Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity



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.

Like the human brain, the working memory store will be linked to multiple topographic map generation systems of various sensory modalities. As working memory is iteratively updated, the maps created in response will amount to sequences of related mental imagery. This system couples these components by embedding them within a multilayered neural network of pattern recognizing nodes. Nodes low in the hierarchy are trained to recognize and represent sensory features and are capable of combining individual features or patterns into composite, topographical maps or images. Nodes high in the hierarchy are multimodal and have a capacity for sustained activity allowing the maintenance of pertinent, high-level features through elapsing time. The higher-order nodes select new features from each mapping to add to the store of temporarily maintained features. This updated set of features are fed back into lower-order sensory nodes where they are continually used to guide the construction of successive topographic maps. Thus, neural networks emulating the prefrontal cortex and its 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 produce generalized intelligence.