Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion
Joint Authors
Ogata, Tetsuya
Zhong, Junpei
Cangelosi, Angelo
Zhang, Xinzheng
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-13
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Studies suggest that, within the hierarchical architecture, the topological higher level possibly represents the scenarios of the current sensory events with slower changing activities.
They attempt to predict the neural activities on the lower level by relaying the predicted information after the scenario of the sensorimotor event has been determined.
On the other hand, the incoming sensory information corrects such prediction of the events on the higher level by the fast-changing novel or surprising signal.
From this point, we propose a predictive hierarchical artificial neural network model that examines this hypothesis on neurorobotic platforms.
It integrates the perception and action in the predictive coding framework.
Moreover, in this neural network model, there are different temporal scales of predictions existing on different levels of the hierarchical predictive coding architecture, which defines the temporal memories in recording the events occurring.
Also, both the fast- and the slow-changing neural activities are modulated by the motor action.
Therefore, the slow-changing neurons can be regarded as the representation of the recent scenario which the sensorimotor system has encountered.
The neurorobotic experiments based on the architecture were also conducted.
American Psychological Association (APA)
Zhong, Junpei& Cangelosi, Angelo& Ogata, Tetsuya& Zhang, Xinzheng. 2018. Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion. Complexity،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1135868
Modern Language Association (MLA)
Zhong, Junpei…[et al.]. Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion. Complexity No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1135868
American Medical Association (AMA)
Zhong, Junpei& Cangelosi, Angelo& Ogata, Tetsuya& Zhang, Xinzheng. Encoding Longer-Term Contextual Information with Predictive Coding and Ego-Motion. Complexity. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1135868
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135868