4.8 Article

Neuron-type-specific signals for reward and punishment in the ventral tegmental area

Journal

NATURE
Volume 482, Issue 7383, Pages 85-U109

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/nature10754

Keywords

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Funding

  1. Howard Hughes Medical Institute from the Helen Hay Whitney Foundation
  2. Human Frontiers Science Program
  3. Howard Hughes Medical Institute
  4. Smith Family New Investigator Award
  5. Alfred Sloan Foundation
  6. Milton Fund
  7. [F32 DK078478]
  8. [P30 DK046200]
  9. [R01 DK075632]
  10. [R01 DK089044]
  11. [P30 DK057521]

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Dopamine has a central role in motivation and reward. Dopaminergic neurons in the ventral tegmental area (VTA) signal the discrepancy between expected and actual rewards (that is, reward prediction error)(1-3), but how they compute such signals is unknown. We recorded the activity of VTA neurons while mice associated different odour cues with appetitive and aversive outcomes. We found three types of neuron based on responses to odours and outcomes: approximately half of the neurons (type I, 52%) showed phasic excitation after reward-predicting odours and rewards in a manner consistent with reward prediction error coding; the other half of neurons showed persistent activity during the delay between odour and outcome that was modulated positively (type II, 31%) or negatively (type III, 18%) by the value of outcomes. Whereas the activity of type I neurons was sensitive to actual outcomes (that is, when the reward was delivered as expected compared to when it was unexpectedly omitted), the activity of type II and type III neurons was determined predominantly by reward-predicting odours. We 'tagged' dopaminergic and GABAergic neurons with the light-sensitive protein channelrhodopsin-2 and identified them based on their responses to optical stimulation while recording. All identified dopaminergic neurons were of type I and all GABAergic neurons were of type II. These results show that VTA GABAergic neurons signal expected reward, a key variable for dopaminergic neurons to calculate reward prediction error.

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