Neuromodulation and plasticity in an autonomous robot.

In this paper we implement a computational model of a neuromodulatory system in an autonomous robot. The output of the neuromodulatory system acts as a value signal, modulating widely distributed synaptic changes. The model is based on anatomical and physiological properties of midbrain diffuse ascending systems, in particular parts of the dopamine and noradrenaline systems. During reward conditioning, the model learns to generate tonic and phasic signals that represent predictions and prediction errors, including precisely timed negative signals if expected rewards are omitted or delayed. We test the robot's learning and behavior in different environmental contexts and observe changes in the development of the neuromodulatory system that depend upon environmental factors. Simulation of a computational model incorporating both reward-related and aversive stimuli leads to the emergence of conditioned reward and aversive behaviors. These studies represent a step towards investigating computational aspects of neuromodulatory systems in autonomous robots.