ELI-chan for intermediate features

We propose E_LI-chan, an emergent local indicator mechanism, to model the representation and self-organization of intermediate features in the visual pathway. This model is motivated by the orientation specificity in the primary visual cortex. Our simulations of E_LI-chan demonstrate that local indicators of the locations of intermediate features emerge, and they become the seeds for unsupervised learning and pattern recognition; E_LI-chan predicts those portions of the input imagery where intermediate features potentially exist. Hence, E_LI-chan can be used to define a set of intermediate features for adaptation, and the onwards processing in a hierarchical pattern recognition system.