Technical Program

Paper Detail

Paper: PS-2A.22
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: A Study on a Correlation between a Predictive Model of Motion Pictures Imitating the Predictive Coding of the Cerebral Cortex and Brain Activity
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
Authors: Chihiro Fujiyama, Ochanomizu University, Japan; Shinji Nishimoto, Satoshi Nishida, National Institute of Information and Communications Technology, Japan; Hideki Asoh, National Institute of Advanced Industrial Science and Technology, Japan; Ichiro Kobayashi, Ochanomizu University, Japan
Abstract: In recent years, deep neural networks inspired by the notion of predictive coding have been shown to make accurate predictions of future frames. In this study, we focus on a predictive neural network, one of such implementations to evaluate the relationship between natural and artificial neural networks. By using PredNet, a predictive neural architecture, we show that representations extracted from the architecture are correlative with brain activities evoked by natural movie stimuli. Our result gives a verification result on the theoretical hypothesis of predictive coding.