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dc.creatorGOLES,ERIC
dc.creatorPALACIOS,ADRIÁN G
dc.date2007-01-01
dc.date.accessioned2019-05-02T21:21:41Z
dc.date.available2019-05-02T21:21:41Z
dc.identifierhttps://scielo.conicyt.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500009
dc.identifier.urihttp://revistaschilenas.uchile.cl/handle/2250/81823
dc.descriptionIn the last twenty years an important effort in brain sciences, especially in cognitive science, has been the development of mathematical tool that can deal with the complexity of extensive recordings corresponding to the neuronal activity obtained from hundreds of neurons. We discuss here along with some historical issues, advantages and limitations of Artificial Neural Networks (ANN) that can help to understand how simple brain circuits work and whether ANN can be helpful to understand brain neural complexity
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dc.languageen
dc.publisherSociedad de Biología de Chile
dc.relation10.4067/S0716-97602007000500009
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceBiological Research v.40 n.4 2007
dc.subjectArtificial
dc.subjectNeural Net
dc.subjectBrain
dc.subjectDynamical Complexity
dc.subjectComputational Neurosciences
dc.subjectCellular Automata
dc.titleDynamical Complexity in Cognitive Neural Networks


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