Department of Electrical and Electronic Engineering, Imperial College London, London, UK,
Neuroinformatics. 2014 Jan;12(1):63-91. doi: 10.1007/s12021-013-9200-7.
The mirror neuron system in primates matches observations of actions with the motor representations used for their execution, and is a topic of intense research and debate in biological and computational disciplines. In robotics, models of this system have been used for enabling robots to imitate and learn how to perform tasks from human demonstrations. Yet, existing computational and robotic models of these systems are found in multiple levels of description, and although some models offer plausible explanations and testable predictions, the difference in the granularity of the experimental setups, methodologies, computational structures and selected modeled data make principled meta-analyses, common in other fields, difficult. In this paper, we adopt an interdisciplinary approach, using the BODB integrated environment in order to bring together several different but complementary computational models, by functionally decomposing them into brain operating principles (BOPs) which each capture a limited subset of the model's functionality. We then explore links from these BOPs to neuroimaging and neurophysiological data in order to pinpoint complementary and conflicting explanations and compare predictions against selected sets of neurobiological data. The results of this comparison are used to interpret mirror system neuroimaging results in terms of neural network activity, evaluate the biological plausibility of mirror system models, and suggest new experiments that can shed light on the neural basis of mirror systems.
灵长类动物的镜像神经元系统将动作观察与用于执行动作的运动表现相匹配，是生物和计算学科中研究和争论的热点话题。在机器人学中，该系统的模型已被用于使机器人能够模仿和学习人类示范如何执行任务。然而，现有的这些系统的计算和机器人模型存在于多个描述层次中，尽管一些模型提供了合理的解释和可测试的预测，但实验设置、方法、计算结构和所选建模数据的粒度差异使得在其他领域中常见的原则性元分析变得困难。在本文中，我们采用跨学科的方法，使用 BODB 集成环境将几个不同但互补的计算模型结合在一起，通过将它们功能分解为脑操作原理 (BOP)，每个 BOP 捕获模型功能的有限子集。然后，我们探索这些 BOP 与神经影像学和神经生理学数据之间的联系，以确定互补和冲突的解释，并将预测与选定的神经生物学数据集进行比较。比较的结果用于根据神经网络活动解释镜像系统神经影像学结果，评估镜像系统模型的生物学合理性，并提出可以阐明镜像系统神经基础的新实验。
