Neuro-Dynamics in a Modular Brain Structure

This paper describes a modular structure for the E-Sense Artificial Intelligence system, where each module can provide a different type of functionality. The modules are complicated and algorithmic, but they can be distinguished by fairly basic neuronal functionality. The paper elucidates further this biological grounding for the structures and shows how the modules complement each other. A general trait is converting signals into types. While type-based may seem obvious, this paper gives the biological context. The 3-level architecture is still essential, with a semantic layer between each level, resulting in coherence and juxtapositions that may help to drive the processes. The middle-level ontology is also shown to be good at separating data merged in the database, allowing content from original documents to be retrieved again. The lower-level database sequences, linked with the upper-level functions, appears to resemble a Kolmogorov-Shannon theory suggested in an earlier paper. Thus, similar structures and processes, but in slightly different contexts, has been able to provide a wider variety of dynamics and increase the knowledge content. Basic test results support some of the work.