Please use this identifier to cite or link to this item: https://doi.org/10.1371/journal.pone.0165600
Title: A novel robot system integrating biological and mechanical intelligence based on dissociated neural network-controlled closed-loop environment
Authors: Li Y. 
Sun R.
Wang Y.
Li H.
Zheng X.
Keywords: animal cell
Article
artificial intelligence
artificial neural network
control system
dissociated neural network
embryo
hippocampus
information processing
machine learning
nerve cell network
neuroprosthesis
nonhuman
rat
robotics
signal processing
simulation
software
algorithm
artificial neural network
biomechanics
cell culture technique
cytology
devices
drug effects
human
nerve cell
robotics
tetanus antibody
Algorithms
Biomechanical Phenomena
Cell Culture Techniques
Humans
Neural Networks (Computer)
Neurons
Robotics
Software
Tetanus Antitoxin
Issue Date: 2016
Citation: Li Y., Sun R., Wang Y., Li H., Zheng X. (2016). A novel robot system integrating biological and mechanical intelligence based on dissociated neural network-controlled closed-loop environment. PLoS ONE 11 (11) : e0165600. ScholarBank@NUS Repository. https://doi.org/10.1371/journal.pone.0165600
Rights: Attribution 4.0 International
Abstract: We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. © 2016 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/10635/161545
ISSN: 19326203
DOI: 10.1371/journal.pone.0165600
Rights: Attribution 4.0 International
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