A template matching model for pattern recognition: self-organization of templates and template matching by a disinhibitory neural network.

A template matching model for pattern recognition is proposed. By following a previously-proposed algorithm for synpatic modification (Hirai, 1980), the template of a stimulus pattern is self-organized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is perfomed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory neural network that receives the disinhibitory input from that cell becomes selectively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also show that the model can organize a clean template under a noisy environment.