A Hubel Wiesel model of early concept generalization based on local correlation of input features

Hubel Wiesel models, successful in visual processing algorithms, have only recently been used in conceptual representation. Despite the biological plausibility of a Hubel-Wiesel like architecture for conceptual memory and encouraging preliminary results, there is no implementation of how inputs at each layer of the hierarchy should be integrated for processing by a given module, based on the correlation of the features. In our paper, we propose the input integration framework - a set of operations performed on the inputs to the learning modules of the Hubel Wiesel model of conceptual memory. These operations weight the modules as being general or specific and therefore determine how modules can be correlated when fed to parents in the higher layers of the hierarchy. Parallels from Psychology are drawn to support our proposed framework. Simulation results on benchmark data show that implementing local correlation corresponds to the process of early concept generalization to reveal the broadest coherent distinctions of conceptual patterns. Finally, we applied the improved model iteratively over two sets of data, which resulted in the generation of finer grained categorizations, similar to progressive differentiation. Based on our results, we conclude that the model can be used to explain how humans intuitively fit a hierarchical representation for any kind of data.