Please use this identifier to cite or link to this item:
https://doi.org/10.1371/journal.pone.0127452
DC Field | Value | |
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dc.title | Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence | |
dc.contributor.author | Li Y. | |
dc.contributor.author | Sun R. | |
dc.contributor.author | Zhang B. | |
dc.contributor.author | Wang Y. | |
dc.contributor.author | Li H. | |
dc.date.accessioned | 2019-11-06T01:30:50Z | |
dc.date.available | 2019-11-06T01:30:50Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Li 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.issn | 19326203 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/161511 | |
dc.description.abstract | Neural 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.rights | Attribution 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.source | Unpaywall 20191101 | |
dc.subject | Article | |
dc.subject | artificial intelligence | |
dc.subject | artificial neural network | |
dc.subject | brain computer interface | |
dc.subject | experimental design | |
dc.subject | mobile phone | |
dc.subject | nerve cell culture | |
dc.subject | nerve cell plasticity | |
dc.subject | robotics | |
dc.subject | stimulus response | |
dc.subject | task performance | |
dc.subject | animal | |
dc.subject | female | |
dc.subject | pregnancy | |
dc.subject | rat | |
dc.subject | system analysis | |
dc.subject | Animals | |
dc.subject | Female | |
dc.subject | Neural Networks (Computer) | |
dc.subject | Pregnancy | |
dc.subject | Rats | |
dc.subject | Systems Integration | |
dc.type | Article | |
dc.contributor.department | BIOMEDICAL ENGINEERING | |
dc.description.doi | 10.1371/journal.pone.0127452 | |
dc.description.sourcetitle | PLoS ONE | |
dc.description.volume | 10 | |
dc.description.issue | 5 | |
dc.description.page | e0127452 | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements |
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10_1371_journal_pone_0127452.pdf | 4.07 MB | Adobe PDF | OPEN | Published | View/Download |
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