Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment.
Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment.
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DOI：
10.1371/journal.pcbi.1000220
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发表时间：
2008-11
影响因子：
4.3
通讯作者：
Tani, Jun
中科院分区：
文献类型：
--
作者：
Yamashita, Yuichi;Tani, Jun
一般认为，人类的熟练行为源于运动控制系统的功能层次结构，其中可重复使用的运动原语被灵活地集成到各种感觉运动序列模式中。然而，控制连续的感觉运动流被分割成基元的方式以及一系列基元被整合成各种行为序列的方式的潜在神经机制尚未得到澄清。在早期的研究中，这种功能层次结构已经实现了通过使用显式的层次结构，与本地模块表示电机原语在较低的水平和较高的模块表示通过额外的机制，如门选择切换的原语序列。然而，当序列包含相似性和重叠时，在这种早期的模型中，由于这种分离的模块结构，泛化和分割之间产生了冲突。为了解决这个问题，我们提出了一种不同类型的神经网络模型。现有的模型既没有使用单独的局部模块来表示原语，也没有引入显式的层次结构。功能层次结构不是通过将架构层次结构强加给系统，而是通过一种基于两种不同类型的神经元的自组织形式出现，每种神经元都具有不同的时间属性（“多时间尺度”）。通过引入多个时间尺度，连续的行为序列被分割成可重用的原语，原语，反过来，灵活地集成到新的序列。在实验中，所提出的网络模型，协调的人形机器人的身体，通过高维的感觉运动控制，也成功地将自己定位在一个物理环境。我们的研究结果表明，它不仅是神经元之间的空间连接，而且神经活动的时间尺度，作为重要的机制，导致在神经系统的功能层次。神经系统中的功能层次，被定义为复杂实体可以被分割成更简单的元素以及简单元素可以被整合到复杂实体中的原则，是神经科学中具有挑战性的研究领域。这样一个功能层次可以直观地被认为是两种方式：空间层次和时间层次。空间层次的一个例子是视觉信息处理，其中狭窄感受野中的元素信息被整合到更大空间中视觉图像的复杂特征中。听觉信息处理是时间层次的一个例子，在听觉信息处理中，短时间窗口内的音节级信息被整合到较长时间窗口内的单词级信息中。虽然大量的研究已经阐明了空间层级的神经机制，但支配时间层级的机制还不清楚。在当前的研究中，我们证明了功能层次结构可以在神经活动的多个时间尺度上进行自组织，而无需显式的空间层次结构。我们的研究结果表明，多个时间尺度是导致神经系统功能层次出现的一个重要因素。这项工作可能有助于提供线索，在没有空间层次结构的情况下，这种层次结构令人困惑的观察。
It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (“multiple timescales”). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems. Functional hierarchy in neural systems, defined as the principle that complex entities may be segmented into simpler elements and that simple elements may be integrated into a complex entity, is a challenging area of study in neuroscience. Such a functional hierarchy may be thought of intuitively in two ways: as hierarchy in space, and as hierarchy in time. An example of hierarchy in space is visual information processing, where elemental information in narrow receptive fields is integrated into complex features of a visual image in a larger space. Hierarchy in time is exemplified by auditory information processing, where syllable-level information within a short time window is integrated into word-level information over a longer time window. Although extensive investigations have illuminated the neural mechanisms of spatial hierarchy, those governing temporal hierarchy are less clear. In the current study, we demonstrate that functional hierarchy can self-organize through multiple timescales in neural activity, without explicit spatial hierarchical structure. Our results suggest that multiple timescales are an essential factor leading to the emergence of functional hierarchy in neural systems. This work could contribute to providing clues regarding the puzzling observation of such hierarchy in the absence of spatial hierarchical structure.
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影响因子：
4.8
作者：
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DOI：
10.1098/rstb.2005.1622
发表时间：
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影响因子：
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作者：
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