Frontiers in Computational Neuroscience (May 2010)
A computational model of basal ganglia and its role in memory retrieval in rewarded visual memory tasks
Abstract
Visual working memory tasks involve a network of cortical areas such as inferotemporal, medial temporal and prefrontal cortices. We suggest here to investigate the role of the basal ganglia in the learning of delayed rewarded tasks through the selective gating of thalamocortical loops. We designed a computational model of the visual loop linking the perirhinal cortex, the basal ganglia and the thalamus, biased by sustained representations in prefrontal cortex. This model learns concurrently different delayed rewarded tasks that require to maintain a visual cue and to associate it to itself or to another visual object to obtain reward. The retrieval of visual information is achieved through thalamic stimulation of the perirhinal cortex. The input structure of the basal ganglia, the striatum, learns to represent visual information based on its association to reward, while the output structure, the substantia nigra pars reticulata, learns to link striatal representations to the disinhibition of the correct thalamocortical loop. In parallel, a dopaminergic cell learns to associate striatal representations to reward and modulates learning of connections within the basal ganglia. The model provides testable predictions about the behavior of several areas during such tasks, while providing a new functional organization of learning within the basal ganglia, putting emphasis on the learning of the striatonigral connections as well as the lateral connections within the substantia nigra pars reticulata. It suggests that the learning of visual working memory tasks is achieved rapidly in the basal ganglia and used as a teacher for feedback connections from prefrontal cortex to posterior cortices.
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