title: A spiking neural network based on the basal ganglia functional anatomy.
abstract: We introduce a spiking neural network of the basal ganglia capable of learning stimulus-action associations. We model learning in the three major basal ganglia pathways, direct, indirect and hyperdirect, by spike time dependent learning and considering the amount of dopamine available (reward). Moreover, we allow to learn a cortico-thalamic pathway that bypasses the basal ganglia. As a result the system develops new functionalities for the different basal ganglia pathways: The direct pathway selects actions by disinhibiting the thalamus, the hyperdirect one suppresses alternatives and the indirect pathway learns to inhibit common mistakes. Numerical experiments show that the system is capable of learning sets of either deterministic or stochastic rules.
authors: Department of Computer Science, Chemnitz University of Technology, Strasse der Nationen 62, 09107 Chemnitz, Germany.
authors: Javier
authors: J
authors: Baladron
authors: Department of Computer Science, Chemnitz University of Technology, Strasse der Nationen 62, 09107 Chemnitz, Germany. Electronic address: fred.hamker@informatik.tu-chemnitz.de.
authors: Fred H
authors: FH
authors: Hamker
mesh_terms: D001479:Basal Ganglia / Q000033:anatomy & histology* / Q000378:metabolism / Q000502:physiology*; D004298:Dopamine / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D000069550:Machine Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D013788:Thalamus / Q000502:physiology
chemical_list: D004298:Dopamine
keywords: Action selection; Basal ganglia; Cognitive modeling; Reinforcement learning; Spiking neurons