Encoding Longer-term Contextual Sensorimotor Information in a Predictive Coding Model

Junpei Zhong, Tetsuya Ogata, Angelo Cangelosi

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

    Abstract

    Studies suggest that the difference of the sensorimotor events can be recorded with the fast- and slower-changing neural activities in the hierarchical brain areas, in which they have bi-directional connections. The slow-changing representations attempt to predict the activities on the faster level by relaying categorized sensorimotor events. On the other hand, the incoming sensory information corrects such event-based prediction on the higher level by the novel or surprising signal. From this motivation, we propose a predictive hierarchical artificial neural network model which is implemented the differentiated temporal parameters for neural updates. Also, both the fast- and slow-changing neural activities are modulated by the active motor activities. This model is examined in the driving dataset, recorded in various events, which incorporates the image sequences and the ego-motion of the vehicle. Experiments show that the model encodes the driving scenarios on the higher-level where the neuron recorded the long-term context.

    Original languageEnglish
    Title of host publicationProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
    EditorsSuresh Sundaram
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages160-167
    Number of pages8
    ISBN (Electronic)9781538692769
    DOIs
    Publication statusPublished - 2019 Jan 28
    Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
    Duration: 2018 Nov 182018 Nov 21

    Publication series

    NameProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

    Conference

    Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
    CountryIndia
    CityBangalore
    Period18/11/1818/11/21

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Theoretical Computer Science

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  • Cite this

    Zhong, J., Ogata, T., & Cangelosi, A. (2019). Encoding Longer-term Contextual Sensorimotor Information in a Predictive Coding Model. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 160-167). [8628911] (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628911