Machine operant conditioning

This research investigates learning of machine reflexes by applying punishment and reward reinforcement to teach artificial neuronlike systems a prescribed behavior. Stochastic neuronlike elements based on the classical weighted sum of inputs and threshold model can learn stimulus-response associations by emulated classical Pavlovian conditioning, i.e. make associations between conditioned and unconditioned stimuli and later responses. Several mathematical models have been developed which apply abstractions of classical conditioning to such threshold logic devices. Temporal sequences of stimulus-response associations can be dynamically learned by using operant conditioning when only aggregate external reinforcement is available.<>