Behavior control of coherent-type neural networks by carrier-frequency modulation

Abstract

Coherent-type artificial neural networks whose behavior is controlled by carrier-frequency modulation are proposed. The network learns teacher signals associated with an information-carrier frequency as a network parameter. The total network system forms a self-homodyne circuit. The learning process is realized by adjusting delay time and conductance of neural connections. Experiments demonstrate that the network behavior is successfully controlled by the carrier-frequency modulation. This result will be applicable not only to signal processing but also to frequency-multiplexed optical neural computing and quantum neural devices such as carrier-energy-controlled neurons in the future.