Modeling dopamine and serotonin systems in a visual recognition network

Many studies have been performed to train a classification network using supervised learning. In order to enable a recognition network to learn autonomously or to later improve its recognition performance through simpler confirmation or rejection, it is desirable to model networks that have an intrinsic motivation system. Although reinforcement learning has been extensively studied, much of the existing models are symbolic whose internal nodes have preset meanings from a set of handpicked symbolic set that is specific for a given task or domain. Neural networks have been used to automatically generate internal (distributed) representations. However, modeling a neuromorphic motivational system for neural networks is still a great challenge. By neuromorphic, we mean that the motivational system for a neural network must be also a neural network, using a standard type of neuronal computation and neuronal learning. This work proposes a neuromorphic motivational system, which includes two subsystems - the serotonin system and the dopamine system. The former signals a large class of stimuli that are intrinsically aversive (e.g., stress or pain). The latter signals a large class of stimuli that are intrinsically appetitive (e.g., sweet and pleasure). We experimented with this motivational system for visual recognition settings to investigate how such a system can learn through interactions with a teacher, who does not give answers, but only punishments and rewards.