The fields of neuroscience and artificial intelligence have, over many decades, respectively, established a huge collection of observed biological facts, and developed an extensive set of theories regarding the processing of information. However, despite this tremendous expenditure of intellectual capital, the daunting complexity of the brain leaves humanity at the dawn of the 21st century with an almost complete lack of understanding of how the brain represents, stores, and processes information. Beginning with Aristotle, philosophers and, more recently psychologists have suggested that association forms the fundamental cognitive processing operation. However, there is no proposed underlying mathematical basis for investigating the brain's operation.

This talk will present the first vestiges of a theory of information processing based on the (limited) observed operation of the thalamacortex, and information theory. Three novel associative memory neural network architectures will be presented, along with a set of experiments from the field of linguistics which demonstrate a system capable of performing some identified cognitive functions: the formation of abstract categories, the ability to combine various sets of information of differing types, context sensitive prediction, and the ability to determine answers consistent with all assumed facts and all knowledge bases.