Diversity and Homogeneity in Responses of Midbrain Dopamine Neurons
Open accessAbstract
Dopamine neurons of the ventral midbrain have been found to signal a reward prediction error that can mediate positive reinforcement. Despite the demonstration of modest diversity at the cellular and molecular levels, there has been little analysis of response diversity in behaving animals. Here we examine response diversity in rhesus macaques to appetitive, aversive, and neutral stimuli having relative motivational values that were measured and controlled through a choice task. First, consistent with previous studies, we observed a continuum of response variability and an apparent absence of distinct clusters in scatter plots, suggesting a lack of statistically discrete subpopulations of neurons. Second, we found that a group of “sensitive” neurons tend to be more strongly suppressed by a variety of stimuli and to be more strongly activated by juice. Third, neurons in the “ventral tier” of substantia nigra were found to have greater suppression, and a subset of these had higher baseline firing rates and late “rebound” activation after suppression. These neurons could belong to a previously identified subgroup of dopamine neurons that express high levels of H-type cation channels but lack calbindin. Fourth, neurons further rostral exhibited greater suppression. Fifth, although we observed weak activation of some neurons by aversive stimuli, this was not associated with their aversiveness. In conclusion, we find a diversity of response properties, distributed along a continuum, within what may be a single functional population of neurons signaling reward prediction error.
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Recent reports that distinguished between different stimulus components of punishers suggest that the physical impact of punishers is the major determinant for dopamine activations, and the psychophysically assessed aversive nature of the punishers did not explain the recorded dopamine activations.
“However, recent reports that distinguished between different stimulus components of punishers suggest that the physical impact of punishers is the major determinant for dopamine activations, and the psychophysically assessed aversive nature of the punishers did not explain the recorded dopamine activations25–27.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [26]
Dopamine neurons in medial and lateral, or dorsal and ventral, midbrain positions show graded differences in responses to rewarding and aversive stimuli.
“Dopamine neurons in medial and lateral, or dorsal and ventral, midbrain positions do show graded differences in responses to rewarding and aversive stimuli2,23,24,26,33.”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [26]
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Cites
Predictive reward signal of dopamine neurons
Schultz, W. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.80.1.1 · Cited by 4,564 · see citing works
Dopamine in motivational control: rewarding, aversive, and alerting
Bromberg-Martin, E. S., Matsumoto, M., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.11.022 · Cited by 2,396 · see citing works
Discrete coding of reward probability and uncertainty by dopamine neurons
Fiorillo, C. D., Tobler, P. N., & Schultz, W. (2003) · Science · doi:10.1126/science.1077349 · Cited by 2,085 · see citing works
Self-citationTwo types of dopamine neuron distinctly convey positive and negative motivational signals
Matsumoto, M., & Hikosaka, O. (2009) · Nature · doi:10.1038/nature08028 · Cited by 1,378 · see citing works
Neuron-type-specific signals for reward and punishment in the ventral tegmental area
Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B., & Uchida, N. (2012) · Nature · doi:10.1038/nature10754 · Cited by 1,318 · see citing works
Input-specific control of reward and aversion in the ventral tegmental area
Lammel, S., Lim, B. K., Ran, C., Huang, K. W., Betley, M. J., Tye, K. M., Deisseroth, K., & Malenka, R. C. (2012) · Nature · doi:10.1038/nature11527 · Cited by 1,300 · see citing works
Adaptive Coding of Reward Value by Dopamine Neurons
Tobler, P. N., Fiorillo, C. D., & Schultz, W. (2005) · Science · doi:10.1126/science.1105370 · Cited by 1,276 · see citing works
Self-citationThe Rhesus Monkey Brain in Stereotaxic Coordinates
Paxinos, G., Huang, X., & Toga, A. W. (1999) · Research Online (University of Wollongong) · Cited by 1,070 · see citing works
Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system
Lammel, S., Hetzel, A., Häckel, O., Jones, I., Liss, B., & Roeper, J. (2008) · Neuron · doi:10.1016/j.neuron.2008.01.022 · Cited by 976 · see citing works
Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli
Mirenowicz, J., & Schultz, W. (1996) · Nature · doi:10.1038/379449a0 · Cited by 829 · see citing works
Phasic excitation of dopamine neurons in ventral VTA by noxious stimuli
Brischoux, F., Chakraborty, S., Brierley, D. I., & Ungless, M. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811507106 · Cited by 801 · see citing works
Neuhoff, H., Neu, A. D., Liss, B., & Roeper, J. (2002) · Journal of Neuroscience · doi:10.1523/jneurosci.22-04-01290.2002 · Cited by 400 · see citing works
Properties and opioid inhibition of mesolimbic dopamine neurons vary according to target location
Ford, C. P., Mark, G. P., & Williams, J. T. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.4331-05.2006 · Cited by 339 · see citing works
The temporal precision of reward prediction in dopamine neurons
Fiorillo, C. D., Newsome, W. T., & Schultz, W. (2008) · Nature Neuroscience · doi:10.1038/nn.2159 · Cited by 310 · see citing works
Self-citationBrown, J. W., Bullock, D. H., & Grossberg, S. (1999) · Journal of Neuroscience · doi:10.1523/jneurosci.19-23-10502.1999 · Cited by 299 · see citing works
Joshua, M., Adler, A., Mitelman, R., Vaadia, E., & Bergman, H. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.3839-08.2008 · Cited by 273 · see citing works
Lynd-Balta, E., & Haber, S. N. (1994) · Neuroscience · doi:10.1016/0306-4522(94)90182-1 · Cited by 233 · see citing works
Margolis, E. B., Mitchell, J., Ishikawa, J., Hjelmstad, G. O., & Fields, H. L. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.1526-08.2008 · Cited by 232 · see citing works
Guarraci, F. A., & Kapp, B. S. (1999) · Behavioural Brain Research · doi:10.1016/s0166-4328(98)00102-8 · Cited by 201 · see citing works
Haber, S. N., Ryoo, H., Cox, C., & Lu, W. (1995) · The Journal of Comparative Neurology · doi:10.1002/cne.903620308 · Cited by 188 · see citing works
Schultz, W., & Romo, R. (1987) · Journal of Neurophysiology · doi:10.1152/jn.1987.57.1.201 · Cited by 184 · cited 3× · see citing works
“1A) and low basal firing rates (0.1–15.0 Hz) (Schultz and Romo, 1987).”
BackgroundInfluentialStatistics of Midbrain Dopamine Neuron Spike Trains in the Awake Primate
Bayer, H. M., Lau, B., & Glimcher, P. W. (2007) · Journal of Neurophysiology · doi:10.1152/jn.01140.2006 · Cited by 184 · see citing works
Wang, D. V., & Tsien, J. Z. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017047 · Cited by 116 · see citing works
Brown, M. T. C., Henny, P., Bolam, J. P., & Magill, P. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.4423-08.2009 · Cited by 106 · see citing works
Fiorillo, C. D., Song, M. R., & Yun, S. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3883-12.2013 · Cited by 98 · see citing works
Self-citationMileykovskiy, B. Y., & Morales, M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5731-10.2011 · Cited by 93 · see citing works
Coizet, V., Dommett, E. J., Redgrave, P., & Overton, P. G. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2006.01.030 · Cited by 92 · see citing works
Transient activation of midbrain dopamine neurons by reward risk
Fiorillo, C. D. (2011) · Neuroscience · doi:10.1016/j.neuroscience.2011.09.037 · Cited by 90 · see citing works
Self-citationSynchronization of Midbrain Dopaminergic Neurons Is Enhanced by Rewarding Events
Joshua, M., Adler, A., Prut, Y., Vaadia, E., Wickens, J. R., & Bergman, H. (2009) · Neuron · doi:10.1016/j.neuron.2009.04.026 · Cited by 87 · see citing works
Towards a General Theory of Neural Computation Based on Prediction by Single Neurons
Fiorillo, C. D. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003298 · Cited by 50 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126 · see citing works
Dopamine reward prediction-error signalling: A two-component response
Schultz, W. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2015.26 · Cited by 1,036 · see citing works
Intact-brain analyses reveal distinct information carried by SNc dopamine subcircuits
Lerner, T. N., Shilyansky, C., Davidson, T. J., Evans, K. E., Beier, K. T., Zalocusky, K. A., Crow, A. K., Malenka, R. C., Luo, L., Tomer, R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.014 · Cited by 794 · see citing works
“…which increase their firing in response to these stimuli, may signal salience rather than value (Brischoux et al., 2009; Bromberg-Martin et al., 2010; Matsumoto and Hikosaka, 2009), though controversy on this point remains (Cohen et al., 2012; Fiorillo et al., 2013, 2013; Ungless et al., 2004).”
BackgroundNeural Circuitry of Reward Prediction Error
Watabe-Uchida, M., Eshel, N., & Uchida, N. (2017) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031109 · Cited by 437 · see citing works
Wulfram Gerstner, B., Lehmann, M., Liakoni, V., Corneil, D., & Brea, J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00053 · Cited by 321 · see citing works
Dopamine neurons projecting to the posterior striatum form an anatomically distinct subclass
Menegas, W., Bergan, J. F., Ogawa, S. K., Isogai, Y., Venkataraju, K. U., Osten, P., Uchida, N., & Watabe‐Uchida, M. (2015) · eLife · doi:10.7554/elife.10032 · Cited by 324 · see citing works
Dopamine neurons share common response function for reward prediction error
Eshel, N., Tian, J., Bukwich, M., & Uchida, N. (2016) · Nature Neuroscience · doi:10.1038/nn.4239 · Cited by 306 · see citing works
Distinct dopamine neurons mediate reward signals for short- and long-term memories
Yamagata, N., Ichinose, T., Aso, Y., Plaçais, P.-Y., Friedrich, A. B., Sima, R., Préat, T., Rubin, G. M., & Tanimoto, H. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1421930112 · Cited by 288 · see citing works
Dopamine Neuron-Specific Optogenetic Stimulation in Rhesus Macaques
Stauffer, W. R., Lak, A., Yang, A., Borel, M., Paulsen, O., Boyden, E. S., & Schultz, W. (2016) · Cell · doi:10.1016/j.cell.2016.08.024 · Cited by 256 · see citing works
Reward functions of the basal ganglia
Schultz, W. (2016) · Journal of Neural Transmission · doi:10.1007/s00702-016-1510-0 · Cited by 248 · see citing works
Dopaminergic basis of salience dysregulation in psychosis
Winton-Brown, T. T., Fusar-Poli, P., Ungless, M. A., & Howes, O. D. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.003 · Cited by 231 · see citing works
Distributed and Mixed Information in Monosynaptic Inputs to Dopamine Neurons
Tian, J., Huang, R., Cohen, J. Y., Osakada, F., Kobak, D., Machens, C. K., Callaway, E. M., Uchida, N., & Watabe-Uchida, M. (2016) · Neuron · doi:10.1016/j.neuron.2016.08.018 · Cited by 224 · see citing works
Two Dimensions of Value: Dopamine Neurons Represent Reward But Not Aversiveness
Fiorillo, C. D. (2013) · Science · doi:10.1126/science.1238699 · Cited by 203 · see citing works
Self-citationThe timing of action determines reward prediction signals in identified midbrain dopamine neurons
Coddington, L. T., & Dudman, J. T. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0245-7 · Cited by 206 · see citing works
Schultz, W., Stauffer, W. R., & Lak, A. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.03.013 · Cited by 164 · see citing works
Generating bursts (and pauses) in the dopamine midbrain neurons
Paladini, C. A., & Roeper, J. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.07.032 · Cited by 158 · see citing works
Mechanisms of Action and Persistent Neuroplasticity by Drugs of Abuse
Korpi, E. R., den Hollander, B., Farooq, U., Vashchinkina, E., Rajkumar, R., Nutt, D. J., Hyytiä, P., & Dawe, G. S. (2015) · Pharmacological Reviews · doi:10.1124/pr.115.010967 · Cited by 148 · see citing works
What Is the Relationship between Dopamine and Effort?
Walton, M. E., & Bouret, S. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.10.001 · Cited by 150 · see citing works
Pain-relief learning in flies, rats, and man: basic research and applied perspectivesPreprint
Gerber, B., Yarali, A., Diegelmann, S., Wotjak, C. T., Pauli, P., & Fendt, M. (2014) · Learning & Memory · doi:10.1101/lm.032995.113 · Cited by 139 · see citing works
Distinct Midbrain and Habenula Pathways Are Involved in Processing Aversive Events in Humans
Hennigan, K., D’Ardenne, K., & McClure, S. M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0927-14.2015 · Cited by 119 · see citing works
Functional Dissection of Basal Ganglia Inhibitory Inputs onto Substantia Nigra Dopaminergic Neurons
Evans, R. C., Twedell, E. L., Zhu, M., Ascencio, J., Zhang, R., & Khaliq, Z. M. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108156 · Cited by 109 · see citing works
Evans, R. C., Zhu, M., & Khaliq, Z. M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0117-17.2017 · Cited by 101 · see citing works
Drosophila Learn Opposing Components of a Compound Food Stimulus
Das, G., Klappenbach, M., Vrontou, E., Perisse, E., Clark, C. M., Burke, C. J., & Waddell, S. (2014) · Current Biology · doi:10.1016/j.cub.2014.05.078 · Cited by 102 · see citing works
Fiorillo, C. D., Song, M. R., & Yun, S. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3883-12.2013 · Cited by 98 · see citing works
Self-citationEly, B. A., Xu, J., Goodman, W. K., Lapidus, K. A. B., Gabbay, V., & Stern, E. R. (2016) · Human Brain Mapping · doi:10.1002/hbm.23179 · Cited by 95 · see citing works
The role of learning-related dopamine signals in addiction vulnerability
Huys, Q. J. M., Tobler, P. N., Hasler, G., & Flagel, S. B. (2014) · Progress in brain research · doi:10.1016/b978-0-444-63425-2.00003-9 · Cited by 87 · see citing works
Pauli, W. M., Larsen, T., Collette, S., Tyszka, J. M., Seymour, B., & O’Doherty, J. P. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.2277-15.2015 · Cited by 81 · see citing works
Modulation of cue-induced firing of ventral tegmental area dopamine neurons by leptin and ghrelin
Plasse, G., van Zessen, R., Luijendijk, M. C. M., Erkan, H., Stuber, G. D., Ramakers, G. M. J., & Adan, R. A. H. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.131 · Cited by 79 · see citing works
An Integrative Way for Studying Neural Basis of Basic Emotions With fMRI
Gu, S., Wang, F., Cao, C., Wu, E., Tang, Y., & Huang, J. H. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00628 · Cited by 79 · see citing works
Kamiński, J., Mamelak, A. N., Birch, K. G., Mosher, C. P., Tagliati, M., & Rutishauser, U. (2018) · Current Biology · doi:10.1016/j.cub.2018.03.024 · Cited by 79 · see citing works
Distributional Reinforcement Learning in the Brain
Lowet, A. S., Qiao, Z., Matias, S., Drugowitsch, J., & Uchida, N. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.004 · Cited by 69 · see citing works
Two spatiotemporally distinct value systems shape reward-based learning in the human brain
Fouragnan, E. F., Retzler, C., Mullinger, K. J., & Philiastides, M. G. (2015) · Nature Communications · doi:10.1038/ncomms9107 · Cited by 64 · see citing works
Redundant dopaminergic activity may enable compensatory axonal sprouting in Parkinson disease
Arkadir, D., Bergman, H., & Fahn, S. (2014) · Neurology · doi:10.1212/wnl.0000000000000243 · Cited by 65 · see citing works
Components and characteristics of the dopamine reward utility signal
Stauffer, W. R., Lak, A., Kobayashi, S., & Schultz, W. (2015) · The Journal of Comparative Neurology · doi:10.1002/cne.23880 · Cited by 62 · see citing works
Toward sophisticated basal ganglia neuromodulation: Review on basal ganglia deep brain stimulation
Cunha, C. D., Boschen, S. L., Gómez‐A, A., Ross, E. K., Gibson, W. S. J., Min, H.-K. P., Lee, K. H., & Blaha, C. D. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.02.003 · Cited by 58 · see citing works
Tarfa, R. A., Evans, R. C., & Khaliq, Z. M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.2969-16.2017 · Cited by 49 · see citing works
What can tiny mushrooms in fruit flies tell us about learning and memory?
Hige, T. (2017) · Neuroscience Research · doi:10.1016/j.neures.2017.05.002 · Cited by 50 · see citing works
Baudonnat, M., Huber, A., David, V., & Walton, M. E. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00175 · Cited by 42 · see citing works
Dopamine Rebound-Excitation Theory: Putting Brakes on PTSD
Lee, J. C., Wang, L. P., & Tsien, J. Z. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00163 · Cited by 39 · see citing works
Yohn, S. E., Galbraith, J., Calipari, E. S., & Conn, P. J. (2019) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00601 · Cited by 36 · see citing works
Dopamine signals and physiological origin of cognitive dysfunction in Parkinson's disease
Matsumoto, M. (2015) · Movement Disorders · doi:10.1002/mds.26177 · Cited by 35 · see citing works
Knolle, F., Ermakova, A. O., Justicia, A., Fletcher, P. C., Bunzeck, N., Düzel, E., & Murray, G. K. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0250-3 · Cited by 31 · see citing works
Reinforcement signaling of punishment versus relief in fruit fliesPreprint
König, C., Khalili, A., Ganesan, M., Nishu, A. P., Garza, A. P., Niewalda, T., Gerber, B., Aso, Y., & Yarali, A. (2018) · Learning & Memory · doi:10.1101/lm.047308.118 · Cited by 39 · see citing works
Multiplexing signals in reinforcement learning with internal models and dopamine
Nakahara, H. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.01.001 · Cited by 31 · see citing works
Brain Computation Is Organized via Power-of-Two-Based Permutation Logic
Xie, K., Fox, G. E., Liu, J., Lyu, C., Lee, J. C., Kuang, H., Jacobs, S., Li, M., Liu, T., Song, S., et al. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00095 · Cited by 30 · see citing works
Sensory regulation of dopaminergic cell activity: Phenomenology, circuitry and function
Overton, P. G., Vautrelle, N., & Redgrave, P. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.01.023 · Cited by 29 · see citing works
Oyama, K., Tateyama, Y., Hernádi, I., Tobler, P. N., Iijima, T., & Tsutsui, K. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00097.2015 · Cited by 27 · see citing works
Lee, I. B., Lee, E., Han, N.-E., Slavuj, M., Hwang, J., Lee, A., Sun, T., Jeong, Y., Baik, J.-H., Park, J.-Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44322-8 · Cited by 17 · see citing works
Inferring spikes from calcium imaging in dopamine neurons
Fleming, W., Jewell, S., Engelhard, B., Witten, D., & Witten, I. B. (2021) · PLoS ONE · doi:10.1371/journal.pone.0252345 · Cited by 26 · see citing works
Dopamine system: manager of neural pathways
Hong, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00854 · Cited by 24 · see citing works
Ramayya, A. G., Zaghloul, K. A., Weidemann, C. T., Baltuch, G. H., & Kahana, M. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00655 · Cited by 25 · see citing works
Rössler, J., Unterassner, L., Wyss, T., Haker, H., Brugger, P., Rössler, W., & Wotruba, D. (2019) · Schizophrenia Bulletin · doi:10.1093/schbul/sby079 · Cited by 20 · see citing works
Silkstone, M., & Brudzyński, S. M. (2019) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2019.172815 · Cited by 21 · see citing works
Asymmetric and adaptive reward coding via normalized reinforcement learning
Louie, K. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010350 · Cited by 18 · see citing works
Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data
Li, M., Zhao, F., Lee, J., Wang, D., Kuang, H., & Tsien, J. Z. (2015) · Scientific Reports · doi:10.1038/srep12474 · Cited by 18 · see citing works
Reward and value coding by dopamine neurons in non-human primates
Alikaya, A., Rack-Wildner, M., & Stauffer, W. R. (2017) · Journal of Neural Transmission · doi:10.1007/s00702-017-1793-9 · Cited by 18 · see citing works
He, Z., Jiang, Y., Gu, S., Wu, D., Qin, D., Feng, G., Ma, X., Huang, J. H., & Wang, F. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.713762 · Cited by 17 · see citing works
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Ding, L. (2023) · Annual Review of Vision Science · doi:10.1146/annurev-vision-111022-123804 · Cited by 17 · see citing works
Cortical regulation of dopaminergic neurons: role of the midbrain superior colliculus
Bertram, C., Dahan, L., Boorman, L. W., Harris, S., Vautrelle, N., Leriche, M., Redgrave, P., & Overton, P. G. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00329.2013 · Cited by 17 · see citing works
Midbrain fMRI: Applications, Limitations and Challenges
Düzel, E., Guitart-Masip, M., Maass, A., Hämmerer, D., Betts, M. J., Speck, O., Weiskopf, N., & Kanowski, M. (2015) · Biological Magnetic Resonance · doi:10.1007/978-1-4899-7591-1_20 · Cited by 16 · see citing works
Werlen, E., & Jones, M. W. (2015) · Progress in brain research · doi:10.1016/bs.pbr.2015.03.002 · Cited by 16 · see citing works
Evans, R. C. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105815 · Cited by 16 · see citing works
Muscarinic Receptor Activation Preferentially Inhibits Rebound in Vulnerable Dopaminergic Neurons
Beaver, M. L., & Evans, R. C. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1443-24.2025 · Cited by 11 · see citing works
Song, M. R., & Lee, S. W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.03.005 · Cited by 13 · see citing works
Self-citationRamayya, A. G., Pedisich, I., Levy, D. F., Lyalenko, A., Wanda, P. A., Rizzuto, D. S., Baltuch, G. H., & Kahana, M. J. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00200 · Cited by 12 · see citing works
Involvement of Midbrain Dopamine Neuron Activity in Negative Reinforcement Learning in Mice
Diao, Z., Li, Y., Cheng, Q., Wu, M., Di, Y., Qian, Z., Wei, C., Liu, Y., Tian, Y., & Ren, W. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02515-6 · Cited by 11 · see citing works
Gerber, B., König, C., Fendt, M., Andreatta, M., Romanos, M., Pauli, P., & Yarali, A. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2018.12.001 · Cited by 11 · see citing works
de Lima, B. F. C., Ramos, D. C., Barbiero, J. K., Pulido, L. N., Redgrave, P., Robinson, D. L., Gómez‐A, A., & Cunha, C. D. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2017.02.052 · Cited by 10 · see citing works
Electrophysiological Correlates of Reward Processing in Dopamine Neurons
Schultz, W. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805308-9.00002-6 · Cited by 9 · see citing works
The causal role between phasic midbrain dopamine signals and learning
Aquili, L. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00139 · Cited by 8 · see citing works
Stojanovic, S., Knowlton, C. J., Egger-Mackrodt, R., Mankel, J., Shin, J., Lammel, S., Canavier, C. C., & Roeper, J. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0361-25.2025 · Cited by 6 · see citing works
Morrens, J. (2014) · Journal of Neurophysiology · doi:10.1152/jn.00751.2013 · Cited by 6 · see citing works
Emergence of reward expectation signals in identified dopamine neuronsPreprint
Coddington, L. T., & Dudman, J. T. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/238881 · Cited by 6 · see citing works
Hadjas, L. C., Kollman, G. J., Linderhof, L., Xia, M., Mansur, S., Saint‐Pierre, M., Lim, B. K., Lee, E. B., Cicchetti, F., Awatramani, R. B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.03.657460 · Cited by 6 · see citing works
Occasion setters determine responses of putative DA neurons to discriminative stimuli
Aquili, L., Bowman, E. M., & Schmidt, R. (2020) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2020.107270 · Cited by 4 · see citing works
Limbrick‐Oldfield, E. H., Leech, R., Wise, R. G., & Ungless, M. A. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.14288 · Cited by 4 · see citing works
Peters, K. Z., Young, A. M. J., & McCutcheon, J. E. (2021) · European Journal of Neuroscience · doi:10.1111/ejn.15108 · Cited by 3 · see citing works
Stimulation of the human ventral tegmental area increases strategic betting
Hirschbichler, S. T., Lagrata, S., Shedd, N., Akram, H., Schwingenschuh, P., Waiß, C., Oberndorfer, S., Matharu, M. S., & Manohar, S. (2025) · Brain · doi:10.1093/brain/awaf210 · Cited by 3 · see citing works
TetrODrive: an open-source microdrive for combined electrophysiology and optophysiology
Brosch, M., Vlasenko, A., Ohl, F. W., & Lippert, M. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abf608 · Cited by 3 · see citing works
The biological and behavioral computations that influence dopamine responses
Stauffer, W. R. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.02.005 · Cited by 3 · see citing works
Bonardi, C., Cheung, T. H. C., Mondragón, E., & Tam, S. K. E. (2016) · doi:10.1002/9781118650813.ch14 · Cited by 3 · see citing works
System-wide dissociation of reward and aversive dopaminergic signalsPreprint
Bouchard, S.-J., Boutin, J., Lévesque, M., & Breton‐Provencher, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.27.656467 · Cited by 3 · see citing works
Versprille, L. J. F. W., McKenzie, C., Amorim, F. E., Yano, K., Dalley, J. W., & Robbins, T. W. (2026) · Journal of Neuroscience · doi:10.1523/jneurosci.1696-25.2026 · Cited by 2 · see citing works
White, T. L., Gonsalves, M. A., Zimmerman, C. S., Joyce, H., Cohen, R. A., Clark, U. S., Sweet, L. H., Lejuez, C. W., & Nitenson, A. Z. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1060877 · Cited by 2 · see citing works
Reinforcement learning with dopamine: A convergence of natural and artificial intelligence
Masset, P., & Gershman, S. J. (2025) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-443-29867-7.00019-0 · Cited by 3 · see citing works
Region-specific weighting of sensory intensity and reward prediction error by dopamine signals
Bouchard, S.-J., Boutin, J., Lévesque, M., & Breton‐Provencher, V. (2026) · iScience · doi:10.1016/j.isci.2026.117130 · Cited by 1 · see citing works
Human reinforcement learning: Insights from intracranial recordings and stimulation
Ramayya, A. G. (2014) · Scholarly Commons (University of Pennsylvania) · Cited by 1 · see citing works
Knolle, F., Ermakova, A. O., Justicia, A., Fletcher, P. C., Bunzeck, N., Düzel, E., & Murray, G. K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/263020 · Cited by 1 · see citing works
Asymmetric and adaptive reward coding via normalized reinforcement learningPreprint
Louie, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.24.469880 · Cited by 1 · see citing works
Ventral Tegmental Area Neuronal Ensembles Accurately Encode Action Number
Wood, J. T. (2015) · D-Scholarship@Pitt (University of Pittsburgh)
Tonic GABAergic inhibition as a new way to regulate mesolimbic dopamine system
Vashchinkina, E. (2013) · Työväentutkimus Vuosikirja
de Lima, B. F. C. (2014) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Gaining on reward prediction errors
Parker, N. F., & Witten, I. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4246
Timing and Conditioning, Theoretical Issues
Bonardi, C., Thc., C., Mondragón, E., & Ske., T. (2015)
Investigating Basal Ganglia Function With Cell-Type-Specific Manipulations
Kravitz, A. V., Devarakonda, K., & Kreitzer, A. C. (2016) · Handbook of behavioral neuroscience · doi:10.1016/b978-0-12-802206-1.00033-7
Lawford‐Smith, H. (2015)
Faculty Opinions recommendation of What Is the Relationship between Dopamine and Effort?
Fellows, L. K. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.734283795.793559475
Spatiotemporal dissociation of physical salience and stimulus evaluation in the human midbrain
Carvalheiro, J., McElhinney, P., Allwood-Spiers, S. E., Paterson, G., Shajan, G., & Philiastides, M. G. (2025) · Current Biology · doi:10.1016/j.cub.2025.09.013
Two Coordinates for Dopamine in the Basal Ganglia
Horváth, S., & Mirnics, K. (2026) · NeuroSci · doi:10.3390/neurosci7050104
- Authors
- Fiorillo, C. D., Yun, S. R., & Song, M. R.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2013-03-13
- Volume
- 33
- Issue
- 11
- Pages
- 4693-4709
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC3873403
- OpenAlex
- W2123563550
- Cited by
- 99
- References
- 30
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 1 · 2023: 2 · 2022: 2 · 2021: 6 · 2020: 4 · 2019: 5 · 2018: 10 · 2017: 9 · 2016: 12 · 2015: 18 · 2014: 14 · 2013: 6
- FWCI
- 8.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- National Research Foundation
- National Research Foundation of Korea
- Ministry of Education, Science and Technology (R32-2008-000-10218-0)
- National Eye Institute (R01EY005603)
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- dopaminergic neurons, ventral midbrain, substantia nigra, response diversity, reward prediction error, rhesus monkeys, appetitive stimuli, aversive stimuli, motivational value, neuronal subpopulations, calbindin, choice task
- MeSH terms
- Action Potentials, Animals, Appetitive Behavior, Avoidance Learning, Brain Mapping, Choice Behavior, Conditioning, Operant, Female, Macaca mulatta, Male, Mesencephalon, Motivation, Neural Inhibition, Photic Stimulation, Reaction Time, Reinforcement, Psychology, Reward, Statistics as Topic, Dopaminergic Neurons
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