Titlebook: Connectionist Models of Learning, Development and Evolution; Proceedings of the S Robert M. French,Jacques P. Sougné Conference proceedings

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Recognition of Novelty Made Easy: Constraints of Channel Capacity on Generative Networks effective information transfer in the brain. Robust and fast information flow processing methods warranting efficient information transfer, e.g. grouping of inputs and information maximization principles need to be applied. For this reason, indepent component analyses on groups of patterns were con

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Developing Knowledge about Living Things: A Connectionist Investigationata shows differences in the rate at which children acquire subcategories of living things, differences in the timing of changes in knowledge organisation, and changes in the distribution of feature types children use to represent their knowledge. The connectionist model was developed to investigate

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Paying Attention to Relevant Dimensions: A Localist Approach stimulus dimensions are irrelevant to the classification task in hand. A procedure is suggested by which a localist model can learn prototype representations that foeus on the relevant dimensions only. These permit good generalization which would be lacking in a simple exemplar-based model.

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Modelling Cognitive Development with Constructivist Neural Networksnomena. This point is empirically investigated with a constructivist neural network model of the acquisition of past tense/particip1e inflections. The model dynamically adapts its architecture to the leaming task by growing units and connections in a task-specific way during learning. In contrast to

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實現(xiàn)

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Visual Crowding and Category-Specific Deficits: a Neural Network Modeltegories are distinct. In a series of experiments a Kohonen self organizing feature map was trained to recognise 2D digitised images. As a result, images of animals and musical instruments were represented within a shared set of processing units, which suggests that they are visually crowded categor