Developmental Learning Based on Coherent Neural Networks with Behavioral Mode Tuning by Carrier-Frequency Modulation

We analyze the developmental-learning dynamics 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 functions as a mode-tuning parameter. We consider two tasks related to bicycle riding, i.e., to ride as temporally long as the system can (task 1) and to ride as far as possible in a certain direction (task 2) which is an advanced one. 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.