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dc.contributor.advisorMatthew A. Wilson.en_US
dc.contributor.authorLynn, Michael (Michael Benjamin)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences.en_US
dc.date.accessioned2016-01-15T21:10:01Z
dc.date.available2016-01-15T21:10:01Z
dc.date.copyright2015en_US
dc.date.issued2015en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/100876
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2015.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 25-26).en_US
dc.description.abstractOrganisms have a remarkable ability to respond to complex sensory inputs with intricate, tuned motor patterns. How does the brain organize and tune these motor responses, and are certain circuit architectures, or connectivity patterns, optimally suited for certain sensorimotor applications? This thesis presents progress towards this particular problem in three subprojects. The first section re-analyzes a large data set of single-unit recordings in zebra finch area HVC during singing. While HVC is known to be essential for proper expression of adult vocalization, its circuit architecture is contentious. Evidence is presented against the recently postulated gesture-trajectory extrema hypothesis for the organization of area HVC. Instead, the data suggest that the synaptic chain model of HVC organization is a better fit for the data, where chains of RA-projecting HVC neurons are synaptically connected to walk the bird through each time-step of the song. The second section examines how optimal sensorimotor estimation using a Bayesian inference framework could be implemented in a cerebellar circuit. Two novel behavioral paradigms are developed to assess how rats might tune their motor output to the statistics of the sensory inputs, and whether their behavior might be consistent with the use of a Bayesian inference paradigm. While neither behavior generated stable behavior, evidence indicates that rats may use a spinal circuit to rapidly and dynamically adjust motor output. The third section addresses the formation of habitual behaviors in a cortico-striatal network using rats. Stress and depression are known to significantly alter decision-making abilities, but the neural substrate of this is poorly understood. Towards this goal, rats are trained on a panel of decision-making tasks in a forced-choice T-maze, and it is shown that a chronic stress procedure produces a dramatic shift in behavior in a subset of these tasks but not the rest. This behavioral shift is reversed by optogenetic stimulation of prelimbic input to striatum, pinpointing a circuit element which may control stress-induced behavioral changes. Furthermore, a circuit hypothesis is presented to explain why sensitivity to changing reward values diminishes with overtraining.en_US
dc.description.statementofresponsibilityby Michael Lynn.en_US
dc.format.extent26 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectBrain and Cognitive Sciences.en_US
dc.titleGeneration and tuning of learned sensorimotor behavior by multiple neural circuit architecturesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciences.en_US
dc.identifier.oclc933528155en_US


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