Stepping out of the box: information processing in the neural networks of the basal ganglia.
The Albin-DeLong 'box and arrow' model has long been the accepted standard model for the basal ganglia network. However, advances in physiological and anatomical research have enabled a more detailed neural network approach. Recent computational models hold that the basal ganglia use reinforcement signals and local competitive learning rules to reduce the dimensionality of sparse cortical information. These models predict a steady-state situation with diminished efficacy of lateral inhibition and low synchronization. In this framework, Parkinson's disease can be characterized as a persistent state of negative reinforcement, inefficient dimensionality reduction, and abnormally synchronized basal ganglia activity.
Bar-Gad
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Department of Physiology, the Center for Neural Computation and the Eric Roland Center for Neurodegenerative Diseases, The Hebrew University, Hadassah Medical School, Jerusalem 91120, Israel.
Bergman
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H
D000818:Animals; D001479:Basal Ganglia / Q000502:physiology* / Q000503:physiopathology; D002540:Cerebral Cortex / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D008606:Mental Processes / Q000502:physiology*; D008959:Models, Neurological; D009069:Movement Disorders / Q000503:physiopathology; D009415:Nerve Net / Q000502:physiology* / Q000503:physiopathology