A Neuro-Mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization. (arXiv:2310.15177v1 [q-bio.NC])

Over the last few years, large neural generative models, capable of
synthesizing intricate sequences of words or producing complex image patterns,
have recently emerged as a popular representation of what has come to be known
as “generative artificial intelligence” (generative AI). Beyond opening the
door to new opportunities as well as challenges for the domain of statistical
machine learning, the rising popularity of generative AI brings with it
interesting questions for Cognitive Science, which seeks to discover the nature
of the processes that underpin minds and brains as well as to understand how
such functionality might be acquired and instantiated in biological (or
artificial) substrate. With this goal in mind, we argue that a promising
long-term pathway lies in the crafting of cognitive architectures, a
long-standing tradition of the field, cast fundamentally in terms of
neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive
Neural GENerative system, which is an architecture that casts the Common Model
of Cognition in terms of Hebbian adaptation operating in service of optimizing
a variational free energy functional.