Neuromorphic motivated systems

Abstract

Although reinforcement learning has been extensively modeled, few agent models that incorporate values use biologically plausible neural networks as a uniform computational architecture. We call biologically plausible neural network architecture neuromorphic. This paper discusses some theoretical constraints on neuromorphic intrinsic value systems [3]. By intrinsic, we mean a value system that is likely programmed by the genes, whose value bias has already taken a shape at the birth time. Such an intrinsic value system plays an important role in developing extrinsic values through the agent's own experience during its life span. Based on our theoretical constraints, we model two types of neurotransmitters, serotonin and dopamine, to construct a neuromorpic intrinsic value system based on a uniform neural network architecture. Serotonin represents punishment and stress, while dopamine represents reward and pleasure. Experimentally, this model allows our simulated robot to develop an attachment to one entity and fear another.