Presentation 2014-06-27
A model for biological motion detection based on motor prediction in the dorsal premotor area
Yuji KAWAI, Minoru ASADA, Yukie NAGAI,
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Abstract(in English) Recent findings about an activation of the dorsal premotor area (PMd) during observation of smooth biological movements suggest that this motor-related area detects biological motions. We hypothesize that a neural network in the PMd acquires an invariance of biological motions from time-series motor commands self-movements and thus interprets the observed smooth trajectories as biological ones based on the invariance. Our simulation shows that predictive learning allows a recurrent neural network to represent the invariance of biological kinematics and thus to detect the biological motions. This result agrees with the fact that the PMd originally functions as a motor predictor. We also show that this neural network can recognize the ankle and wrist trajectories of walking human as biological regardless of the subject's sex and emotional state.
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Keyword(in English) biological motion / 1/3 power law / prediction learning / recurrent network / dorsal premotor area
Paper # NC2014-12,IBISML2014-12
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Committee IBISML
Conference Date 2014/6/18(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) A model for biological motion detection based on motor prediction in the dorsal premotor area
Sub Title (in English)
Keyword(1) biological motion
Keyword(2) 1/3 power law
Keyword(3) prediction learning
Keyword(4) recurrent network
Keyword(5) dorsal premotor area
1st Author's Name Yuji KAWAI
1st Author's Affiliation Graduate School of Engineering, Osaka University()
2nd Author's Name Minoru ASADA
2nd Author's Affiliation Graduate School of Engineering, Osaka University
3rd Author's Name Yukie NAGAI
3rd Author's Affiliation Graduate School of Engineering, Osaka University
Date 2014-06-27
Paper # NC2014-12,IBISML2014-12
Volume (vol) vol.114
Number (no) 105
Page pp.pp.-
#Pages 6
Date of Issue