title: A computational model of Dopamine and Acetylcholine aberrant learning in Basal Ganglia.
abstract: Basal Ganglia (BG) are implied in many motor and cognitive tasks, such as action selection, and have a central role in many pathologies, primarily Parkinson Disease. In the present work, we use a recently developed biologically inspired BG model to analyze how the dopamine (DA) level can affect the temporal response during action selection, and the capacity to learn new actions following rewards and punishments. The model incorporates the 3 main pathways (direct, indirect and hyperdirect) working in BG functioning. The behavior of 2 alternative networks (the first with normal DA levels, the second with reduced DA) is analyzed both in untrained conditions, and during training performed in different epochs. The results show that reduced DA causes delayed temporal responses in the untrained network, and difficult of learning during training, characterized by the necessity of much more epochs. The results provide interesting hints to understand the behavior of healthy and dopamine depleted subjects, such as parkinsonian patients.
authors.forename: Chiara
authors.initials: C
authors.lastname: Baston
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
mesh_terms: D000109:Acetylcholine / Q000378:metabolism*; D001479:Basal Ganglia / Q000502:physiology*; D003198:Computer Simulation; D004298:Dopamine / Q000378:metabolism*; D006801:Humans; D007858:Learning / Q000502:physiology; D008297:Male; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity; D018377:Neurotransmitter Agents / Q000502:physiology; D010300:Parkinson Disease / Q000378:metabolism; D012201:Reward; D013569:Synapses / Q000502:physiology
chemical_list: D018377:Neurotransmitter Agents; D000109:Acetylcholine; D004298:Dopamine