[en] Living organisms are able to successfully perform challeng- ing tasks such as perception, classification, association, and control. In hope for similar successes in artificial systems, neuromorphic engineering uses neurophysiological models of perception and information processing in biological sys- tems to emulate their functions but also resemble their struc- ture [1]. In this abstract, we focus on the basal ganglia (BG), brain region in control of primitive functions of the nervous system, and specifically on their involvement in action selec- tion and reinforcement learning (RL). We hypothesize that neuromorphic-inspired systems will greatly benefit the RL community.
