Evaluating neuromodulator-controlled stochastic plasticity for learning recurrent neural control networks

Abstract

Learning recurrent neural networks as behavior controllers for robots requires measures to guide the learning towards a desired behavior. Organisms in nature solve this problem with feedback signals to assess their behavior and to refine their actions. In line with this, a neural framework is developed where the synaptic learning is controlled by artificial neuromodulators that are produced in response to (undesired) sensory signals. To test this framework and to get a base line to evaluate further approaches, we perform five classical benchmark experiments with a simple random plasticity method. We show that even with this simple plasticity method, behaviors can already be found for all experiments, even for comparably large networks with over 90 plastic synapses. The performance depends strongly on the complexity of the task and less on the chosen network topology. This suggests that controlling learning with neuromodulators is a viable approach that is promising to work also with more sophisticated plasticity methods in the future.