Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0127452
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dc.titleApplication of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence
dc.contributor.authorLi Y.
dc.contributor.authorSun R.
dc.contributor.authorZhang B.
dc.contributor.authorWang Y.
dc.contributor.authorLi H.
dc.date.accessioned2019-11-06T01:30:50Z
dc.date.available2019-11-06T01:30:50Z
dc.date.issued2015
dc.identifier.citationLi Y., Sun R., Zhang B., Wang Y., Li H. (2015). Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence. PLoS ONE 10 (5) : e0127452. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0127452
dc.identifier.issn19326203
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/161511
dc.description.abstractNeural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. © 2015 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20191101
dc.subjectArticle
dc.subjectartificial intelligence
dc.subjectartificial neural network
dc.subjectbrain computer interface
dc.subjectexperimental design
dc.subjectmobile phone
dc.subjectnerve cell culture
dc.subjectnerve cell plasticity
dc.subjectrobotics
dc.subjectstimulus response
dc.subjecttask performance
dc.subjectanimal
dc.subjectfemale
dc.subjectpregnancy
dc.subjectrat
dc.subjectsystem analysis
dc.subjectAnimals
dc.subjectFemale
dc.subjectNeural Networks (Computer)
dc.subjectPregnancy
dc.subjectRats
dc.subjectSystems Integration
dc.typeArticle
dc.contributor.departmentBIOMEDICAL ENGINEERING
dc.description.doi10.1371/journal.pone.0127452
dc.description.sourcetitlePLoS ONE
dc.description.volume10
dc.description.issue5
dc.description.pagee0127452
dc.published.statePublished
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This item is licensed under a Creative Commons License Creative Commons