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一种典型的层状新皮质回路,其自下而上、水平和自上而下的通路控制注意力、学习和预测。

A Canonical Laminar Neocortical Circuit Whose Bottom-Up, Horizontal, and Top-Down Pathways Control Attention, Learning, and Prediction.

作者信息

Grossberg Stephen

机构信息

Graduate Program in Cognitive and Neural Systems, Departments of Mathematics and Statistics, Psychological and Brain Sciences, and Biomedical Engineering, Center for Adaptive Systems, Boston University, Boston, MA, United States.

出版信息

Front Syst Neurosci. 2021 Apr 23;15:650263. doi: 10.3389/fnsys.2021.650263. eCollection 2021.

DOI:10.3389/fnsys.2021.650263
PMID:33967708
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8102731/
Abstract

All perceptual and cognitive circuits in the human cerebral cortex are organized into layers. Specializations of a canonical laminar network of bottom-up, horizontal, and top-down pathways carry out multiple kinds of biological intelligence across different neocortical areas. This article describes what this canonical network is and notes that it can support processes as different as 3D vision and figure-ground perception; attentive category learning and decision-making; speech perception; and cognitive working memory (WM), planning, and prediction. These processes take place within and between multiple parallel cortical streams that obey computationally complementary laws. The interstream interactions that are needed to overcome these complementary deficiencies mix cell properties so thoroughly that some authors have noted the difficulty of determining what exactly constitutes a cortical stream and the differences between streams. The models summarized herein explain how these complementary properties arise, and how their interstream interactions overcome their computational deficiencies to support effective goal-oriented behaviors.

摘要

人类大脑皮层中的所有感知和认知回路都被组织成层状结构。由自下而上、水平和自上而下通路构成的典型层状网络的特化,在不同的新皮层区域执行多种生物智能。本文描述了这个典型网络是什么,并指出它可以支持诸如三维视觉和图形-背景感知、注意力类别学习和决策、语音感知以及认知工作记忆(WM)、规划和预测等不同的过程。这些过程发生在多个并行的皮层流内部和之间,这些皮层流遵循计算互补定律。为克服这些互补缺陷所需的流间相互作用将细胞特性混合得非常彻底,以至于一些作者指出很难确定究竟什么构成皮层流以及流之间的差异。本文总结的模型解释了这些互补特性是如何产生的,以及它们的流间相互作用如何克服其计算缺陷以支持有效的目标导向行为。

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