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dc.contributor.authorZeid, Omaren_US
dc.contributor.authorBullock, Danielen_US
dc.coverage.spatialUnited Statesen_US
dc.date2019-08-09
dc.date.accessioned2020-04-14T18:59:10Z
dc.date.available2020-04-14T18:59:10Z
dc.date.issued2019-12
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/31522826
dc.identifier.citationOmar Zeid, Daniel Bullock. 2019. "Moving in time: Simulating how neural circuits enable rhythmic enactment of planned sequences.." Neural Netw, Volume 120, pp. 86 - 107. https://doi.org/10.1016/j.neunet.2019.08.006
dc.identifier.issn1879-2782
dc.identifier.urihttps://hdl.handle.net/2144/40161
dc.description.abstractMany complex actions are mentally pre-composed as plans that specify orderings of simpler actions. To be executed accurately, planned orderings must become active in working memory, and then enacted one-by-one until the sequence is complete. Examples include writing, typing, and speaking. In cases where the planned complex action is musical in nature (e.g. a choreographed dance or a piano melody), it appears to be possible to deploy two learned sequences at the same time, one composed from actions and a second composed from the time intervals between actions. Despite this added complexity, humans readily learn and perform rhythm-based action sequences. Notably, people can learn action sequences and rhythmic sequences separately, and then combine them with little trouble (Ullén & Bengtsson 2003). Related functional MRI data suggest that there are distinct neural regions responsible for the two different sequence types (Bengtsson et al. 2004). Although research on musical rhythm is extensive, few computational models exist to extend and inform our understanding of its neural bases. To that end, this article introduces the TAMSIN (Timing And Motor System Integration Network) model, a systems-level neural network model capable of performing arbitrary item sequences in accord with any rhythmic pattern that can be represented as a sequence of integer multiples of a base interval. In TAMSIN, two Competitive Queuing (CQ) modules operate in parallel. One represents and controls item order (the ORD module) and the second represents and controls the sequence of inter-onset-intervals (IOIs) that define a rhythmic pattern (RHY module). Further circuitry helps these modules coordinate their signal processing to enable performative output consistent with a desired beat and tempo.en_US
dc.format.extentp. 86 - 107en_US
dc.languageeng
dc.language.isoen_US
dc.relation.ispartofNeural Netw
dc.subjectCompetitive queuingen_US
dc.subjectComputational neuroscienceen_US
dc.subjectMusic performanceen_US
dc.subjectRhythmen_US
dc.subjectSystems neuroscienceen_US
dc.subjectBrainen_US
dc.subjectConnectomeen_US
dc.subjectHumansen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectModels, neurologicalen_US
dc.subjectMusicen_US
dc.subjectNerve neten_US
dc.subjectNeural networks, computeren_US
dc.subjectPeriodicityen_US
dc.subjectTime perceptionen_US
dc.subjectArtificial intelligence & image processingen_US
dc.titleMoving in time: simulating how neural circuits enable rhythmic enactment of planned sequencesen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1016/j.neunet.2019.08.006
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Psychological & Brain Sciencesen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv488413


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