Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity
This work addresses the challenge of creating biologically inspired AI for synthetic consciousness, but it appears incremental as it builds on existing concepts of working memory and neural networks without presenting new empirical results.
The paper tackles the problem of simulating human working memory and mental continuity by proposing an AI architecture with interconnected neural networks that maintain representations through persistent neural activity, aiming to achieve human-like thought and cognition.
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. Taken together, these components outline a biologically motivated route toward synthetic consciousness or artificial sentience and subjectivity.