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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation
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Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

机译:利用平运动神经元的增益调节机制:在视运动转换和具体化仿真中的应用

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The so-called self-other correspondence problem in imitation demands to find the transformation that maps the motor dynamics of one partner to our own. This requires a general purpose sensorimotor mechanism that transforms an external fixation-point (partner's shoulder) reference frame to one's own body-centered reference frame. We propose that the mechanism of gain-modulation observed in parietal neurons may generally serve these types of transformations by binding the sensory signals across the modalities with radial basis functions (tensor products) on the one hand and by permitting the learning of contextual reference frames on the other hand. In a shoulder-elbow robotic experiment, gain-field neurons (GF) intertwine the visuo-motor variables so that their amplitude depends on them all. In situations of modification of the body-centered reference frame, the error detected in the visuo-motor mapping can serve then to learn the transformation between the robot's current sensorimotor space and the new one. These situations occur for instance when we turn the head on its axis (visual transformation), when we use a tool (body modification), or when we interact with a partner (embodied simulation). Our results defend the idea that the biologically-inspired mechanism of gain modulation found in parietal neurons can serve as a basic structure for achieving nonlinear mapping in spatial tasks as well as in cooperative and social functions. (C) 2014 Elsevier Ltd. All rights reserved.
机译:模仿中的所谓“自我-他人”对应问题要求找到一种转换,该转换将一个伙伴的运动动力学映射到我们自己。这需要通用的感觉运动机制,该机制将外部注视点(伴侣的肩膀)参考系转换为以自己的身体为中心的参考系。我们提出,在壁神经元中观察到的增益调节机制通常可通过一方面将跨模态的感觉信号与径向基函数(张量积)绑定,并允许通过学习上下文参考框架来为这些类型的转换服务。另一方面。在肩肘机器人实验中,增益场神经元(GF)将视觉运动变量交织在一起,使得它们的振幅完全取决于它们。在修改以人体为中心的参考系的情况下,在可见运动标测中检测到的错误可以用来学习机器人当前的感觉运动空间与新的感觉运动空间之间的转换。例如,当我们绕其轴旋转头部(视觉转换),使用工具(车身修改)或与伙伴互动(嵌入式仿真)时,就会发生这些情况。我们的结果捍卫了这样一种观念,即在顶神经元中发现了生物学启发的增益调节机制,可以作为在空间任务以及合作和社交功能中实现非线性映射的基本结构。 (C)2014 Elsevier Ltd.保留所有权利。

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