Fuzzy Oscillations: a Novel Model for Solving Pattern Segmentation
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In this thesis we develop a novel network model that extends the traditional artificial neural network (ANN) model to include oscillatory behaviour. This model is able to correctly classify combinations of previously learned input patterns by grouping features that belong to the same category. This grouping process is termed segmentation and we show how synchrony of oscillations is the necessary missing component of ANNs to be able to perform this segmentation. Using this model we go on to show that top-down modulatory feedback is necessary to enable separation of multiple objects in a scene and segmentation of their individual features. This type of feedback is distinctly different than recurrency and is what enables the rich dynamics between the nodes of our network. Additionally, we show how our model's dynamics avoid the combinatorial explosion in required training repetitions of traditional feed-forward classification networks. In these networks, relations between objects must explicitly be learned. In contrast, the dynamics of modulatory feedback allow us to defer calculation of these relations until run-time, thus creating a more robust system. We call our model Fuzzy Oscillations, and it achieves good results when compared to existing models. However, oscillatory neural network models successful in achieving segmentation are a relatively recent development. We thus feel that our model is a contribution to the field of oscillatory neural networks.