Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Cellular, synaptic and network effects of neuromodulation
Neural Networks - Computational models of neuromodulation
EWLR-8 Proceedings of the 8th European Workshop on Learning Robots: Advances in Robot Learning
Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Principles of Brain Functioning: A Synergetic Approach to Brain Activity, Behavior and Cognition
Principles of Brain Functioning: A Synergetic Approach to Brain Activity, Behavior and Cognition
Anticipations, Brains, Individual and Social Behavior: An Introduction to Anticipatory Systems
Anticipatory Behavior in Adaptive Learning Systems
Hi-index | 0.00 |
Regardless of complex, unknown, and dynamically-changing environments, living creatures can recognize situated environments and behave adaptively 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 the environment 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 under various viscous force fields. The parameters of the networks are optimized using genetic algorithms. Simulation results indicate that the proposed model inherits high robustness even though it is situated in unexperienced environments.