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首页> 外文会议>The Fourth joint IEEE international conferences on development and learning and epigenetic robotics >A model for biological motion detection based on motor prediction in the dorsal premotor area
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A model for biological motion detection based on motor prediction in the dorsal premotor area

机译：基于运动预测的背运动前区生物运动检测模型

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Recent findings regarding dorsal premotor area (PMd) activation 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 self-induced motor commands for smooth movements and interprets the observed biological motions as ones satisfying the invariance in self-movements. To verify our hypothesis, we developed a recurrent neural network (RNN) to be trained with smooth motor movements, and examined how the RNN acquires biological invariance. The results showed that predictive learning of the RNN contributed to invariance acquisition, which enabled it to detect biological motions. Our findings agree with the fact that the PMd originally functions as a motor predictor. Moreover, this RNN could judge the ankle and wrist trajectories of a walking human as biological regardless of the subject's sex and emotional state.
机译：在观察平滑的生物运动过程中有关背运动前区域（PMd）激活的最新发现表明，该运动相关区域可检测到生物运动。我们假设PMd中的神经网络获取了平滑运动的自感应运动命令的不变性，并将所观察到的生物运动解释为满足自我运动不变性的运动。为了验证我们的假设，我们开发了一个递归神经网络（RNN）进行平滑运动运动的训练，并研究了RNN如何获得生物学不变性。结果表明，RNN的预测学习有助于不变性的获取，从而使它能够检测生物运动。我们的发现与以下事实相吻合，即PMd最初是运动预测因子。而且，该RNN可以将行走中的人的踝关节和腕部轨迹判断为生物学的，而与受试者的性别和情绪状态无关。

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