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dc.contributor.authorZhong, Junpei
dc.contributor.authorCangelosi, Angelo
dc.contributor.authorWermter, Stefan
dc.date.accessioned2014-10-07T09:15:48Z
dc.date.available2014-10-07T09:15:48Z
dc.date.issued2014-02-04
dc.identifier.citationZhong , J , Cangelosi , A & Wermter , S 2014 , ' Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives ' , Frontiers in Behavioral Neuroscience , vol. 8 , no. FEB , 22 . https://doi.org/10.3389/fnbeh.2014.00022
dc.identifier.issn1662-5153
dc.identifier.otherPURE: 7644541
dc.identifier.otherPURE UUID: ceb71c34-b863-462e-8ef6-7bd6a053f9a0
dc.identifier.otherScopus: 84893634354
dc.identifier.urihttp://hdl.handle.net/2299/14543
dc.descriptionCopyright ©2014 Zhong, Cangelosi and Wermter.This is an open-access article distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms
dc.description.abstractThe acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.en
dc.language.isoeng
dc.relation.ispartofFrontiers in Behavioral Neuroscience
dc.rightsOpen
dc.subjectHorizontal product
dc.subjectParametric biases
dc.subjectPre-symbolic communication
dc.subjectRecurrent neural networks
dc.subjectSensorimotor integration
dc.subjectBehavioral Neuroscience
dc.subjectCognitive Neuroscience
dc.subjectNeuropsychology and Physiological Psychology
dc.titleToward a self-organizing pre-symbolic neural model representing sensorimotor primitivesen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2014-02-04
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.3389/fnbeh.2014.00022
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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