| Modeling cortex of the brain and building brain-like intelligent systems has been the focus of significant research efforts over many years. This dissertation discusses a novel approach to modeling the cortical information processing underlying cognition and intelligence of the human brain which focuses on a paradigm that is different than that used in the traditional algorithm-based AI models. By considering the cortical column as the functional processing element, the cortex can be modeled in the aggregate level. The resulting model is a Parametrically Coupled Logistic Map network (PCLMN) which emulates the thalamo-cortical information processing. Coupling such a model with a suitable associative memory creates an unprecedented, albeit, abstract model of a cortical module that could facilitate understanding the human brain and eventually building an artificial brain. |
