Reinforcement learning in a bio-connectionist model based in the thalamo-cortical neural circuit

Abstract

Abstract In a previous study, we presented a program to simulate a particular dynamic of the thalamo-cortical biological system. The method used was called bio-connectionism which linked the thalamo-cortical mechanism reproduced with animal perception. In this presentation, a reinforcement learning program is supported by this mechanism. In a game world designed to test the model developed, the agent is assigned to a character that must learn by trial and error from its own experience upon recognition of aversive and appetitive patterns. The results confirm, support and extend the notion of configuration, a term familiar with sparse coding principles. If, as it is documented, this mechanism observed in sensory areas can be thought as condition of perception, the brain areas taken together – each in its interaction with a respective sub-thalamic nucleus – are suspected to be considered as condition of cognition. We introduce some philosophical questions derived from the experimental results in the discussion section.