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dc.contributor.authorBullock, Danielen_US
dc.contributor.authorContreras-Vidal, Joséen_US
dc.date.accessioned2011-11-14T18:21:52Z
dc.date.available2011-11-14T18:21:52Z
dc.date.issued1991-10
dc.identifier.urihttps://hdl.handle.net/2144/2078
dc.description.abstractThis paper attempts a rational, step-by-step reconstruction of many aspects of the mammalian neural circuitry known to be involved in the spinal cord's regulation of opposing muscles acting on skeletal segments. Mathematical analyses and local circuit simulations based on neural membrane equations are used to clarify the behavioral function of five fundamental cell types, their complex connectivities, and their physiological actions. These cell types are: α-MNs, γ-MNs, IaINs, IbINs, and Renshaw cells. It is shown that many of the complexities of spinal circuitry are necessary to ensure near invariant realization of motor intentions when descending signals of two basic types independently vary over large ranges of magnitude and rate of change. Because these two types of signal afford independent control, or Factorization, of muscle LEngth and muscle TEnsion, our construction was named the FLETE model (Bullock and Grossberg, 1988b, 1989). The present paper significantly extends the range of experimental data encompassed by this evolving model.en_US
dc.description.sponsorshipNational Science Foundation (IRI-87-16960, IRI-90-24877); Instituto Tecnológico y de Estudios Superiores de Monterreyen_US
dc.language.isoen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-1991-023
dc.rightsCopyright 1991 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.titleHow Spinal Neural Networks Reduce Discrepancies between Motor Intention and Motor Realizationen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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