Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0127452
Title: Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence
Authors: Li Y. 
Sun R.
Zhang B.
Wang Y.
Li H.
Keywords: Article
artificial intelligence
artificial neural network
brain computer interface
experimental design
mobile phone
nerve cell culture
nerve cell plasticity
robotics
stimulus response
task performance
animal
female
pregnancy
rat
system analysis
Animals
Female
Neural Networks (Computer)
Pregnancy
Rats
Systems Integration
Issue Date: 2015
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
Rights: Attribution 4.0 International
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.
Source Title: PLoS ONE
URI: https://scholarbank.nus.edu.sg/handle/10635/161511
ISSN: 19326203
DOI: 10.1371/journal.pone.0127452
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1371_journal_pone_0127452.pdf4.07 MBAdobe PDF

OPEN

PublishedView/Download

SCOPUSTM   
Citations

2
checked on Aug 2, 2021

Page view(s)

144
checked on Jul 30, 2021

Download(s)

1
checked on Jul 30, 2021

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons