A spiking neural network based on the basal ganglia functional anatomy. (July 2015)

• Record Type: Journal Article
• Authors: Baladron, Javier; Hamker, Fred H.
• Is Part Of: Neural networks. Volume 67(2015:Jul.)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-07
• Subjects: Spiking neurons -- Basal ganglia -- Reinforcement learning -- Cognitive modeling -- Action selection; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.03.002
• Languages: English
• ISSNs: 0893-6080

Abstract

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.