Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions

Adaptation to inversion of the visual eld is studied in a simple simulated model of phototactic behaviour. Inspired by recent ndings in neuroscience, a novel neural architecture based on continuous dynamical neural networks is implemented. Individual cells behave homeostatically by facilitating local plasticity whenever their activity goes out of bounds. Robots are evolved to perform long-term phototaxis on a series of light sources while trying to keep neurons behaving homeostatically. Robots are then tested under the condition of left/right inversion of vision. Initially, their phototactic capability is lost, which in most cases causes neurons to lose homeostasis and trigger plastic changes. After long periods of maladaptation, robots adapt to the new sensorimotor situation, and phototactic behaviour is recovered. The introduction of other disruptions such as radical perturbations to motor and sensor gains also results in eventual adaptation. The model intends to bring Ashbyan ideas on the relation between adaption and internal stability into the context of current research on evolutionary robotics. In spite of the promising preliminary results, a series of unsolved questions are raised by this model. Possible solutions are suggested.