Norifumi Watanabe

Neural coding model of perceptual reconstruction using the morphoelectrotonic transform theory

We construct the model of the perceptual reconstruction from input visual information. The relations between objects are simulated from the correlation of input pulse and associative memory model in this model. And, the relation and the bind between objects are characterized by the morphoelectrotonic transform based on the experimental data of neurophysiology. The associative memory model is composed of the synchronous associative memory network, and extracts the relations between objects by the phase obtained from input pulse sequence. The binding of the object is reconstructed the relation of the associative memory, and the correlation of the modality is learned.

Neural coding model of associative ontology with up/down state and morphoelectrotonic transform

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.