大脑皮层层流计算

S. Grossberg
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引用次数: 0

摘要

大脑皮层在所有感觉和认知模式中计算最高形式的生物智能。新皮层细胞被组织成回路,在所有皮层区域形成六个皮层层,进行感知和认知。这些层中细胞特性的变化以及它们之间的联系被用来将大脑皮层划分为50多个分区或区域,这些分区或区域具有不同的功能。然而,为什么大脑皮层有层流组织来控制行为,直到最近才成为一个谜。同样神秘的是,这种无处不在的层状皮层设计的变化如何产生如此多不同类型的智能行为。本章解释层流计算如何促进生物智能,以及新皮层细胞的分层电路如何支持各种高阶生物智能,包括视觉、语言和认知,使用相同的规范层流电路的变体。该规范电路可用于通用VLSI芯片,可专门用于执行不同类型的生物智能,并无缝连接在一起以控制自主自适应算法和移动机器人。这些回路显示了预先注意的自底向上的自动处理和注意的任务选择性自顶向下的处理是如何在皮层的深层连接在一起形成决策接口的。在这里,自下而上和自上而下的约束相互合作和竞争,通过结合快速前馈和反馈处理、模拟和数字计算、预注意和注意学习的特性,包括层流ART特性,如模拟相干性,来产生最佳决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Laminar Computing by Cerebral Cortex
The cerebral cortex computes the highest forms of biological intelligence in all sensory and cognitive modalities. Neocortical cells are organized into circuits that form six cortical layers in all cortical areas that carry out perception and cognition. Variations in cell properties within these layers and their connections have been used to classify the cerebral cortex into more than fifty divisions, or areas, to which distinct functions have been attributed. Why the cortex has a laminar organization for the control of behavior has, however, remained a mystery until recently. Also mysterious has been how variations on this ubiquitous laminar cortical design can give rise to so many different types of intelligent behavior. This chapter explains how Laminar Computing contributes to biological intelligence, and how layered circuits of neocortical cells support all the various kinds of higher-order biological intelligence, including vision, language, and cognition, using variations of the same canonical laminar circuit. This canonical circuit can be used in general-purpose VLSI chips that can be specialized to carry out different kinds of biological intelligence, and seamlessly joined together to control autonomous adaptive algorithms and mobile robots. These circuits show how preattentive automatic bottom-up processing and attentive task-selective top-down processing are joined together in the deeper cortical layers to form a decision interface. Here, bottom-up and top-down constraints cooperate and compete to generate the best decisions, by combining properties of fast feedforward and feedback processing, analog and digital computing, and preattentive and attentive learning, including laminar ART properties such as analog coherence.
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