Analysis of Cortical Connectivity Using Hopfield Neural Network

There is striking similarity in the connectivity between perceptrons in an Artificial Neural Network and neurons in brain. Therefore it is a natural logical step to investigate cortical connectivity using Artificial Neural Networks. Present approaches to ascertaining cortical connectivity, e.g. Structural Equation Modeling between various regions of interest (ROI) in the active brain are-tedious and time-consuming . For example, modeling the connectivity of a large number of brain regions often involves numerous parameter changes to achieve a good fit. Functional Magnetic Resonance Imaging (fMRI) is increasing recognized as a standard technique for brain mapping. This study explores the utility of a Hopfield Neural Network to determine cortical connectivity in an fMRI data set