This article presents an artificial intelligence (AI) architecture intended to simulate the iterative updating of the human working memory system. 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 representational 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). Representations held in persistent activity are recursively replaced resulting in incremental changes to the content of the working memory system. As this content gradually evolves, successive processing states overlap and are continuous with one another. The present article will explore how this architecture can lead to iterative shift in the distribution of coactive representations, ultimately leading to mental continuity between processing states, and thus to human-like thought and cognition. Like the human brain, this AI working memory store will be linked to multiple imagery (topographic map) generation systems corresponding to various sensory modalities. As working memory is iteratively updated, the maps created in response will construct sequences of related mental imagery. Thus, neural networks emulating the prefrontal cortex and its reciprocal interactions with early sensory and motor cortex capture the imagery guidance functions of the human brain. This sensory and motor imagery creation, coupled with an iteratively updated working memory store may provide an AI system with the cognitive assets needed to achieve synthetic consciousness or artificial sentience.

翻译：本文提出一种旨在模拟人类工作记忆系统迭代更新机制的人工智能架构。该架构包含多个相互连接的神经网络，用于模拟大脑皮层的功能化模块。这些模块按层级结构组织，并整合于全局工作空间之中，能够暂时维持高级表征模式——类似于工作记忆中保持的心理项目。这种维持通过两种模式的持续神经活动实现：持续性神经放电（形成注意焦点）与突触增强（形成短时存储）。以持续活动形式保持的表征会被递归替换，从而导致工作记忆系统内容的渐进式变化。随着内容逐渐演变，连续的处理状态相互重叠并保持连续性。本文将探讨该架构如何引发协同激活表征分布的迭代迁移，最终实现处理状态间的思维连续性，从而形成类人的思维与认知能力。与人脑类似，该人工智能工作记忆存储系统将与多个对应不同感觉模态的意象（拓扑图谱）生成系统相连接。随着工作记忆的迭代更新，响应生成的图谱将构建出相关联的心理意象序列。因此，模拟前额叶皮层及其与早期感觉皮层、运动皮层互馈作用的神经网络，能够复现人脑的意象引导功能。这种感觉与运动意象生成机制，结合迭代更新的工作记忆存储系统，可能为人工智能系统提供实现合成意识或人工感知所需的认知基础。