Combining Supervised, Unsupervised, and Reinforcement Learning in a Network of Spiking Neurons

Abstract

The human brain constantly learns via mutiple different learning strategies. It can learn by simply having stimuli being presented to its sensory organs which is considered unsupervised learning. In addition, it can learn associations between inputs and outputs when a teacher provides the output which is considered as supervised learning. Most importantly, it can learn very efficiently if correct behaviour is followed by reward and/or incorrect behaviour is followed by punishment which is considered reinforcement learning. So far, most artificial neural architectures implement only one of the three learning mechanisms — even though the brain integrates all three. Here, we have implemented unsupervised, supervised, and reinforcement learning within a network of spiking neurons. In order to achieve this ambitious goal, the existing learning rule called spike-timing-dependent plasticity had to be extended such that it is modulated by the reward signal dopamine.