AFA-PredNet: The Action Modulation Within Predictive Coding

Junpei Zhong*, Angelo Cangelosi, Xinzheng Zhang, Tetsuya Ogata


研究成果: Conference contribution

4 被引用数 (Scopus)


The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception. Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner. Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals. In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition. In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction. Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way. In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment.

ホスト出版物のタイトル2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2018 10月 10
イベント2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
継続期間: 2018 7月 82018 7月 13


名前Proceedings of the International Joint Conference on Neural Networks


Other2018 International Joint Conference on Neural Networks, IJCNN 2018
CityRio de Janeiro

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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