Abstract
We propose a new coding model to the associative ontology that based on result of association experiment to person. The semantic network with the semantic distance on the words is constructed on the neural network and the association relation is expressed by using the up and down states. The associative words are changing depending on the context and the words with the polysemy and the homonym solve vagueness in self organization by using the up and down states. In addition, the relation of new words is computed depending on the context by morphoelectrotonic transform theory. In view of these facts, the simulation model of dynamic cell assembly on neural network depending on the context and word sense disambiguation is constructed.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Watanabe, N., Ishizaki, S. (2005). Neural Coding Model of Associative Ontology with Up/Down State and Morphoelectrotonic Transform. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_138
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DOI: https://doi.org/10.1007/11550907_138
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