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Abstract：
Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model\'s internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of \"shared\" neurons responding to all category instances-the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances [\"proper names\" (PN)] or symbols related to classes of instances sharing common features [\"category terms\" (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies.
摘要：
语言影响认知和概念加工，但是在人脑中实现这种因果效应的机制仍然未知。这里,我们使用类别形成和符号学习的大脑约束深度神经网络模型，并在神经电路层面分析了紧急模型内部机制。在一组模拟中，该网络具有类似的神经活动索引模式，属于相同类别的对象和动作的实例。生物现实的Hebbian学习导致了分布在网络多个区域的特定于实例的神经元的形成，and,此外,到响应所有类别实例的\'共享\'神经元的单元组装电路-网络与概念类别相关。在两组独立的模拟中，网络学习了相同的模式以及单个实例的符号（\'专有名称\'）或与共享共同特征的实例类别相关的符号（\'类别术语\'）。学习类别术语显著增加了网络中共享神经元的数量，从而使类别表示更健壮，同时减少实例特定的神经元的数量。相比之下,专有名称学习可防止实例特异性神经元的大量减少，并阻止一般类别细胞的过度生长。表征相似度分析进一步证实，在类别项学习之后，类别实例的神经活动模式变得更加相似，相对于有专有名称和没有任何符号的学习。这些基于网络的概念机制，专有名称和类别术语解释了符号学习为什么以及如何改变对象感知和记忆，正如实验研究所揭示的那样。重要性声明特定个体（MickyMouse）和对象类别（housemouse）的言语符号如何因果关系地影响概念表示和处理？类别术语和专有名称已被证明分别促进类别形成和实例学习，潜在地通过分别将注意力引导到类别关键和特定于对象的特征上。然而，这些观察在神经回路水平上的潜在机制仍然未知。使用受人脑特性约束的数学精确深度神经网络模型，我们展示了类别术语学习加强和巩固概念表示，而专有名称支持特定于对象的机制。基于网络内部机制和无监督的基于相关的学习，这项工作为符号学习对概念形成的因果效应提供了神经生物学解释，人类大脑中的类别构建和实例表示。
