A Hubel Wiesel Model of Early Concept Generalization Based on Local Correlation of Input Features

Abstract

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. If we assume that the brain uses a unique Hubel Wiesel like architecture to represent the input information of any modality, it is important to account for the local correlation of conceptual inputs as an equivalent to the existing local correlation of visual inputs in the visual counterpart models. However, there is no intuitive local correlation among the conceptual inputs. The key contribution of this thesis is the proposal of an input integration framework that accounts for the local correlation of the conceptual inputs in a Hubel Wiesel like architecture to facilitate the achievement of broad and coherent concept categories at the top of the hierarchy. The building blocks of our model are two algorithms: 1) Bottom-up hierarchical learning algorithm, and 2) Input integration framework. The first algorithm handles the process of categorization in a modular and hierarchical manner that benefits from competitive unsupervised learning in its modules. The second algorithm consists of a set of operations over the input features or modules to weigh them as general or specific to specify how they should be locally correlated within the modules of the hierarchy. Furthermore, the input integration framework interferes with the process of similarity measurement applied by the first algorithm such that, high-weighted features would count more than the low-weighted features towards the similarity of conceptual patterns. Simulation results on benchmark data admit that implementing the proposed input integration framework facilitates the achievement of the broadest coherent distinctions of conceptual patterns. Achieving such categorizations is a quality that our model shares with the process of early concept generalization. Finally, we applied our proposed model of early concept generalization 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.