Dynamical Neural Schmitt Trigger for Robot Control
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Synergies Between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation
Reflex-oscillations in evolved single leg neurocontrollers for walking machines
Natural Computing: an international journal
Adaptive behavior control with self-regulating neurons
50 years of artificial intelligence
Flexible and multistable pattern generation by evolving constrained plastic neurocontrollers
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Artificial homeostatic system: a novel approach
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Neural control of a modular multi-legged walking machine: Simulation and hardware
Robotics and Autonomous Systems
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Synaptic plasticity for recurrent neural networks is derived by introducing neurons as self-regulating units. These neurons have homeostatic properties for certain parameter domains. Depending on its underlying connectivity a neurocontroller endowed with the derived synaptic plasticity rule can generate a variety of different behaviors. The structure of these networks can be developed by evolutionary techniques. For demonstration, examples are given generating a walking behavior for a 3-joint single leg of a walking machine.