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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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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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Optogenetics: 10 years of microbial opsins in neuroscience
Deisseroth, K. A. (2015) · Nature Neuroscience · doi:10.1038/nn.4091 · Cited by 1,335
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
Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons
Watabe‐Uchida, M., Zhu, L., Ogawa, S. K., Vamanrao, A., & Uchida, N. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.017 · Cited by 1,277
Self-citationDysregulation of the dopamine system in the pathophysiology of schizophrenia and depression
Grace, A. A. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.57 · Cited by 1,166
Neuronal Reward and Decision Signals: From Theories to Data
Schultz, W. (2015) · Physiological Reviews · doi:10.1152/physrev.00023.2014 · Cited by 1,126
Ventral tegmental area: cellular heterogeneity, connectivity and behaviour
Morales, M., & Margolis, E. B. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.165 · Cited by 1,108
Ultrafast neuronal imaging of dopamine dynamics with designed genetically encoded sensors
Patriarchi, T., Cho, J. R., Merten, K., Howe, M. W., Marley, A., Xiong, W.-H., Folk, R. W., Broussard, G. J., Liang, R., Jang, M. J., et al. (2018) · Science · doi:10.1126/science.aat4422 · Cited by 1,088
Dopamine neurons modulate neural encoding and expression of depression-related behaviour
Tye, K. M., Mirzabekov, J. J., Warden, M. R., Ferenczi, E. A., Tsai, H.-C., Finkelstein, J., Kim, S.-Y., Adhikari, A., Thompson, K. R., Andalman, A. S., et al. (2013) · Nature · doi:10.1038/nature11740 · Cited by 1,073
Circuit architecture of VTA dopamine neurons revealed by systematic input-output mapping
Beier, K. T., Steinberg, E. E., DeLoach, K. E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X. J., Kremer, E. J., Malenka, R. C., & Luo, L. (2015) · Cell · doi:10.1016/j.cell.2015.07.015 · Cited by 1,068
What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior
Behrens, T. E. J., Muller, T. H., Whittington, J. C. R., Mark, S., Baram, A. B., Stachenfeld, K. L., & Kurth-Nelson, Z. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.002 · Cited by 1,055
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
The Nucleus Accumbens: An Interface Between Cognition, Emotion, and Action
Floresco, S. B. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010213-115159 · Cited by 938
A causal link between prediction errors, dopamine neurons and learning
Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K. A., & Janak, P. H. (2013) · Nature Neuroscience · doi:10.1038/nn.3413 · Cited by 932
Berke, J. D. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0152-y · Cited by 904
Locus coeruleus and dopaminergic consolidation of everyday memory
Takeuchi, T., Duszkiewicz, A. J., Sonneborn, A., Spooner, P. A., Yamasaki, M., Watanabe, M., Smith, C. C., Fernández, G., Deisseroth, K., Greene, R. W., et al. (2016) · Nature · doi:10.1038/nature19325 · Cited by 827
Reward and aversion in a heterogeneous midbrain dopamine system
Lammel, S., Lim, B. K., & Malenka, R. C. (2014) · Neuropharmacology · doi:10.1016/j.neuropharm.2013.03.019 · Cited by 804
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
Dissociable dopamine dynamics for learning and motivation
Mohebi, A., Pettibone, J. R., Hamid, A., Wong, J.-M. T., Vinson, L. T., Patriarchi, T., Tian, L., Kennedy, R. T., & Berke, J. D. (2019) · Nature · doi:10.1038/s41586-019-1235-y · Cited by 751
Distinct extended amygdala circuits for divergent motivational states
Jennings, J. H., Sparta, D. R., Stamatakis, A. M., Ung, R. L., Pleil, K. E., Kash, T. L., & Stuber, G. D. (2013) · Nature · doi:10.1038/nature12041 · Cited by 704
Integration of optogenetics with complementary methodologies in systems neuroscience
Kim, C. K., Adhikari, A., & Deisseroth, K. (2017) · Nature reviews. Neuroscience · doi:10.1038/nrn.2017.15 · Cited by 690
Updating dopamine reward signals
Schultz, W. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.11.012 · Cited by 651
Activation of VTA GABA Neurons Disrupts Reward Consumption
van Zessen, R., Phillips, J. L., Budygin, E. A., & Stuber, G. D. (2012) · Neuron · doi:10.1016/j.neuron.2012.02.016 · Cited by 646
Neurophysiology of performance monitoring and adaptive behavior
Ullsperger, M., Danielmeier, C., & Jocham, G. (2014) · Physiological Reviews · doi:10.1152/physrev.00041.2012 · Cited by 634
The role of D2-autoreceptors in regulating dopamine neuron activity and transmission
Ford, C. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2014.01.025 · Cited by 630
Rapid signalling in distinct dopaminergic axons during locomotion and reward
Howe, M. W., & Dombeck, D. A. (2016) · Nature · doi:10.1038/nature18942 · Cited by 618
Dopamine reward prediction error coding
Schultz, W. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.1/wschultz · Cited by 597
“Eating addiction”, rather than “food addiction”, better captures addictive-like eating behavior
Hebebrand, J., Albayrak, Ö., Adan, R. A. H., Antel, J., Diéguez, C., de Jong, J. W., Leng, G., Menzies, J. R. W., Mercer, J. G., Murphy, M., et al. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2014.08.016 · Cited by 576
Striatal circuits for reward learning and decision-making
Cox, J., & Witten, I. B. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0189-2 · Cited by 584
Dopamine neuron activity before action initiation gates and invigorates future movements
da Silva, J. A., Tecuapetla, F., Paixão, V., & Costa, R. M. (2018) · Nature · doi:10.1038/nature25457 · Cited by 574
Prolonged dopamine signalling in striatum signals proximity and value of distant rewards
Howe, M. W., Tierney, P. L., Sandberg, S. G., Phillips, P. E. M., & Graybiel, A. M. (2013) · Nature · doi:10.1038/nature12475 · Cited by 565
Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons
Engelhard, B., Finkelstein, J., Cox, J., Fleming, W., Jang, H. J., Ornelas, S., Koay, S. A., Thiberge, S. Y., Daw, N. D., Tank, D. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1261-9 · Cited by 544
What the orbitofrontal cortex does not do
Stalnaker, T. A., Cooch, N. K., & Schoenbaum, G. (2015) · Nature Neuroscience · doi:10.1038/nn.3982 · Cited by 531
Dopamine and Cognitive Control in Prefrontal Cortex
Ott, T., & Nieder, A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.12.006 · Cited by 530
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J. Z., & Kepecs, A. (2013) · Nature · doi:10.1038/nature12176 · Cited by 516
How prediction errors shape perception, attention, and motivation
den Ouden, H. E. M., Kok, P., & de Lange, F. P. (2012) · Frontiers in Psychology · doi:10.3389/fpsyg.2012.00548 · Cited by 512
Cerebellar granule cells encode the expectation of reward
Wagner, M. J., Kim, T. H., Savall, J., Schnitzer, M. J., & Luo, L. (2017) · Nature · doi:10.1038/nature21726 · Cited by 505
Dopaminergic neurons promote hippocampal reactivation and spatial memory persistence
McNamara, C. G., Tejero-Cantero, Á., Trouche, S., Campo-Urriza, N., & Dupret, D. (2014) · Nature Neuroscience · doi:10.1038/nn.3843 · Cited by 486
Scalable control of mounting and attack by Esr1+ neurons in the ventromedial hypothalamus
Lee, H., Kim, D.-W., Remedios, R., Anthony, T. E., Chang, A., Madisen, L., Zeng, H., & Anderson, D. J. (2014) · Nature · doi:10.1038/nature13169 · Cited by 479
Dorsal raphe neurons signal reward through 5-HT and glutamate
Liu, Z., Zhou, J., Li, Y., Hu, F., Lu, Y., Ma, M., Feng, Q., Zhang, J.-E., Wang, D., Zeng, J., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.010 · Cited by 469
A distributional code for value in dopamine-based reinforcement learning
Dabney, W., Kurth-Nelson, Z. L., Uchida, N., Starkweather, C. K., Hassabis, D., Munos, R., & Botvinick, M. (2020) · Nature · doi:10.1038/s41586-019-1924-6 · Cited by 462
Self-citationGenetic Dissection of Neural Circuits: A Decade of Progress
Luo, L., Callaway, E. M., & Svoboda, K. (2018) · Neuron · doi:10.1016/j.neuron.2018.03.040 · Cited by 454
Are you or aren’t you? Challenges associated with physiologically identifying dopamine neurons
Ungless, M. A., & Grace, A. A. (2012) · Trends in Neurosciences · doi:10.1016/j.tins.2012.02.003 · Cited by 450
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
Frémaux, N., & Wulfram Gerstner, B. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00085 · Cited by 449
Reinforcement learning and episodic memory in humans and animals: an integrative framework
Gershman, S. J., & Daw, N. D. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033625 · Cited by 446
A computational and neural model of momentary subjective well-being
Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1407535111 · Cited by 444
Predictions and the brain: how musical sounds become rewarding
Salimpoor, V. N., Zald, D. H., Zatorre, R. J., Dagher, A., & McIntosh, A. R. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.12.001 · Cited by 443
Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain
Kim, C. K., Yang, S., Pichamoorthy, N., Young, N. P., Kauvar, I., Jennings, J. H., Lerner, T. N., Berndt, A., Lee, S. Y., Ramakrishnan, C., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3770 · Cited by 439
Neural 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
Self-citationSerotonin neurons in the dorsal raphe nucleus encode reward signals
Li, Y., Zhong, W., Wang, D., Feng, Q., Liu, Z., Zhou, J., Jia, C., Hu, F., Zeng, J., Guo, Q., et al. (2016) · Nature Communications · doi:10.1038/ncomms10503 · Cited by 440
Fields, H. L., & Margolis, E. B. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.01.002 · Cited by 432
Coordinated and compartmentalized neuromodulation shapes sensory processing in Drosophila
Cohn, R., Morantte, I., & Ruta, V. (2015) · Cell · doi:10.1016/j.cell.2015.11.019 · Cited by 430
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Beyeler, A., Namburi, P., Glober, G. F., Simonnet, C., Calhoon, G. G., Conyers, G. F., Luck, R., Wildes, C. P., & Tye, K. M. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.004 · Cited by 412
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Self-citationCentral Cholinergic Neurons Are Rapidly Recruited by Reinforcement Feedback
Hangya, B., Ranade, S. P., Lorenc, M., & Kepecs, A. (2015) · Cell · doi:10.1016/j.cell.2015.07.057 · Cited by 399
Dopamine Prediction Errors in Reward Learning and Addiction: From Theory to Neural Circuitry
Keiflin, R., & Janak, P. H. (2015) · Neuron · doi:10.1016/j.neuron.2015.08.037 · Cited by 401
Dissecting the diversity of midbrain dopamine neurons
Roeper, J. (2013) · Trends in Neurosciences · doi:10.1016/j.tins.2013.03.003 · Cited by 395
Brown, M. T. C., Tan, K. R., O’Connor, E. C., Nikonenko, I., Müller, D., & Lüscher, C. (2012) · Nature · doi:10.1038/nature11657 · Cited by 393
Dopamine neurons create Pavlovian conditioned stimuli with circuit-defined motivational properties
Saunders, B. T., Richard, J. M., Margolis, E. B., & Janak, P. H. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0191-4 · Cited by 393
Dopamine neurons projecting to the posterior striatum reinforce avoidance of threatening stimuli
Menegas, W., Akiti, K., Amo, R., Uchida, N., & Watabe‐Uchida, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0222-1 · Cited by 392
Self-citationSerotonergic neurons signal reward and punishment on multiple timescales
Cohen, J. Y., Amoroso, M. W., & Uchida, N. (2015) · eLife · doi:10.7554/elife.06346 · Cited by 388
Self-citationMood as representation of momentum
Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.010 · Cited by 383
Midbrain dopamine neurons control judgment of time
Soares, S., Atallah, B. V., & Paton, J. J. (2016) · Science · doi:10.1126/science.aah5234 · Cited by 365
Stamatakis, A. M., Jennings, J. H., Ung, R. L., Blair, G. A., Weinberg, R. J., Neve, R. L., Boyce, F., Mattis, J., Ramakrishnan, C., Deisseroth, K., et al. (2013) · Neuron · doi:10.1016/j.neuron.2013.08.023 · Cited by 364
Targeting neurons and photons for optogenetics
Packer, A. M., Roska, B., & Häusser, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3427 · Cited by 359
Single rodent mesohabenular axons release glutamate and GABA
Root, D. H., Mejias-Aponte, C. A., Zhang, S., Wang, H.-L., Hoffman, A. F., Lupica, C. R., & Morales, M. (2014) · Nature Neuroscience · doi:10.1038/nn.3823 · Cited by 349
A parvalbumin-positive excitatory visual pathway to trigger fear responses in mice
Shang, C., Liu, Z., Chen, Z., Shi, Y., Wang, Q., Liu, S., Li, D., & Cao, P. (2015) · Science · doi:10.1126/science.aaa8694 · Cited by 349
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Parker, N. F., Cameron, C. M., Taliaferro, J. P., Lee, J., Choi, J. Y., Davidson, T. J., Daw, N. D., & Witten, I. B. (2016) · Nature Neuroscience · doi:10.1038/nn.4287 · Cited by 348
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 335
Self-citationDopamine Is Required for the Neural Representation and Control of Movement Vigor
Panigrahi, B., Martin, K. A., Li, Y., Graves, A. R., Vollmer, A., Olson, L., Mensh, B. D., Karpova, A. Y., & Dudman, J. T. (2015) · Cell · doi:10.1016/j.cell.2015.08.014 · Cited by 330
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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 321
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Kim, H. R., Malik, A. N., Mikhael, J. G., Bech, P., Tsutsui‐Kimura, I., Sun, F., Zhang, Y., Li, Y., Watabe‐Uchida, M., Gershman, S. J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.013 · Cited by 308
Self-citationReinforcement signalling in Drosophila; dopamine does it all after all
Waddell, S. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.01.005 · Cited by 298
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Sharpe, M. J., Chang, C. Y., Liu, M. A., Batchelor, H. M., Mueller, L. E., Jones, J. L., Niv, Y., & Schoenbaum, G. M. (2017) · Nature Neuroscience · doi:10.1038/nn.4538 · Cited by 298
An integrative perspective on the role of dopamine in schizophrenia
Maia, T. V., & Frank, M. J. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.05.021 · Cited by 293
Mandelblat-Cerf, Y., Ramesh, R. N., Burgess, C. R., Patella, P., Yang, Z., Lowell, B. B., & Andermann, M. L. (2015) · eLife · doi:10.7554/elife.07122 · Cited by 290
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Cho, J. R., Treweek, J. B., Robinson, J. E., Xiao, C., Bremner, L. R., Greenbaum, A., & Gradinaru, V. (2017) · Neuron · doi:10.1016/j.neuron.2017.05.020 · Cited by 276
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Oleson, E. B., Gentry, R. N., Chioma, V. C., & Cheer, J. F. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3087-12.2012 · Cited by 272
Taylor, N. E., Van Dort, C. J., Kenny, J. D., Pei, J., Guidera, J. A., Vlasov, K. Y., Lee, J. T., Boyden, E. S., Brown, E. N., & Solt, K. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614340113 · Cited by 269
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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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