Real-Time implementation of the coupled neural mass and its application

Xinyu Hao, Huiyan Li, Jiang Wang, Xile Wei, Shuangming Yang, Yanqiu Che

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The neural mass model is a self-oscillation network composed of two neural populations. In this study, we use the fieldprogrammable gate array (FPGA) device to implement the neural mass model and the hardware implementation results are exactly the same as the MATLAB simulation results. The study reveals that dynamical characteristics of the neural population implemented on FPGA can meet the real-Time computational requirements. Besides, we propose a control method of the robotic arm based on the oscillation dynamics of the network. For the implementation results of FPGA is real-Time, it can be used to realize the robotic control. A closed-loop control system is realized by inputting the error signals of robotic arm into the neural network model and obtaining the feedback signal to arm joint for error elimination. The results show that the control method based on the neural mass model can quickly and effectively eliminate the angle errors.

Original languageEnglish (US)
Title of host publicationProceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018
PublisherAssociation for Computing Machinery
Pages29-34
Number of pages6
ISBN (Electronic)9781450364614
DOIs
StatePublished - Jun 30 2018
Event2018 International Conference on Intelligent Science and Technology, ICIST 2018 - London, United Kingdom
Duration: Jun 30 2018Jul 2 2018

Publication series

NameACM International Conference Proceeding Series

Other

Other2018 International Conference on Intelligent Science and Technology, ICIST 2018
CountryUnited Kingdom
CityLondon
Period6/30/187/2/18

Fingerprint

Robotic arms
Closed loop control systems
MATLAB
Robotics
Neural networks
Feedback
Hardware

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

Cite this

Hao, X., Li, H., Wang, J., Wei, X., Yang, S., & Che, Y. (2018). Real-Time implementation of the coupled neural mass and its application. In Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018 (pp. 29-34). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3233740.3233749
Hao, Xinyu ; Li, Huiyan ; Wang, Jiang ; Wei, Xile ; Yang, Shuangming ; Che, Yanqiu. / Real-Time implementation of the coupled neural mass and its application. Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018. Association for Computing Machinery, 2018. pp. 29-34 (ACM International Conference Proceeding Series).
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Hao, X, Li, H, Wang, J, Wei, X, Yang, S & Che, Y 2018, Real-Time implementation of the coupled neural mass and its application. in Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 29-34, 2018 International Conference on Intelligent Science and Technology, ICIST 2018, London, United Kingdom, 6/30/18. https://doi.org/10.1145/3233740.3233749

Real-Time implementation of the coupled neural mass and its application. / Hao, Xinyu; Li, Huiyan; Wang, Jiang; Wei, Xile; Yang, Shuangming; Che, Yanqiu.

Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018. Association for Computing Machinery, 2018. p. 29-34 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hao X, Li H, Wang J, Wei X, Yang S, Che Y. Real-Time implementation of the coupled neural mass and its application. In Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018. Association for Computing Machinery. 2018. p. 29-34. (ACM International Conference Proceeding Series). https://doi.org/10.1145/3233740.3233749