Adaptive Neural Models of Queuing and Timing in Fluent Action

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dc.contributor.author Bullock, Daniel en_US
dc.date.accessioned 2011-11-14T18:15:28Z
dc.date.available 2011-11-14T18:15:28Z
dc.date.issued 2003-09 en_US
dc.identifier.uri http://hdl.handle.net/2144/1915
dc.description.abstract Temporal structure in skilled, fluent action exists at several nested levels. At the largest scale considered here, short sequences of actions that are planned collectively in prefrontal cortex appear to be queued for performance by a cyclic competitive process that operates in concert with a parallel analog representation that implicitly specifies the relative priority of elements of the sequence. At an intermediate scale, single acts, like reaching to grasp, depend on coordinated scaling of the rates at which many muscles shorten or lengthen in parallel. To ensure success of acts such as catching an approaching ball, such parallel rate scaling, which appears to be one function of the basal ganglia, must be coupled to perceptual variables, such as time-to-contact. At a fine scale, within each act, desired rate scaling can be realized only if precisely timed muscle activations first accelerate and then decelerate the limbs, to ensure that muscle length changes do not under- or over-shoot the amounts needed for the precise acts. Each context of action may require a much different timed muscle activation pattern than similar contexts. Because context differences that require different treatment cannot be known in advance, a formidable adaptive engine-the cerebellum-is needed to amplify differences within, and continuosly search, a vast parallel signal flow, in order to discover contextual "leading indicators" of when to generate distinctive parallel patterns of analog signals. From some parts of the cerebellum, such signals controls muscles. But a recent model shows how the lateral cerebellum, such signals control muscles. But a recent model shows how the lateral cerebellum may serve the competitive queuing system (in frontal cortex) as a repository of quickly accessed long-term sequence memories. Thus different parts of the cerebellum may use the same adaptive engine system design to serve the lowest and the highest of the three levels of temporal structure treated. If so, no one-to-one mapping exists between levels of temporal structure and major parts of the brain. Finally, recent data cast doubt on network-delay models of cerebellar adaptive timing. en_US
dc.description.sponsorship National Institute of Mental Health (R01 DC02852) en_US
dc.language.iso en_US en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-2003-018 en_US
dc.rights Copyright 2003 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. en_US
dc.subject Adaptive timing en_US
dc.subject Competitive queuing en_US
dc.subject Neural network en_US
dc.subject Prehension en_US
dc.subject Motor coordination en_US
dc.subject Time-to-contact en_US
dc.subject Cerebellum en_US
dc.subject Prefrontal cortex en_US
dc.subject Basal ganglia en_US
dc.title Adaptive Neural Models of Queuing and Timing in Fluent Action en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US

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