title: A Biologically Inspired Computational Model of Basal Ganglia in Action Selection.
abstract: The basal ganglia (BG) are a subcortical structure implicated in action selection. The aim of this work is to present a new cognitive neuroscience model of the BG, which aspires to represent a parsimonious balance between simplicity and completeness. The model includes the 3 main pathways operating in the BG circuitry, that is, the direct (Go), indirect (NoGo), and hyperdirect pathways. The main original aspects, compared with previous models, are the use of a two-term Hebb rule to train synapses in the striatum, based exclusively on neuronal activity changes caused by dopamine peaks or dips, and the role of the cholinergic interneurons (affected by dopamine themselves) during learning. Some examples are displayed, concerning a few paradigmatic cases: action selection in basal conditions, action selection in the presence of a strong conflict (where the role of the hyperdirect pathway emerges), synapse changes induced by phasic dopamine, and learning new actions based on a previous history of rewards and punishments. Finally, some simulations show model working in conditions of altered dopamine levels, to illustrate pathological cases (dopamine depletion in parkinsonian subjects or dopamine hypermedication). Due to its parsimonious approach, the model may represent a straightforward tool to analyze BG functionality in behavioral experiments.
authors.affiliation: Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Baston
authors.affiliation: Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi", University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy.
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
mesh_terms: D000109:Acetylcholine / Q000502:physiology; D000465:Algorithms; D001479:Basal Ganglia / Q000502:physiology*; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D003220:Conflict, Psychological; D003342:Corpus Striatum / Q000502:physiology; D004298:Dopamine / Q000502:physiology; D006801:Humans; D007395:Interneurons / Q000502:physiology; D007858:Learning; D000069550:Machine Learning; D008959:Models, Neurological; D016571:Neural Networks, Computer; D018377:Neurotransmitter Agents / Q000502:physiology; D012201:Reward; D013569:Synapses / Q000502:physiology
chemical_list: D018377:Neurotransmitter Agents; D000109:Acetylcholine; D004298:Dopamine