Abstract
TAM (topographic attentive mapping) network is a biologically-motivated neural network with receptive fields of Gabor function. However, the receptive field's layer is monotype, and there is a lack of performance for rotating visual images. In this paper, we formulate four TAM networks with multilayer structure of extensive receptive fields. We discuss their performance and show the usefulness of TAM network using some examples of character recognition.
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