Abstract
We proposes an autonomous dynamical pattern recognition and learning system. It is demonstrated that, first, when the embedded pattern, i.e., known pattern, is given to the network, the firing pattern of the network immediately goes to the relevant embedded pattern and the network state reduces to the oscillatory state at once. Second, when no embedded pattern, i.e., unknown pattern, is given to the network, the network state oscillates chaotically. It is considered as “I don't know” state. Finally, when Hebb rule is applied to the network under the external stimuli that are unknown patterns, the internal state of the network is inversely bifurcated from the chaotic state to the periodic state according to the progress of learning. By utilizing this phase transition as an index of the progress of learning, the network can learn new patterns without any external observers.
