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dc.contributor.author
Lew, Sergio Eduardo
dc.contributor.author
Rey, Hernan Gonzalo
dc.contributor.author
Gutnisky, D. A.
dc.contributor.author
Zanutto, Bonifacio Silvano
dc.date.available
2017-10-06T20:50:48Z
dc.date.issued
2008-08
dc.identifier.citation
Lew, Sergio Eduardo; Rey, Hernan Gonzalo; Gutnisky, D. A.; Zanutto, Bonifacio Silvano; Differences in prefrontal and motor structures learning dynamics depend on task complexity: a neural network model; Elsevier Science; Neurocomputing; 71; 13-15; 8-2008; 2782-2793
dc.identifier.issn
0925-2312
dc.identifier.uri
http://hdl.handle.net/11336/26188
dc.description.abstract
Neurons in the basal ganglia (BG) of monkeys learning a simple visual discrimination (VD) task show faster changes in activity than those in the prefrontal cortex (PFC). This motivated the hypothesis that changes in the BG activity can ''lead'' those in the PFC. Given that the PFC is a key player in the learning of complex tasks, we tested the former hypothesis by using a neural network model that learns simple and complex contingencies as VD and delayed matching to sample (DMTS) tasks. Even though the model accounted for the results in the VD task no such ''lead'' was observed in the DMTS task. We propose that when the task requires learning high-order contingencies, such as in the DMTS case, motor structures quickly select the subset of responses allowing the subject to obtain reward, but learning in the cortico-BG loop progresses in a concurrent way in order to maximize reward.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Prefontal Cortex
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Premotor Cortex
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Basal Ganglia
dc.subject
Dopamine
dc.subject.classification
Ingeniería Médica
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Ingeniería Médica
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Differences in prefrontal and motor structures learning dynamics depend on task complexity: a neural network model
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-10-04T15:02:33Z
dc.identifier.eissn
0925-2312
dc.journal.volume
71
dc.journal.number
13-15
dc.journal.pagination
2782-2793
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Lew, Sergio Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
dc.description.fil
Fil: Rey, Hernan Gonzalo. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina
dc.description.fil
Fil: Gutnisky, D. A.. Texas A&M University; Estados Unidos
dc.description.fil
Fil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; Argentina
dc.journal.title
Neurocomputing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0925231207003104
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.neucom.2007.09.010
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