A Neuromodulatory Neural Networks Model for Environmental Cognition and Motor Adaptation

Regardless of complex, unknown, and dynamically-changing environments, living creatures can recognize situated environments and behave adaptively by theirselves in real-time. However it is impossible to prepare optimal motion trajectories with respect to every possible situations in advance. The key concept for realizing suitable environmental cognition and motor adaptation is a context-based elicitation of constraints which are canalizing well-suited sensorimotor coordination. For this aim, in this study, we propose a polymorphic neural networks model called CTRNN+NM (CTRNN with neuromodulatory bias). The proposed model is applied to two dimensional arm-reaching movement control in various viscous curl force fields. The model parameters were optimized by GA. Simulation results reveal that the proposed model inherits high robustness even though it is situated in unexperienced environment, which has same curl but different size of viscous force, since it evolved "how to adapt" instead of "how to move.".