed loop Neural constraints in closed loop Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cefd61e3-c913-4df8-8c2c-ffe822e77b2d"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Moving fast : neural constraints in closed loop</Title> <Subtitle>Neural constraints in closed loop</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Saxena, Shreya, 1988-</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>The generation of fast movements during sensorimotor control is fundamentally limited by the biophysics of neural activity and the physiological dynamics of the muscles involved. Yet, the limiting factors and the corresponding tradeoffs have not been rigorously quantified. We use feedback control principles to identify limitations in the ability of the sensorimotor control system to track intended fast periodic movements. We show that (i) a linear model for movement generation fails to predict known undesirable phenomena encountered in the regime of fast movements, and (ii) the theory of pulsatile control of movement generation allows us to correctly characterize fundamental limitations in this regime. This thesis identifies the fastest periodic movement possible for given musculoskeletal and neuronal dynamics, which has far-reaching implications in sensorimotor control. The use of neuronal decoders in the Brain Machine Interface setting is discussed; we introduce a real-time decoder of neuronal activity, and derive conditions for its stability in the presence of feedback. The framework developed in this thesis allows us to characterize the effect of compromised neural and physiological activity on movement, and guide the design of corresponding therapeutic measures.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>