学习摇摇摇铃的神经机器人模型

Forrest Yeh, A. Warlaumont, Y. Chen, Timothy M. Shea, B. Stark
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引用次数: 0

摘要

奖励调节的Hebbian学习是一种生物学上合理的神经学习机制,以前已应用于各种学习任务。例如,最近的研究使用奖励调节的峰值时间依赖可塑性(STDP)来帮助解释婴儿如何学习产生音节咿呀学语[1]。这个项目试图将这种学习机制扩展到婴儿运动发展的一个新领域,摇晃拨浪鼓。该实验利用神经脉冲序列来调节机械臂关节点周围正弦运动的频率。当连接在机械臂上的拨浪鼓发出的声音的音量(定义为均方根(RMS)振幅)超过最近试验的均方根时,给予强化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neurorobotic model of learning to shake a rattle
Reward-modulated Hebbian learning is a biologically plausible neural learning mechanism that has been previously applied to a variety of learning tasks. For example, recent work used reward-modulated spike timing dependent plasticity (STDP) to help explain how infants learn to produce syllabic babbling [1]. This project attempts to extend this learning mechanism to a new domain of infant motor development, shaking a rattle. The experiment transduces neural spike trains to adjust frequency of sinusoidal movement around a robotic arm's articulation point. Reinforcement given when the volume, defined as the root mean square (RMS) amplitude, of sound made by a rattle attached to the robot arm exceeded the mean RMS of recent trials.
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