  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.
