Article | Published:

Neural signals of extinction in the inhibitory microcircuit of the ventral midbrain

Nature Neuroscience volume 16, pages 7178 (2013) | Download Citation

Abstract

Midbrain dopaminergic (DA) neurons are thought to guide learning via phasic elevations of firing in response to reward predicting stimuli. The mechanism for these signals remains unclear. Using extracellular recording during associative learning, we found that inhibitory neurons in the ventral midbrain of mice responded to salient auditory stimuli with a burst of activity that occurred before the onset of the phasic response of DA neurons. This population of inhibitory neurons exhibited enhanced responses during extinction and was anticorrelated with the phasic response of simultaneously recorded DA neurons. Optogenetic stimulation revealed that this population was, in part, derived from inhibitory projection neurons of the substantia nigra that provide a robust monosynaptic inhibition of DA neurons. Thus, our results elaborate on the dynamic upstream circuits that shape the phasic activity of DA neurons and suggest that the inhibitory microcircuit of the midbrain is critical for new learning in extinction.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Dopamine neurons and their role in reward mechanisms. Curr. Opin. Neurobiol. 7, 191–197 (1997).

  2. 2.

    , & A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).

  3. 3.

    Predictive reward signal of dopamine neurons. J. Neurophysiol. 80, 1–27 (1998).

  4. 4.

    & Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli. Nature 379, 449–451 (1996).

  5. 5.

    , & Dopamine responses comply with basic assumptions of formal learning theory. Nature 412, 43–48 (2001).

  6. 6.

    , & Dopamine neurons encode the better option in rats deciding between differently delayed or sized rewards. Nat. Neurosci. 10, 1615–1624 (2007).

  7. 7.

    & Lateral habenula as a source of negative reward signals in dopamine neurons. Nature 447, 1111–1115 (2007).

  8. 8.

    , & Coding of predicted reward omission by dopamine neurons in a conditioned inhibition paradigm. J. Neurosci. 23, 10402–10410 (2003).

  9. 9.

    , , & Tripartite mechanism of extinction suggested by dopamine neuron activity and temporal difference model. J. Neurosci. 28, 9619–9631 (2008).

  10. 10.

    , , & Dopamine cells respond to predicted events during classical conditioning: evidence for eligibility traces in the reward-learning network. J. Neurosci. 25, 6235–6242 (2005).

  11. 11.

    The phasic reward signal of primate dopamine neurons. Adv. Pharmacol. 42, 686–690 (1998).

  12. 12.

    et al. Reward-predictive cues enhance excitatory synaptic strength onto midbrain dopamine neurons. Science 321, 1690–1692 (2008).

  13. 13.

    & Two types of dopamine neuron distinctly convey positive and negative motivational signals. Nature 459, 837–841 (2009).

  14. 14.

    , , , & Negative reward signals from the lateral habenula to dopamine neurons are mediated by rostromedial tegmental nucleus in primates. J. Neurosci. 31, 11457–11471 (2011).

  15. 15.

    , , & Regulation of firing of dopaminergic neurons and control of goal-directed behaviors. Trends Neurosci. 30, 220–227 (2007).

  16. 16.

    , & GABAergic afferents activate both GABAA and GABAB receptors in mouse substantia nigra dopaminergic neurons in vivo. J. Neurosci. 28, 10386–10398 (2008).

  17. 17.

    & GABAergic control of substantia nigra dopaminergic neurons. Prog. Brain Res. 160, 189–208 (2007).

  18. 18.

    , & GABAA receptor–mediated inhibition of rat substantia nigra dopaminergic neurons by pars reticulata projection neurons. J. Neurosci. 15, 3092–3103 (1995).

  19. 19.

    & Pedunculopontine tegmental nucleus controls conditioned responses of midbrain dopamine neurons in behaving rats. J. Neurosci. 25, 4725–4732 (2005).

  20. 20.

    & The short-latency dopamine signal: a role in discovering novel actions? Nat. Rev. Neurosci. 7, 967–975 (2006).

  21. 21.

    , & Distinct tonic and phasic anticipatory activity in lateral habenula and dopamine neurons. Neuron 67, 144–155 (2010).

  22. 22.

    , , & The pars reticulata of the substantia nigra: a window to basal ganglia output. Prog. Brain Res. 160, 151–172 (2007).

  23. 23.

    , & Mechanisms of action selection and timing in substantia nigra neurons. J. Neurosci. 32, 5534–5548 (2012).

  24. 24.

    , , , & Neuron type–specific signals for reward and punishment in the ventral tegmental area. Nature 482, 85–88 (2012).

  25. 25.

    , & Behavior-related modulation of substantia nigra pars reticulata neurons in rats performing a conditioned reinforcement task. Neuroscience 111, 337–349 (2002).

  26. 26.

    , , & Impact of expected value on neural activity in rat substantia nigra pars reticulata. Eur. J. Neurosci. 33, 2308–2317 (2011).

  27. 27.

    et al. Absence of NMDA receptors in dopamine neurons attenuates dopamine release, but not conditioned approach, during Pavlovian conditioning. Proc. Natl. Acad. Sci. USA 107, 13491–13496 (2010).

  28. 28.

    , & Responses of monkey dopamine neurons to reward and conditioned stimuli during successive steps of learning a delayed response task. J. Neurosci. 13, 900–913 (1993).

  29. 29.

    et al. Stereological estimates of dopaminergic, GABAergic and glutamatergic neurons in the ventral tegmental area, substantia nigra and retrorubral field in the rat. Neuroscience 152, 1024–1031 (2008).

  30. 30.

    & Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

  31. 31.

    , & Statistics of midbrain dopamine neuron spike trains in the awake primate. J. Neurophysiol. 98, 1428–1439 (2007).

  32. 32.

    The Rat Nervous System, 3rd edn. (Elsevier, 2004).

  33. 33.

    et al. Segregation and convergence of information flow through the cortico-subthalamic pathways. J. Neurosci. 21, 5764–5772 (2001).

  34. 34.

    & The Inferior Colliculus (Springer, 2005).

  35. 35.

    et al. Short-latency visual input to the subthalamic nucleus is provided by the midbrain superior colliculus. J. Neurosci. 29, 5701–5709 (2009).

  36. 36.

    & Cholinergic, GABAergic and glutamate-enriched inputs from the mesopontine tegmentum to the subthalamic nucleus in the rat. J. Neurosci. 15, 7105–7120 (1995).

  37. 37.

    , & The role of the pedunculopontine tegmental nucleus in relation to conditioned motor performance in the cat. II. Effects of reversible inactivation by intracerebral microinjections. Exp. Brain Res. 121, 411–418 (1998).

  38. 38.

    , & Adult plasticity in multisensory neurons: short-term experience-dependent changes in the superior colliculus. J. Neurosci. 29, 15910–15922 (2009).

  39. 39.

    Behavioral dopamine signals. Trends Neurosci. 30, 203–210 (2007).

  40. 40.

    , & Dopamine in motivational control: rewarding, aversive and alerting. Neuron 68, 815–834 (2010).

  41. 41.

    , , & The inhibitory influence of the lateral habenula on midbrain dopamine cells: ultrastructural evidence for indirect mediation via the rostromedial mesopontine tegmental nucleus. J. Comp. Neurol. 519, 1143–1164 (2011).

  42. 42.

    , & Patterns of axonal branching of neurons of the substantia nigra pars reticulata and pars lateralis in the rat. J. Comp. Neurol. 492, 349–369 (2005).

  43. 43.

    et al. How visual stimuli activate dopaminergic neurons at short latency. Science 307, 1476–1479 (2005).

  44. 44.

    & The pedunculopontine tegmental nucleus: where the striatum meets the reticular formation. Prog. Neurobiol. 47, 1–29 (1995).

  45. 45.

    , , & Dichotomous dopaminergic control of striatal synaptic plasticity. Science 321, 848–851 (2008).

  46. 46.

    Conditioned Reflexes: an Investigation of the Physiological Activity of the Cerebral Cortex (Oxford University Press, 1927).

  47. 47.

    , , & FLEX switch targets Channelrhodopsin-2 to multiple cell types for imaging and long-range circuit mapping. J. Neurosci. 28, 7025–7030 (2008).

  48. 48.

    & Dynamics of membrane excitability determine interspike interval variability: a link between spike generation mechanisms and cortical spike train statistics. Neural Comput. 10, 1047–1065 (1998).

Download references

Acknowledgements

J. Paton, W. Denk, A. Karpova, A. Lee, J. Magee and G. Murphy provided critical feedback at various stages of preparation of the manuscript and progression of the project. We are indebted to the extensive feedback from our colleagues following presentation of this work at internal seminars on the Janelia Farm Research Campus. We thank members of our laboratory for critical reading and feedback on the manuscript. J.B. receives funding from the Cambridge-Janelia Farm Graduate Program. This work was supported by funding from the Howard Hughes Medical Institute.

Author information

Affiliations

  1. Howard Hughes Medical Institute, Janelia Farm Research Campus, Ashburn, Virginia, USA.

    • Wei-Xing Pan
    • , Jennifer Brown
    •  & Joshua Tate Dudman
  2. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.

    • Jennifer Brown

Authors

  1. Search for Wei-Xing Pan in:

  2. Search for Jennifer Brown in:

  3. Search for Joshua Tate Dudman in:

Contributions

W.-X.P. and J.T.D. designed the project. J.T.D., W.-X.P. and J.B. analyzed the data and wrote the manuscript. W.-X.P. performed the in vivo recording and behavioral experiments. J.B. performed the in vitro experiments. J.T.D. implemented the computational model and performed a minority of the experiments.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Joshua Tate Dudman.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–10

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nn.3283

Further reading