Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism.

Dopamine (DA) depletion in the basal ganglia (BG) of Parkinson's patients gives rise to both frontal-like and implicit learning impairments. Dopaminergic medication alleviates some cognitive deficits but impairs those that depend on intact areas of the BG, apparently due to DA ''overdose.'' These findings are difficult to accommodate with verbal theories of BG/DA function, owing to complexity of system dynamics: DA dynamically modulates function in the BG, which is itself a modulatory system. This article presents a neural network model that instantiates key biological properties and provides insight into the underlying role of DA in the BG during learning and execution of cognitive tasks. Specifically, the BG modulates the execution of ''actions'' (e.g., motor different parts of the frontal cortex. Phasic changes in DA, which occur during error feedback, dynamically modulate the BG threshold for facilitating/suppressing a cortical command in response to particular stimuli. Reduced dynamic range of DA explains Parkinson and DA overdose deficits with a single underlying dysfunction, despite overall differences in raw DA levels. Simulated Parkinsonism and medication effects provide a theoretical basis for behavioral data in probabilistic classification and reversal tasks. The model also provides novel testable predictions for neuropsychological and pharmacological studies, and motivates further investigation of BG/DA interactions with the prefrontal cortex in working memory.

Pubmed ID: 15701239


  • Frank MJ


Journal of cognitive neuroscience

Publication Data

January 9, 2005

Associated Grants


Mesh Terms

  • Basal Ganglia
  • Brain Chemistry
  • Cognition Disorders
  • Dopamine
  • Feedback
  • Humans
  • Models, Neurological
  • Neural Inhibition
  • Neural Networks (Computer)
  • Nonlinear Dynamics
  • Parkinson Disease
  • Probability