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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
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Serotonin's many meanings elude simple theories
Dayan, P., & Huys, Q. J. M. (2015) · eLife · doi:10.7554/elife.07390 · Cited by 41
Mouse Models of Neurodevelopmental Disease of the Basal Ganglia and Associated Circuits
Pappas, S. S., Leventhal, D., Albin, R. L., & Dauer, W. T. (2014) · Current topics in developmental biology/Current Topics in Developmental Biology · doi:10.1016/b978-0-12-397920-9.00001-9 · Cited by 41
Visualization of plasticity in fear-evoked calcium signals in midbrain dopamine neuronsPreprint
Gore, B. B., Soden, M. E., & Zweifel, L. S. (2014) · Learning & Memory · doi:10.1101/lm.036079.114 · Cited by 41
Motor skill learning and reward consumption differentially affect VTA activation
Leemburg, S., Canonica, T., & Luft, A. R. (2018) · Scientific Reports · doi:10.1038/s41598-017-18716-w · Cited by 40
Determination of neurotensin projections to the ventral tegmental area in mice
Woodworth, H. L., Brown, J. A., Batchelor, H. M., Bugescu, R., & Leinninger, G. M. (2018) · Neuropeptides · doi:10.1016/j.npep.2018.02.003 · Cited by 40
Role of the hippocampal CA1 region in incremental value learning
Jeong, Y., Huh, N., Lee, J., Yun, I., Lee, J. W., Lee, I., & Jung, M. W. (2018) · Scientific Reports · doi:10.1038/s41598-018-28176-5 · Cited by 40
Gentry, R. N., Schuweiler, D. R., & Roesch, M. R. (2018) · Brain Research · doi:10.1016/j.brainres.2018.10.008 · Cited by 41
Near-infrared catecholamine nanosensors for high spatiotemporal dopamine imaging
Yang, S. J., Bonis-O’Donnell, J. T. D., Beyene, A. G., & Landry, M. P. (2021) · Nature Protocols · doi:10.1038/s41596-021-00530-4 · Cited by 43
The Roles of Basolateral Amygdala Parvalbumin Neurons in Fear Learning
Yau, J. O.-Y., Chaichim, C., Power, J., & McNally, G. P. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2461-20.2021 · Cited by 40
Stievenard, A., Méquinion, M., Andrews, Z. B., Destée, A., Chartier-Harlin, M.-C., Viltart, O., & Vanbesien-Mailliot, C. C. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.11.021 · Cited by 39
Sands, L. P., Jiang, A., Liebenow, B., DiMarco, E. K., Laxton, A. W., Tatter, S. B., Montague, P. R., & Kishida, K. T. (2023) · Science Advances · doi:10.1126/sciadv.adi4927 · Cited by 39
Neural mechanisms of decision‐making in aging
Lighthall, N. R. (2019) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1519 · Cited by 40
Neural correlates and determinants of approach–avoidance conflict in the prelimbic prefrontal cortex
Fernández-León, J. A., Engelke, D. S., Aquino-Miranda, G., Goodson, A., Rasheed, M. N., & Monte, F. H. D. (2021) · eLife · doi:10.7554/elife.74950 · Cited by 39
Contemporary approaches to neural circuit manipulation and mapping: focus on reward and addiction
Saunders, B. T., Richard, J. M., & Janak, P. H. (2015) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2014.0210 · Cited by 40
Wittmann, B. C., Tan, G. C.-Y., Lisman, J. E., Dolan, R. J., & Düzel, E. (2013) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2013.07.018 · Cited by 39
Prospective contingency explains behavior and dopamine signals during associative learning
Qian, L., Burrell, M., Hennig, J. A., Matias, S., Murthy, V. N., Gershman, S. J., & Uchida, N. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01915-4 · Cited by 38
Self-citationMelancholy, anhedonia, apathy: the search for separable behaviors and neural circuits in depression
Post, R. J., & Warden, M. R. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.02.018 · Cited by 39
Newly identified sleep–wake and circadian circuits as potential therapeutic targets
Venner, A., Todd, W. D., Fraigne, J. J., Bowrey, H. E., Eban-Rothschild, A., Kaur, S., & Anaclet, C. (2019) · SLEEP · doi:10.1093/sleep/zsz023 · Cited by 38
Frey, A.-L., & McCabe, C. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.049 · Cited by 38
Expected Reward Value and Reward Uncertainty Have Temporally Dissociable Effects on Memory Formation
Stanek, J. K., Dickerson, K. C., Chiew, K. S., Clement, N. J., & Adcock, R. A. (2019) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01411 · Cited by 38
De Bundel, D., Femenía, T., DuPont, C. M., Konradsson-Geuken, Å., Feltmann, K., Schilström, B., & Lindskog, M. (2013) · International Journal of Neuropsychopharmacology · doi:10.1017/s1461145713000370 · Cited by 37
Takahashi, Y. K., Stalnaker, T. A., Mueller, L. E., Harootonian, S. K., Langdon, A., & Schoenbaum, G. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01310-x · Cited by 37
Mejías-Aponte, C. A., Ye, C.-Q., Bonci, A., Kiyatkin, E. A., & Morales, M. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.3422-13.2015 · Cited by 37
Dopamine signals and physiological origin of cognitive dysfunction in Parkinson's disease
Matsumoto, M. (2015) · Movement Disorders · doi:10.1002/mds.26177 · Cited by 37
Information processing in the vertebrate habenula
Fore, S., Palumbo, F., Pelgrims, R., & Yaksi, E. (2017) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2017.08.019 · Cited by 37
Behavioral Modulation by Spontaneous Activity of Dopamine Neurons
Ichinose, T., Tanimoto, H., & Yamagata, N. (2017) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2017.00088 · Cited by 37
Libbrecht, S., Hoffman, L., Welkenhuysen, M., den Haute, C. V., Baekelandt, V., Braeken, D., & Haesler, S. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00888.2017 · Cited by 37
Self-citationHughes, R. N., Watson, G. D. R., Petter, E. A., Kim, N., Bakhurin, K. I., & Yin, H. H. (2019) · Current Biology · doi:10.1016/j.cub.2019.08.022 · Cited by 37
Vancraeyenest, P., Arsenault, J. T., Li, X., Zhu, Q., Kobayashi, K., Isa, K., Isa, T., & Vanduffel, W. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.013 · Cited by 38
Packheiser, J., Donoso, J. R., Cheng, S., Güntürkün, O., & Pusch, R. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101901 · Cited by 38
Cue and Reward Evoked Dopamine Activity Is Necessary for Maintaining Learned Pavlovian Associations
van Zessen, R., Flores-Dourojeanni, J. P., Eekel, T., van den Reijen, S., Lodder, B., Omrani, A., Smidt, M. P., Ramakers, G. M. J., van der Plasse, G., Stuber, G. D., et al. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.2744-20.2021 · Cited by 37
Dopamine neurons evaluate natural fluctuations in performance quality
Duffy, A., Latimer, K. W., Goldberg, J. H., Fairhall, A. L., & Gadagkar, V. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110574 · Cited by 37
Faget, L. C., Oriol, L., Lee, W.-C., Zell, V., Sargent, C., Flores, A. J., Hollon, N. G., Ramanathan, D., & Hnasko, T. S. (2024) · Nature Communications · doi:10.1038/s41467-024-48340-y · Cited by 37
Population activity structure of excitatory and inhibitory neurons
Bittner, S. R., Williamson, R. C., Snyder, A. C., Litwin-Kumar, A., Doiron, B., Chase, S. M., Smith, M. A., & Yu, B. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181773 · Cited by 37
Duvarci, S., Simpson, E. H., Schneider, G., Kandel, E. R., Roeper, J., & Sigurdsson, T. (2018) · Nature Communications · doi:10.1038/s41467-018-05214-4 · Cited by 37
Yohn, S. E., Galbraith, J., Calipari, E. S., & Conn, P. J. (2019) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00601 · Cited by 36
Cassaday, H. J., Muir, C., Stevenson, C. W., Bonardi, C., Hock, R. M., & Waite, L. J. (2023) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2023.107757 · Cited by 36
State Dependency of Chemosensory Coding in the Gustatory Thalamus (VPMpc) of Alert Rats
Liu, H., & Fontanini, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0839-15.2015 · Cited by 36
Baker, P. M., Mathis, V., Lecourtier, L., Simmons, S. C., Nugent, F. S., Hill, S., & Mizumori, S. J. Y. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.826475 · Cited by 36
Shin, J., Kovacheva, L., Thomas, D., Stojanovic, S., Knowlton, C. J., Mankel, J., Boehm, J., Farassat, N., Paladini, C. A., Striessnig, J., et al. (2022) · Science Advances · doi:10.1126/sciadv.abm4560 · Cited by 36
Illuminating Vertebrate Olfactory Processing
Spors, H., Albeanu, D. F., Murthy, V. N., Rinberg, D. A., Uchida, N., Wachowiak, M., & Friedrich, R. W. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3328-12.2012 · Cited by 36
Self-citationAccumbal Dopamine Release Tracks the Expectation of Dopamine Neuron-Mediated Reinforcement
Covey, D. P., & Cheer, J. F. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.055 · Cited by 36
Mesostriatal dopamine is sensitive to changes in specific cue-reward contingencies
Garr, E., Cheng, Y., Jeong, H., Brooke, S., Castell, L., Bal, A., Magnard, R., Namboodiri, V. M. K., & Janak, P. H. (2024) · Science Advances · doi:10.1126/sciadv.adn4203 · Cited by 35
Cerebellar Modulation of Mesolimbic Dopamine Transmission Is Functionally Asymmetrical
Holloway, Z. R., Paige, N. B., Comstock, J. F., Nolen, H. G., Sable, H. J. K., & Lester, D. B. (2019) · The Cerebellum · doi:10.1007/s12311-019-01074-w · Cited by 35
Molecular regulation of GABA ergic neuron differentiation and diversity in the developing midbrain
Lahti, L., Achim, K., & Partanen, J. M. (2013) · Acta Physiologica · doi:10.1111/apha.12062 · Cited by 35
Optogenetics, physiology, and emotions
Kravitz, A. V., & Bonci, A. (2013) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2013.00169 · Cited by 35
Learning and memory with neuropathic pain: impact of old age and progranulin deficiency
Albuquerque, B., Häußler, A., Vannoni, E., Wolfer, D. P., & Tegeder, I. (2013) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2013.00174 · Cited by 35
Achim, K., Peltopuro, P., Lahti, L., Li, J. Y. H., Salminen, M., & Partanen, J. M. (2012) · Development · doi:10.1242/dev.076380 · Cited by 35
Morello, F., & Partanen, J. M. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.10.001 · Cited by 35
A multidimensional distributional map of future reward in dopamine neurons
Sousa, M., Bujalski, P., Cruz, B. F., Louie, K., McNamee, D., & Paton, J. J. (2025) · Nature · doi:10.1038/s41586-025-09089-6 · Cited by 35
Optogenetic and chemogenetic insights into the food addiction hypothesis
Krashes, M. J., & Kravitz, A. V. (2014) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2014.00057 · Cited by 34
Paul, E. J., Tossell, K., & Ungless, M. A. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14534 · Cited by 33
Dasgupta, S., Wörgötter, F., & Manoonpong, P. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00126 · Cited by 34
Zhukovskaya, A., Zimmerman, C. A., Willmore, L., Pan-Vazquez, A., Janarthanan, S. R., Lynch, L. A., Falkner, A. L., & Witten, I. B. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.009 · Cited by 33
Morphological elucidation of basal ganglia circuits contributing reward prediction
Fujiyama, F., TAKAHASHI, S. Y., & Karube, F. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00006 · Cited by 33
Murata, K., Kinoshita, T., Fukazawa, Y., Kobayashi, K., Yamanaka, A., Hikida, T., Manabe, H., & Yamaguchi, M. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00050 · Cited by 33
Ambrasė, A., Lewis, C. A., Barth, C., & Derntl, B. (2020) · Frontiers in Neuroendocrinology · doi:10.1016/j.yfrne.2020.100873 · Cited by 33
The convergence of aversion and reward signals in individual neurons of the mice lateral habenula
Li, J., Fan, R., Liu, X., Shen, X., Liu, X., & Zhao, H. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113637 · Cited by 33
Bloem, B., Huda, R., Amemori, K., Abate, A., Krishna, G., Wilson, A. L., Carter, C. W., Sur, M., & Graybiel, A. M. (2022) · Nature Communications · doi:10.1038/s41467-022-28983-5 · Cited by 33
Shen, L., Zhang, G., Tao, C., Seo, M. B., Zhang, N. K., Huang, J., Zhang, L. I., & Tao, H. W. (2022) · Nature Communications · doi:10.1038/s41467-022-28854-z · Cited by 33
Hasegawa, T., Chiken, S., Kobayashi, K., & Nambu, A. (2022) · Nature Communications · doi:10.1038/s41467-022-29750-2 · Cited by 33
Beier, K. T. (2022) · eLife · doi:10.7554/elife.76886 · Cited by 33
Metabolomic Biomarkers in the Diagnosis of Non-Alcoholic Fatty Liver Disease
Chashmniam, S., Ghafourpour, M., Farimani, A. R., Gholami, A., & Gh, B. F. N. M. (2019) · Hepatitis Monthly · doi:10.5812/hepatmon.92244 · Cited by 33
Dopaminergic circuits controlling threat and safety learning
Duvarci, S. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.10.001 · Cited by 32
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Morese, R., Rabellino, D., Sambataro, F., Perussia, F., Valentini, M. C., Bara, B. G., & Bosco, F. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0166357 · Cited by 32
Holloway, Z. R., Freels, T. G., Comstock, J. F., Nolen, H. G., Sable, H. J. K., & Lester, D. B. (2018) · Synapse · doi:10.1002/syn.22074 · Cited by 33
Cortical processing of chemosensory and hedonic features of taste in active licking mice
Bouaichi, C. G., & Vincis, R. (2020) · Journal of Neurophysiology · doi:10.1152/jn.00069.2020 · Cited by 32
Internal States Influence the Representation and Modulation of Food Intake by Subthalamic Neurons
Wu, H., Yan, X., Tang, D., Gu, W., Luan, Y., Cai, H., Zhou, C., & Xiao, C. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00533-3 · Cited by 32
The learning of prospective and retrospective cognitive maps within neural circuits
Namboodiri, V. M. K., & Stuber, G. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.034 · Cited by 32
Seitz, B. M., Hoang, I. B., DiFazio, L. E., Blaisdell, A. P., & Sharpe, M. J. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.035 · Cited by 32
Goedhoop, J. N., van den Boom, B. J. G., Robke, R., Veen, F., Fellinger, L., van Elzelingen, W., Arbab, T., & Willuhn, I. (2022) · eLife · doi:10.7554/elife.82711 · Cited by 32
Duration between rewards controls the rate of behavioral and dopaminergic learning
Burke, D. A., Taylor, A., Jeong, H., Lee, S., Zsembik, L. C. P., Wu, B., Floeder, J. R., Naik, G. A., Chen, R., & Namboodiri, V. M. K. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02206-2 · Cited by 32
Organization of Neural Systems for Aversive Information Processing: Pain, Error, and Punishment
Kobayashi, S. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00136 · Cited by 32
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 32
Bakhurin, K. I., Hughes, R. N., Jiang, Q., Hossain, M. A., Gutkin, B. S., Fallon, I. P., & Yin, H. H. (2025) · Nature Communications · doi:10.1038/s41467-025-64132-4 · Cited by 31
Scharff, C., & Adam, I. (2012) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.10.001 · Cited by 31
Lateral hypothalamic area neuropeptides modulate ventral tegmental area dopamine neurons and feeding
Perez‐Bonilla, P., Santiago-Colon, K., & Leinninger, G. M. (2020) · Physiology & Behavior · doi:10.1016/j.physbeh.2020.112986 · Cited by 31
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Ming, Y., Wu, D., Wang, Y., Shi, Y., & Lin, C. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.2997031 · Cited by 31
Ferguson, L. M., Ahrens, A. M., Longyear, L. G., & Aldridge, J. W. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2947-19.2020 · Cited by 31
Pascoli, V., Hiver, A., Li, Y., Harada, M., Esmaeili, V., & Lüscher, C. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01429-8 · Cited by 31
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Smith, K. S., & Graybiel, A. M. (2013) · Brain Research · doi:10.1016/j.brainres.2013.01.008 · Cited by 31
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Stamatakis, A. M., & Stuber, G. D. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.17615/8wvk-5k07 · Cited by 31
Protracted maturation of forebrain afferent connections of the ventral tegmental area in the rat
Yetnikoff, L., Reichard, R. A., Schwartz, Z. M., Parsely, K. P., & Zahm, D. S. (2014) · Journal of Comparative Neurology · doi:10.1002/cne.23459 · Cited by 30
Dopamine neurons drive spatiotemporally heterogeneous striatal dopamine signals during learning
Engel, L., Wolff, A. R., Blake, M., Collins, V. L., Sinha, S., & Saunders, B. T. (2024) · Current Biology · doi:10.1016/j.cub.2024.05.069 · Cited by 30
Coding of whisker motion across the mouse face
Severson, K. S., Xu, D., Yang, H., & O’Connor, D. H. (2019) · eLife · doi:10.7554/elife.41535 · Cited by 30
Choi, J. Y., Jang, H. J., Ornelas, S., Fleming, W., Fürth, D., Au, J., Bandi, A., Engel, E. A., & Witten, I. B. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108492 · Cited by 29
A hindbrain dopaminergic neural circuit prevents weight gain by reinforcing food satiation
Han, Y., Xia, G., He, Y., He, Y., Farías, M., Xu, Y., & Wu, Q. (2021) · Science Advances · doi:10.1126/sciadv.abf8719 · Cited by 30
Nigrostriatal dopamine signals sequence-specific action-outcome prediction errors
Hollon, N. G., Williams, E., Howard, C. D., Li, H., Traut, T. I., & Jin, X. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.040 · Cited by 30
Neuromodulation and Neurophysiology on the Timescale of Learning and Decision-Making
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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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