Neuromodulation in a learning robot: interactions between neural plasticity and behavior

We investigate the effects of behavioral activity on the development of patterns of synaptic connections in a model of neuromodulation embedded in an autonomous robot. Robot behavior produces clustered spatial distributions of rewarding objects in environments initially containing a high density of uniformly distributed objects. The spatial clustering of the rewarding objects restricts robot movements to sectors of high reward density, alters patterns of connections signaling predictions about reward timing, and changes the temporal profile of interactions between the robot and the objects. These effects are outcomes of embodied behavioral activity and are not pre-programmed or externally controlled. We discuss our results in the context of the reciprocal coupling of neural dynamics, behavioral activity and stimulus distributions.