What are the computations of the cerebellum, the basal ganglia and the cerebral cortex?

Doya, Kenji

doi:10.1016/s0893-6080(99)00046-5

Cited by 659 publications

(539 citation statements)

References 87 publications

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“…Recently, it has been speculated that certain cerebellar functions that can be mimicked with internal model approaches influence dopamine neuron activity (Doya, 1999). However, the term 'internal model' in the context of the cerebellum is defined differently than in the current paper (Kawato & Gomi, 1992).…”

Section: Internal Model Approachesmentioning

confidence: 90%

TD models of reward predictive responses in dopamine neurons

Suri

2002

Neural Networks

116

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This article focuses on recent modeling studies of dopamine neuron activity and their influence on behavior. Activity of midbrain dopamine neurons is phasically increased by stimuli that increase the animal's reward expectation and is decreased below baseline levels when the reward fails to occur. These characteristics resemble the reward prediction error signal of the temporal difference (TD) model, which is a model of reinforcement learning. Computational modeling studies show that such a dopamine-like reward prediction error can serve as a powerful teaching signal for learning with delayed reinforcement, in particular for learning of motor sequences.Several lines of evidence suggest that dopamine is also involved in 'cognitive' processes that are not addressed by standard TD models. I propose the hypothesis that dopamine neuron activity is crucial for planning processes, also referred to as 'goal-directed behavior', which select actions by evaluating predictions about their motivational outcomes. q

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Section: Internal Model Approachesmentioning

confidence: 90%

TD models of reward predictive responses in dopamine neurons

Suri

2002

Neural Networks

116

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“…Many brain systems are involved in motor learning. A recent model of motor learning [67] assumes that the cerebellum is specialized for supervised learning, the basal ganglia subserves reinforcement learning, and the cerebral cortex implements unsupervised learning. In the model, sequential procedures are acquired independently by two cortical systems, one using spatial coordinates and another using motor coordinates in the early and late stages of learning, respectively [68].…”

Section: Motor Learning and Adaptationmentioning

confidence: 99%

Computational approaches to motor control

Flash

Sejnowski²

2001

Current Opinion in Neurobiology

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New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.

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“…In fact, a person performs and memorizes a set of movements more or less similar to the correct movement, improving performance on the basis of practiced motor experience. Doya [78] suggested that different areas of the brain (the cerebellum, the basal ganglia and the cortex) are involved in the process of movement learning through their cellular architecture. According to known computational models, each brain structure might implement three different learning paradigms, which are: Supervised Learning, Reinforcement Learning, and Unsupervised Learning [78,79].…”

Section: Motor Control and Learningmentioning

confidence: 99%