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
Country/TerritoryIndia
CityBangalore
Period18/11/1818/11/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science

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