Article: Robotic simulation of human brain using convolutional deep belief networks Journal: International Journal of Intelligent Machines and Robotics (IJIMR) 2018 Vol.1 No.2 pp.180 - 191 Abstract: Collective endeavours in the fields of computational neuroscience, software engineering, and biology permitted outlining naturally sensible models of the human brain in light of convolutional deep belief networks. While satisfactory devices exist to mimic either complex neural systems or their surroundings, there is so far no mechanism that permits to productively setting up a correspondence amongst brain and its mathematical model. Deep robotics is another stage that intends to fill this gap by offering researchers and innovation engineers in distinguishing human brain diseases by enabling them to associate human brain models to itemised re-enactments of automated programming. In this manuscript, deep robotics utilising convolutional deep belief networks were exploited to recreate human brain in distinguishing brain diseases. Prediction accuracy of the three noteworthy ideal models, for example, artificial neural networks, machine learning and deep learning were looked at in distinguishing brain related diseases, such as, Alzheimer's disease and Parkinson's sickness. Customary on the numerical analysis, convolutional deep belief networks outclassed neural back-propagation networks and convolutional neural networks in estimating Alzheimer's disease and Parkinson's sickness. Inderscience Publishers - linking academia, business and industry through research

Title: Robotic simulation of human brain using convolutional deep belief networks

Authors: P.S. Jagadeesh Kumar; Yanmin Yuan; Yang Yung; Mingmin Pan; Wenli Hu

Addresses: Department of Computer Science and Engineering, Harvard University, Cambridge, USA ' Department of Bioengineering, Harvard University, Cambridge, USA ' Biomedical Engineering Research Centre, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore ' Biomedical Engineering Research Centre, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore ' Biomedical Engineering Research Centre, Nanyang Technological University, 50 Nanyang Ave, 639798, Singapore

Abstract: Collective endeavours in the fields of computational neuroscience, software engineering, and biology permitted outlining naturally sensible models of the human brain in light of convolutional deep belief networks. While satisfactory devices exist to mimic either complex neural systems or their surroundings, there is so far no mechanism that permits to productively setting up a correspondence amongst brain and its mathematical model. Deep robotics is another stage that intends to fill this gap by offering researchers and innovation engineers in distinguishing human brain diseases by enabling them to associate human brain models to itemised re-enactments of automated programming. In this manuscript, deep robotics utilising convolutional deep belief networks were exploited to recreate human brain in distinguishing brain diseases. Prediction accuracy of the three noteworthy ideal models, for example, artificial neural networks, machine learning and deep learning were looked at in distinguishing brain related diseases, such as, Alzheimer's disease and Parkinson's sickness. Customary on the numerical analysis, convolutional deep belief networks outclassed neural back-propagation networks and convolutional neural networks in estimating Alzheimer's disease and Parkinson's sickness.

Keywords: Alzheimer's disease; convolutional deep belief networks; CDBN; deep robotics; human brain simulation; neuroscience; Parkinson's sickness.

DOI: 10.1504/IJIMR.2018.10016324

International Journal of Intelligent Machines and Robotics, 2018 Vol.1 No.2, pp.180 - 191

Received: 26 Jul 2018
Accepted: 28 Jul 2018
Published online: 26 Sep 2018 *

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Title: Robotic simulation of human brain using convolutional deep belief networks

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