Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the action-understanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives.

中文翻译：

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模拟镜像神经元中目标特异性的发展。

神经生理学研究表明，顶叶镜像神经元不仅编码动作，还编码这些动作的目标。尽管一些镜像神经元会在感知到某个动作时（与目标无关）触发，但大多数只会在动作被感知为具有特定目标的动作的一部分时触发。这个结果对于行动理解假设很重要因为它为这种认知能力提供了潜在的神经学基础。它还与人工认知系统的设计有关，特别是机器人系统，它们在与其他代理的交互中依赖于镜像系统的计算模型。然而，迄今为止，还没有计算模型明确解决产生特定目标和独立于目标的顶叶镜像神经元的机制。在本文中，我们提出了一个基于自组织图的计算模型，该模型接收人工输入，这些输入表示有关观察或执行的动作以及执行它们的上下文的信息。我们表明该地图发展了一个生物学上合理的组织，其中出现了特定于目标的镜像神经元。我们进一步表明，可以在地图不同输入之间的几何关系中找到外观和目标特定神经元数量的根本原因。结果对动作理解假设很重要，因为它们为特定目标的顶叶镜像神经元的出现提供了一种机制，并导致了许​​多预测：（1）新目标的学习可能主要重新分配现有的目标特定神经元，而不是招募新人；(2) 执行和观察到的动作之间的输入差异可以解释观察到的特定目标神经元数量的相应差异；(3) 目标特定神经元的百分比可能因运动原语而异。结果对动作理解假设很重要，因为它们为特定目标的顶叶镜像神经元的出现提供了一种机制，并导致了许​​多预测：（1）新目标的学习可能主要重新分配现有的目标特定神经元，而不是招募新人；(2) 执行和观察到的动作之间的输入差异可以解释观察到的特定目标神经元数量的相应差异；(3) 目标特定神经元的百分比可能因运动原语而异。结果对动作理解假设很重要，因为它们为特定目标的顶叶镜像神经元的出现提供了一种机制，并导致了许​​多预测：（1）学习新目标可能主要是重新分配现有的特定目标神经元，而不是招募新人；(2) 执行和观察到的动作之间的输入差异可以解释观察到的特定目标神经元数量的相应差异；(3) 目标特定神经元的百分比可能因运动原语而异。(2) 执行和观察到的动作之间的输入差异可以解释观察到的特定目标神经元数量的相应差异；(3) 目标特定神经元的百分比可能因运动原语而异。(2) 执行和观察到的动作之间的输入差异可以解释观察到的特定目标神经元数量的相应差异；(3) 目标特定神经元的百分比可能因运动原语而异。