Computational models of the basal ganglia.
Computer simulation studies and mathematical analysis of models of the basal ganglia are being used increasingly to explore theories of basal ganglia function. We review the implications of these new models for a general understanding of basal ganglia function in normal as well as in diseased brains. The focus is on their functional similarities rather than on the details of mathematical methodologies and simulation techniques. Most of the models suggest a vital role for the basal ganglia in learning. Although this interest in learning is partly driven by experimental results associating the acute firing of dopamine cells with reward prediction in monkeys, some of the models have preceded the electrophysiological results. Another common theme of the models is selection. In this case, the striatum is seen as detecting and selecting cortical contexts for access to basal ganglia output. Although the behavioral consequences of this selection are hard to define, the models provide frameworks within which to explore these ideas empirically. This provides a means of refining our understanding of basal ganglia function and to consider dysfunction within the new logical frameworks.
Gillies
A
A
Institute for Adaptive and Neural Computation, The University of Edinburgh, UK.
Arbuthnott
G
G
D000818:Animals; D001479:Basal Ganglia / Q000502:physiology* / Q000503:physiopathology; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D004298:Dopamine / Q000378:metabolism / Q000502:physiology*; D006801:Humans; D007266:Inhibition, Psychological; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net; D010300:Parkinson Disease / Q000503:physiopathology; D012054:Reinforcement, Psychology
D004298:Dopamine