Solving the Binding Problem with Separated Extraction of Information by Oscillatory Self-organizing Neural Models

抄録

As a solution of the binding problem with separated extraction of information from the several objects, there is a hypothesis that the property binding of information seems to be represented by phase-locking among neuronal oscillatory firing. We introduce this synchronous firing hypothesis into the self-organizing maps, or SOMs algorithm. Here we propose oscillatory self-organizing neural models. By utilizing the computer simulation results, we report that our models could separate and extract information of the shapes and the colors from two simultaneous inputs without the binding problem.