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Neuron-type-specific signals for reward and punishment in the ventral tegmental area
Open accessAbstract
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), 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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“one successful theoretical model posits that associative learning is driven by the activity of dopamine neurons in the midbrain, where spiking activity resembles the reward prediction error (RPE) signal in an RL algorithm called temporal difference (TD) learning [1][2][3][4].”
From Emergence of belief-like representations through reinforcement learning (2023) · cites this paper as [3]
In the citing paper
“Animal research indicates that dopaminergic neurons are strongly excited by acute stress and aversive stimuli during adulthood (Brischoux et al., 2009; Cohen et al., 2012; Wenzel et al., 2015; Zweifel et al., 2011).”
From The effects of psychosocial stress on dopaminergic function and the acute stress response (2019) · cites this paper as Cohen et al., 2012
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“they also respond to aversive events [21, 22]”
From The molecular and cellular mechanisms of depression: a focus on reward circuitry (2019) · cites this paper as [22]
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“these changes fail to occur with more-controlled arm, mouth and eye movements in a considerable range of studies2,5,9,13,151,152,154”
From Dopamine reward prediction-error signalling: A two-component response (2016) · cites this paper as [154]
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Citations
Cites
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388
Millisecond-timescale, genetically targeted optical control of neural activity
Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G., & Deisseroth, K. A. (2005) · Nature Neuroscience · doi:10.1038/nn1525 · Cited by 5,015
Classical conditioning II: Current research and theory.
Black, A. H., & Prokasy, W. F. (1972) · Appleton-Century-Crofts eBooks · Cited by 4,449
Channelrhodopsin-2, a directly light-gated cation-selective membrane channel
Nagel, G., Szellas, T., Huhn, W., Kateriya, S., Adeishvili, N., Berthold, P. R., Ollig, D., Hegemann, P., & Bamberg, E. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1936192100 · Cited by 2,730
NEURAL MECHANISMS OF ADDICTION: The Role of Reward-Related Learning and Memory
Hyman, S. E., Malenka, R. C., & Nestler, E. J. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113009 · Cited by 2,647
Swanson, L. W. (1982) · Brain Research Bulletin · doi:10.1016/0361-9230(82)90145-9 · Cited by 1,718
Behavioral Theories and the Neurophysiology of Reward
Schultz, W. (2006) · Annual Review of Psychology · doi:10.1146/annurev.psych.56.091103.070229 · Cited by 1,637
Opioids excite dopamine neurons by hyperpolarization of local interneurons
Johnson, S., & North, R. (1992) · The Journal of Neuroscience · doi:10.1523/jneurosci.12-02-00483.1992 · Cited by 1,637
Two 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
Midbrain dopamine neurons encode a quantitative reward prediction error signal
Bayer, H. M., & Glimcher, P. W. (2005) · Neuron · doi:10.1016/j.neuron.2005.05.020 · Cited by 1,304
Flagged first, then by citations
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Sorted by citations
Items 201–300 of 1,000
Connecting circuits for supraspinal control of locomotion
Ferreira-Pinto, M. J., Ruder, L., Capelli, P., & Arber, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.015 · Cited by 140
Swart, J. C., Froböse, M. I., Cook, J. L., Geurts, D. E. M., Frank, M. J., Cools, R., & den Ouden, H. E. M. (2017) · eLife · doi:10.7554/elife.22169 · Cited by 140
Coarse Graining, Fixed Points, and Scaling in a Large Population of Neurons
Meshulam, L., Gauthier, J. L., Brody, C. D., Tank, D. W., & Bialek, W. (2019) · Physical Review Letters · doi:10.1103/physrevlett.123.178103 · Cited by 140
Optogenetic dissection of neural circuits underlying emotional valence and motivated behaviors
Nieh, E. H., Kim, S., Namburi, P., & Tye, K. M. (2012) · Brain Research · doi:10.1016/j.brainres.2012.11.001 · Cited by 139
Correia, P. A., Lottem, E., Banerjee, D., Machado, A. S., Carey, M. R., & Mainen, Z. F. (2017) · eLife · doi:10.7554/elife.20975 · Cited by 138
Positive reward prediction errors during decision-making strengthen memory encoding
Jang, A. I., Nassar, M. R., Dillon, D. G., & Frank, M. J. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0597-3 · Cited by 138
Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner
Silvetti, M., Vassena, E., Abrahamse, E. L., & Verguts, T. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006370 · Cited by 140
The Olfactory Tubercle Encodes Odor Valence in Behaving Mice
Gadziola, M. A., Tylicki, K. A., Christian, D. L., & Wesson, D. W. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4750-14.2015 · Cited by 137
Root, D. H., Barker, D. J., Estrin, D. J., Miranda-Barrientos, J. A., Liu, B., Zhang, S., Wang, H.-L., Vautier, F., Ramakrishnan, C., Kim, Y. S., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108094 · Cited by 137
The medial prefrontal cortex shapes dopamine reward prediction errors under state uncertainty
Starkweather, C. K., Gershman, S. J., & Uchida, N. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.036 · Cited by 136
Self-citationKhamassi, M., & Humphries, M. D. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00079 · Cited by 136
Midbrain dopamine neurons compute inferred and cached value prediction errors in a common framework
Sadacca, B. F., Jones, J. L., & Schoenbaum, G. M. (2016) · eLife · doi:10.7554/elife.13665 · Cited by 136
Meta-analysis of human prediction error for incentives, perception, cognition, and action
Corlett, P. R., Mollick, J. A., & Kober, H. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-021-01264-3 · Cited by 135
Dopaminergic and Prefrontal Basis of Learning from Sensory Confidence and Reward Value
Lak, A., Okun, M., Moss, M. M., Gurnani, H., Farrell, K., Wells, M. J., Reddy, C. B., Kepecs, Á., Harris, K. D., & Carandini, M. (2019) · Neuron · doi:10.1016/j.neuron.2019.11.018 · Cited by 137
Postingestive Modulation of Food Seeking Depends on Vagus-Mediated Dopamine Neuron Activity
Fernandes, A. B., da Silva, J. A., Almeida, J., Cui, G., Gerfen, C. R., Costa, R. M., & Oliveira-Maia, A. J. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.009 · Cited by 134
The impact of stress in decision making in the context of uncertainty
Morgado, P. F., Sousa, N., & Cerqueira, J. (2014) · Journal of Neuroscience Research · doi:10.1002/jnr.23521 · Cited by 134
Ellwood, I. T., Patel, T., Wadia, V., Lee, A. T., Liptak, A. T., Bender, K. J., & Sohal, V. S. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.1221-17.2017 · Cited by 132
The Origins and Organization of Vertebrate Pavlovian ConditioningPreprint
Fanselow, M., & Wassum, K. (2015) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a021717 · Cited by 132
Wenzel, J. M., Rauscher, N. A., Cheer, J. F., & Oleson, E. B. (2015) · ACS Chemical Neuroscience · doi:10.1021/cn500255p · Cited by 131
Pavlovian valuation systems in learning and decision making
Clark, J. J., Hollon, N. G., & Phillips, P. E. M. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.06.004 · Cited by 131
Reward processing by the dorsal raphe nucleus: 5-HT and beyondPreprint
Luo, M., Zhou, J., & Liu, Z. (2015) · Learning & Memory · doi:10.1101/lm.037317.114 · Cited by 131
Multiphasic modulation of cholinergic interneurons by nigrostriatal afferents
Straub, C., Tritsch, N. X., Hagan, N. A., Gu, C., & Sabatini, B. L. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0589-14.2014 · Cited by 130
Stress effects on the neural substrates of motivated behavior
Hollon, N. G., Burgeno, L. M., & Phillips, P. E. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4114 · Cited by 129
Jia, X., Siegle, J. H., Bennett, C., Gale, S. D., Denman, D. J., Koch, C., & Olsen, S. R. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00680.2018 · Cited by 130
The Evolving Understanding of Dopamine Neurons in the Substantia Nigra and Ventral Tegmental Area
Gantz, S. C., Ford, C., Morikawa, H., & Williams, J. T. (2017) · Annual Review of Physiology · doi:10.1146/annurev-physiol-021317-121615 · Cited by 126
Breton, J., Charbit, A. R., Snyder, B. J., Fong, P. T. K., Dias, E. V., Himmels, P., Lock, H., & Margolis, E. B. (2018) · The Journal of Comparative Neurology · doi:10.1002/cne.24572 · Cited by 126
Dynamic Nigrostriatal Dopamine Biases Action Selection
Howard, C. D., Li, H., Geddes, C. E., & Jin, X. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.029 · Cited by 125
Cue-Evoked Dopamine Promotes Conditioned Responding during Learning
Morrens, J., Aydın, Ç., van Rensburg, A. J., Rabell, J. E., & Haesler, S. (2020) · Neuron · doi:10.1016/j.neuron.2020.01.012 · Cited by 124
Self-citationPupil diameter is not an accurate real-time readout of locus coeruleus activity
Megemont, M., McBurney-Lin, J., & Yang, H. (2022) · eLife · doi:10.7554/elife.70510 · Cited by 127
Kappa opioid receptors on dopaminergic neurons are necessary for kappa-mediated place aversion
Chefer, V. I., Bäckman, C. M., Gigante, E. D., & Shippenberg, T. S. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.171 · Cited by 122
A dopamine gradient controls access to distributed working memory in the large-scale monkey cortex
Froudist‐Walsh, S., Bliss, D. P., Ding, X., Rapan, L., Niu, M., Knoblauch, K., Zilles, K., Kennedy, H., Palomero‐Gallagher, N., & Wang, X. (2021) · Neuron · doi:10.1016/j.neuron.2021.08.024 · Cited by 122
Midbrain dopamine neurons signal aversion in a reward-context-dependent manner
Matsumoto, H., Tian, J., Uchida, N., & Watabe‐Uchida, M. (2016) · eLife · doi:10.7554/elife.17328 · Cited by 121
Self-citationTakahashi, Y. K., Langdon, A., Niv, Y., & Schoenbaum, G. M. (2016) · Neuron · doi:10.1016/j.neuron.2016.05.015 · Cited by 120
Dorsal tegmental dopamine neurons gate associative learning of fear
Groessl, F., Munsch, T., Meis, S., Griessner, J., Kaczanowska, J., Pliota, P., Kargl, D., Badurek, S., Kraitsy, K., Rassoulpour, A., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0174-5 · Cited by 118
Ventral Tegmental Dopamine Neurons Control the Impulse Vector during Motivated Behavior
Hughes, R. N., Bakhurin, K. I., Petter, E. A., Watson, G. D. R., Kim, N., Friedman, A. D., & Yin, H. H. (2020) · Current Biology · doi:10.1016/j.cub.2020.05.003 · Cited by 115
Xu, H., & Yang, F. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02233-0 · Cited by 114
Anatomical and single-cell transcriptional profiling of the murine habenular complex
Wallace, M. L., Huang, K. W., Hochbaum, D., Hyun, M., Radeljic, G., & Sabatini, B. L. (2020) · eLife · doi:10.7554/elife.51271 · Cited by 113
Processing of Hedonic and Chemosensory Features of Taste in Medial Prefrontal and Insular Networks
Jezzini, A., Mazzucato, L., Camera, G. L., & Fontanini, A. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2974-13.2013 · Cited by 113
The effects of psychosocial stress on dopaminergic function and the acute stress response
Bloomfield, M. A., McCutcheon, R. A., Kempton, M., Freeman, T. P., & Howes, O. (2019) · eLife · doi:10.7554/elife.46797 · Cited by 112
Dopamine, reward learning, and active inference
FitzGerald, T. H. B., Dolan, R. J., & Friston, K. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00136 · Cited by 112
Esslinger, C., Englisch, S., Inta, D., Rausch, F., Schirmbeck, F. N., Mier, D. U., Kirsch, P., Meyer‐Lindenberg, A., & Zink, M. (2012) · Schizophrenia Research · doi:10.1016/j.schres.2012.06.025 · Cited by 110
Topography of Reward and Aversion Encoding in the Mesolimbic Dopaminergic System
Yuan, L., Dou, Y., & Sun, Y.-G. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0271-19.2019 · Cited by 110
Value Learning through Reinforcement
Daw, N. D., & Tobler, P. N. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-416008-8.00015-2 · Cited by 109
Cai, L. X., Pizano, K., Gundersen, G. W., Hayes, C. L., Fleming, W., Holt, S., Cox, J., & Witten, I. B. (2020) · eLife · doi:10.7554/elife.54936 · Cited by 108
Central Processing of Itch in the Midbrain Reward Center
Su, X., Chen, M., Yuan, Y., Li, Y., Guo, S.-S., Luo, H., Huang, C., Sun, W., Li, Y., Zhu, M. X., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.03.030 · Cited by 107
Multi-channel fiber photometry for population neuronal activity recording
Guo, Q., Zhou, J., Feng, Q., Lin, R., Gong, H., Luo, Q., Zeng, S., Luo, M., & Fu, L. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.003919 · Cited by 107
Optogenetic identification of striatal projection neuron subtypes during in vivo recordings
Kravitz, A. V., Owen, S. F., & Kreitzer, A. C. (2012) · Brain Research · doi:10.1016/j.brainres.2012.11.018 · Cited by 106
Glutamate neurons are intermixed with midbrain dopamine neurons in nonhuman primates and humans
Root, D. H., Wang, H., Liu, B., Barker, D. J., Mód, L., Szocsics, P., Silva, A. C., Maglóczky, Z., & Morales, M. (2016) · Scientific Reports · doi:10.1038/srep30615 · Cited by 106
Akiti, K., Tsutsui‐Kimura, I., Xie, Y., Mathis, A., Markowitz, J. E., Anyoha, R., Datta, S. R., Mathis, M. W., Uchida, N., & Watabe‐Uchida, M. (2022) · Neuron · doi:10.1016/j.neuron.2022.08.022 · Cited by 106
Self-citationTyree, S. M., & de Lecea, L. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00050 · Cited by 103
Dopamine transients follow a striatal gradient of reward time horizons
Mohebi, A., Wei, W., Pelattini, L., Kim, K., & Berke, J. D. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01566-3 · Cited by 105
Atallah, H. E., McCool, A. D., Howe, M. W., & Graybiel, A. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.021 · Cited by 103
Nakanishi, S., Hikida, T., & Yawata, S. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.04.026 · Cited by 103
A comparison of neural responses to appetitive and aversive stimuli in humans and other mammals
Hayes, D. J., Duncan, N. W., Xu, J., & Northoff, G. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.06.018 · Cited by 102
Rapid Plasticity of Higher-Order Thalamocortical Inputs during Sensory Learning
Audette, N. J., Bernhard, S. M., Ray, A., Stewart, L. T., & Barth, A. L. (2019) · Neuron · doi:10.1016/j.neuron.2019.04.037 · Cited by 102
The Tail of the Striatum: From Anatomy to Connectivity and Function
Valjent, E., & Gangarossa, G. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.10.016 · Cited by 102
Belief state representation in the dopamine system
Babayan, B. M., Uchida, N., & Gershman, S. J. (2018) · Nature Communications · doi:10.1038/s41467-018-04397-0 · Cited by 101
Self-citationYetnikoff, L., Cheng, A. Y., Lavezzi, H. N., Parsley, K. P., & Zahm, D. S. (2015) · Journal of Comparative Neurology · doi:10.1002/cne.23797 · Cited by 101
Associative learning changes cross-modal representations in the gustatory cortex
Vincis, R., & Fontanini, A. (2016) · eLife · doi:10.7554/elife.16420 · Cited by 101
A primate temporal cortex–zona incerta pathway for novelty seeking
Ogasawara, T., Sogukpinar, F., Zhang, K., Feng, Y.-Y., Pai, J., Jezzini, A., & Monosov, I. E. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00950-1 · Cited by 100
Functional Connectome Analysis of Dopamine Neuron Glutamatergic Connections in Forebrain Regions
Mingote, S., Chuhma, N., Kusnoor, S. V., Field, B. C., Deutch, A. Y., & Rayport, S. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.1674-15.2015 · Cited by 100
Diversity and Homogeneity in Responses of Midbrain Dopamine Neurons
Fiorillo, C. D., Yun, S. R., & Song, M. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3886-12.2013 · Cited by 99
Progress in understanding mood disorders: optogenetic dissection of neural circuits
Lammel, S., Tye, K. M., & Warden, M. R. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12049 · Cited by 98
Fiorillo, C. D., Song, M. R., & Yun, S. R. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.3883-12.2013 · Cited by 98
Learning Reward Uncertainty in the Basal Ganglia
Mikhael, J. G., & Bogacz, R. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005062 · Cited by 99
Bidirectional Anticipation of Future Osmotic Challenges by Vasopressin Neurons
Mandelblat-Cerf, Y., Kim, A., Burgess, C. R., Subramanian, S., Tannous, B. A., Lowell, B. B., & Andermann, M. L. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.021 · Cited by 98
Self-citationLaszlovszky, T., Schlingloff, D., Hegedüs, P., Freund, T. F., Gulyás, A. I., Kepecs, Á., & Hangya, B. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0648-0 · Cited by 98
Encoding and Tracking of Outcome-Specific Expectancy in the Gustatory Cortex of Alert Rats
Gardner, M., & Fontanini, A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1820-14.2014 · Cited by 98
Neural mechanisms of the nucleus accumbens circuit in reward and aversive learning
Hikida, T., Morita, M., & Macpherson, T. (2016) · Neuroscience Research · doi:10.1016/j.neures.2016.01.004 · Cited by 98
The Raphe Dopamine System Controls the Expression of Incentive Memory
Lin, R., Liang, J., Wang, R., Yan, T., Zhou, Y., Liu, Y., Feng, Q., Sun, F., Li, Y., Li, A., et al. (2020) · Neuron · doi:10.1016/j.neuron.2020.02.009 · Cited by 97
Orbitofrontal control of visual cortex gain promotes visual associative learning
Liu, D., Deng, J., Zhang, Z., Zhang, Z., Sun, Y.-G., Yang, T., & Yao, H. (2020) · Nature Communications · doi:10.1038/s41467-020-16609-7 · Cited by 99
The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning
Nasser, H. M., Calu, D. J., Schoenbaum, G. M., & Sharpe, M. J. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00244 · Cited by 97
van Daal, R. J. J., Aydın, Ç., Michon, F., Aarts, A. A. A., Kraft, M. D., Kloosterman, F., & Haesler, S. (2021) · Nature Protocols · doi:10.1038/s41596-021-00539-9 · Cited by 96
Self-citationLee, E., Balasubramanian, H., Tsolias, A., Anakwe, S. U., Medalla, M., Shenoy, K. V., & Chandrasekaran, C. (2021) · eLife · doi:10.7554/elife.67490 · Cited by 96
Population-wide distributions of neural activity during perceptual decision-making
Wohrer, A., Humphries, M. D., & Machens, C. K. (2012) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2012.09.004 · Cited by 96
Turning a Negative into a Positive: Ascending GABAergic Control of Cortical Activation and Arousal
Brown, R. E., & McKenna, J. T. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00135 · Cited by 95
Zhang, Z., Liu, Q., Wen, P., Zhang, J., Rao, X., Zhou, Z., Zhang, H., He, X., Li, J., Zhou, Z., et al. (2017) · eLife · doi:10.7554/elife.25423 · Cited by 95
Basal Ganglia Output Controls Active Avoidance Behavior
Hormigo, S., Vega‐Flores, G., & Castro‐Alamancos, M. A. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1842-16.2016 · Cited by 94
In vivo functional diversity of midbrain dopamine neurons within identified axonal projections
Farassat, N., Costa, K. M., Stojanovic, S., Albert, S., Kovacheva, L., Shin, J., Egger, R., Somayaji, M., Duvarci, S., Schneider, G., et al. (2019) · eLife · doi:10.7554/elife.48408 · Cited by 96
Dopaminergic action prediction errors serve as a value-free teaching signal
Greenstreet, F., Vergara, H. M., Johansson, Y., Pati, S., Schwarz, L., Lenzi, S. C., Geerts, J. P., Wisdom, M., Gubanova, A., Rollik, L. B., et al. (2025) · Nature · doi:10.1038/s41586-025-09008-9 · Cited by 94
Self-citationHunger and Satiety Gauge Reward Sensitivity
Cassidy, R. M., & Tong, Q. (2017) · Frontiers in Endocrinology · doi:10.3389/fendo.2017.00104 · Cited by 93
The role of state uncertainty in the dynamics of dopamine
Mikhael, J. G., Kim, H. R., Uchida, N., & Gershman, S. J. (2022) · Current Biology · doi:10.1016/j.cub.2022.01.025 · Cited by 88
Self-citationRecent advances in understanding the role of phasic dopamine activity
Schultz, W. (2019) · F1000Research · doi:10.12688/f1000research.19793.1 · Cited by 94
Tsutsui‐Kimura, I., Matsumoto, H., Akiti, K., Yamada, M. M., Uchida, N., & Watabe‐Uchida, M. (2020) · eLife · doi:10.7554/elife.62390 · Cited by 92
Self-citationAmo, R., Matias, S., Yamanaka, A., Tanaka, K. F., Uchida, N., & Watabe‐Uchida, M. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01109-2 · Cited by 94
Self-citationThe 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 92
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Metadata
- Authors
- Cohen, J. Y., Haesler, S., Vong, L., Lowell, B. B., & Uchida, N.
- Affiliations
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Uchida, Naoshige
- Venue
- Nature
- Published
- 2012-01-18
- Volume
- 482
- Issue
- 7383
- Pages
- 85-88
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3271183
- OpenAlex
- W2022561563
- Cited by
- 1,318
- References
- 39
- Citations by year
- 2026: 52 · 2025: 115 · 2024: 90 · 2023: 78 · 2022: 99 · 2021: 116 · 2020: 113 · 2019: 106 · 2018: 100 · 2017: 83 · 2016: 88 · 2015: 102 · 2014: 69 · 2013: 63 · 2012: 43
- FWCI
- 36.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institute of Mental Health (R01MH095953)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK057521, R01DK075632, R01DK089044, P30DK046200, F32DK078478)
- 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 › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- ventral tegmental area, dopaminergic neurons, GABAergic neurons, reward prediction error, reward anticipation, aversive stimuli, dopamine, channelrhodopsin-2
- MeSH terms
- Channelrhodopsins, Animals, Cues, Dopamine, gamma-Aminobutyric Acid, Male, Mice, Inbred C57BL, Odorants, Punishment, Reward, Ventral Tegmental Area, Principal Component Analysis, Mice, Dopaminergic Neurons, GABAergic Neurons
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