Neural mechanisms for learning hierarchical structures of information
Neural mechanisms for learning hierarchical structures of information
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DOI：
10.1016/j.conb.2021.10.011
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发表时间：
2021-10-01
影响因子：
5.7
通讯作者：
Haga, Tatsuya
中科院分区：
文献类型：
--
作者：
Fukai, Tomoki;Asabuki, Toshitake;Haga, Tatsuya
来自环境的空间和时间信息通常是分层组织的，我们对环境的知识也是如此。识别嵌入在分层结构信息中的有意义的片段对于认知功能至关重要，包括视觉，听觉，运动，记忆和语言处理。分割使得能够抓住孤立实体之间的联系，为推理和思考提供基础。重要的是，大脑在没有外部指令的情况下学习这种分割。在这里，我们回顾了在单细胞和网络水平上实现的基本计算机制。网络级机制与用于图分割的机器学习方法具有有趣的相似性。大脑可能在其处理层次的多个级别上实现用于分析环境的层次结构的方法。
Spatial and temporal information from the environment is often hierarchically organized, so is our knowledge formed about the environment. Identifying the meaningful segments embedded in hierarchically structured information is crucial for cognitive functions, including visual, auditory, motor, memory, and language processing. Segmentation enables the grasping of the links between isolated entities, offering the basis for reasoning and thinking. Importantly, the brain learns such segmentation without external instructions. Here, we review the underlying computational mechanisms implemented at the single-cell and network levels. The network-level mechanism has an interesting similarity to machine-learning methods for graph segmentation. The brain possibly implements methods for the analysis of the hierarchical structures of the environment at multiple levels of its processing hierarchy.