Developmental learning with behavioral mode tuning by carrier-frequency modulation in coherent neural networks.

We propose a developmental learning architecture with which a motion-control system learns multiple tasks similar to each other or advanced ones incrementally and efficiently by tuning its behavioral mode. The system is based on a coherent neural network whose carrier frequency works as a mode-tuning parameter. In our experiments, we consider two tasks related to bicycle riding. The first is to ride as temporally long as the system can before it falls down (task 1). The second is an advanced one, i.e., to ride as far as possible in a certain direction (task 2). We compare developmental learning to learn task 2 after task 1 with the direct learning of task 2. We also examine the effect of the mode tuning by comparing variable-mode learning (VML), where the carrier frequency is set free to move, with fixed-mode learning (FML), where the frequency is unchanged. We find that VML developmental learning results in the most efficient learning among the possible combinations. We discuss the effects of the incremental task assignment as well as the behavioral mode tuning in developmental learning.