Digital neuromorphic implementation of the biologically inspired pallidal oscillator

Shuangming Yang, Xinyu Hao, Jiang Wang, Huiyan Li, Bin Deng, Yanqiu Che

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

Abstract

This paper proposes a modified biologically conductance-based pallidal oscillator model, targeting low-cost and multiplierless implementation with relevant and reliable dynamical characteristics on digital neuromorphic platform. High-Accuracy neural computation is limited in scale and efficiency by available hardware resources, so there are significant demands for costefficient hardware circuits in the large-scale simulations of neuromorphic field. Thus, the feasibility of a digital implementation with lower hardware overhead cost is investigated in this paper. Implementation results on a field-programmable gate array device demonstrate that the presented model can reduce the hardware resource cost significantly compared to the conventional look-up-Table-based design. The proposed methology is an essential step towards the real-Time implementation of large-scale spiking neural network, and is meaningful for the investigation on the neurodegenerative diseases and its model-based closed-loop control. It can also be applied in the real-Time control of the bio-inspired neurorobotics.

Original languageEnglish (US)
Title of host publicationProceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018
PublisherAssociation for Computing Machinery
Pages23-28
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

All Science Journal Classification (ASJC) codes

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

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  • Cite this

    Yang, S., Hao, X., Wang, J., Li, H., Deng, B., & Che, Y. (2018). Digital neuromorphic implementation of the biologically inspired pallidal oscillator. In Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018 (pp. 23-28). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3233740.3233748