The concept of connectionism states that higher cognitive functions emerge from the interaction of many simple elements. Accordingly, research on canonical microcircuits conceptualizes findings on fundamental neuroanatomical circuits as well as recurrent organizational principles of the cerebral cortex and examines the link between architectures and their associated functionality. In this study, we establish minimal canonical microcircuit models as elements of hierarchical processing networks. Based on a combination of descriptive time simulations and explanatory state-space mappings, we show that minimal canonical microcircuits effectively segregate feedforward and feedback information flows and that feedback information conditions basic processing operations in minimal canonical microcircuits. Further, we derive and examine two prototypical meta-circuits of cooperating minimal canonical microcircuits for the neurocognitive problems of priming and structure building. Through the application of these findings to a language network of syntax parsing, this study embodies neurocognitive research on hierarchical communication in light of canonical microcircuits, cell assembly theory, and predictive coding.

中文翻译：

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从规范微电路模型的层次交互作用中出现了认知启动和结构构建。

连接主义的概念指出，较高的认知功能来自许多简单元素的相互作用。因此，对规范微电路的研究将基本神经解剖电路以及大脑皮层的周期性组织原理的发现概念化，并检查了体系结构及其相关功能之间的联系。在这项研究中，我们建立了最小的规范微电路模型作为分层处理网络的元素。基于描述性的时间模拟和说明性的状态空间映射的组合，我们表明最小的规范微电路有效地隔离了前馈和反馈信息流，并且反馈信息在最小的规范微电路中限制了基本的处理操作。进一步，我们推导并研究了针对最小启动和结构构建的神经认知问题的协作最小规范微电路的两个原型元电路。通过将这些发现应用于语法分析的语言网络，本研究体现了根据规范的微电路，细胞组装理论和预测编码进行的层次通信的神经认知研究。