This article presents an artificial intelligence (AI) architecture intended
to simulate the human working memory system as well as the manner in which it
is updated iteratively. It features several interconnected neural networks
designed to emulate the specialized modules of the cerebral cortex. These are
structured hierarchically and integrated into a global workspace. They are
capable of temporarily maintaining high-level patterns akin to the
psychological items maintained in working memory. This maintenance is made
possible by persistent neural activity in the form of two modalities: sustained
neural firing (resulting in a focus of attention) and synaptic potentiation
(resulting in a short-term store). This persistent activity is updated
iteratively resulting in incremental changes to the content of the working
memory system. As the content stored in working memory gradually evolves,
successive states overlap and are continuous with one another. The present
article will explore how this architecture can lead to gradual shift in the
distribution of coactive representations, ultimately leading to mental
continuity between processing states, and thus to human-like cognition.

本文介绍了一种人工智能（AI）体系结构，旨在模拟人类工作记忆系统及其迭代更新方式。此体系结构包括多个相互连接的神经网络，旨在模拟大脑皮层的专门模块。它们具有层次结构并集成到全局工作区中。通过持续的神经活动，它们能够暂时维持类似于工作记忆中维护的心理模式。本文将探讨如何通过此体系结构逐渐转换协同表示的分布，最终导致处理状态之间的心理连续性，从而导致类似人类的认知。