Reference : Neuromorphic reinforcement learning
Scientific congresses and symposiums : Unpublished conference/Abstract
Engineering, computing & technology : Electrical & electronics engineering
Engineering, computing & technology : Multidisciplinary, general & others
Neuromorphic reinforcement learning
Dethier, Julie mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Ernst, Damien [> >]
Sepulchre, Rodolphe [> >]
31st Benelux Meeting on Systems and Control
March 27-29, 2012
The Netherlands
[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.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS

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