Guillermo S. Donatti

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The already introduced Neural Map provides a structural association for the building blocks of dynamically generated object models. Its learning and recall procedures are built upon the Growing Neural Gas algorithm, which is highly parameterized. The values of these parameters are obtained through a time-consuming empirical approach. In the present work, we(More)
We present a so-called Neural Map, a novel memory framework for visual object recognition and categorization systems. The properties of its computational theory include self-organization and intelligent matching of the image features that are used to build their object models. Its performance for representing the visual object knowledge comprised by these(More)
Traditional neural models of the human brain fail to reproduce its complex behavior. In contrast to these models, neural dynamics define their state not only depending on current inputs, but also on previous ones, providing a framework that is able to model these properties more accurately. Neural fields describe the distribution of the activation of neuron(More)
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