The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Cited by
Sorted by citations
Items 601–700 of 1,000
Nishioka, T., Attachaipanich, S., Hamaguchi, K., Lazarus, M., d’Exaerde, A. D. K., Macpherson, T., & Hikida, T. (2023) · Nature Communications · doi:10.1038/s41467-023-38025-3 · Cited by 29
Selective encoding of reward predictions and prediction errors by globus pallidus subpopulations
Farries, M. A., Faust, T. W., Mohebi, A., & Berke, J. D. (2023) · Current Biology · doi:10.1016/j.cub.2023.08.042 · Cited by 29
Reward expectations direct learning and drive operant matching in Drosophila
Rajagopalan, A. E., Darshan, R., Hibbard, K. L., Fitzgerald, J. E., & Turner, G. C. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221415120 · Cited by 29
Learning to represent reward structure: A key to adapting to complex environments
Nakahara, H., & Hikosaka, O. (2012) · Neuroscience Research · doi:10.1016/j.neures.2012.09.007 · Cited by 30
Overview on Research and Clinical Applications of Optogenetics
Towne, C. L., & Thompson, K. R. (2016) · Current Protocols in Pharmacology · doi:10.1002/cpph.13 · Cited by 29
Hutchison, M. A., Gu, X., Adrover, M. F., LEE, M. R., Hnasko, T. S., Alvarez, V. A., & Lü, W. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.7 · Cited by 29
Kristofova, M., Aher, Y. D., Ilic, M., Radoman, B., Kalaba, P., Dragačević, V., Aher, N. Y., Leban, J. J., Korz, V., Zanon, L., et al. (2018) · Behavioural Brain Research · doi:10.1016/j.bbr.2018.01.032 · Cited by 29
Chohan, M. O., Esses, S., Haft, J., Ahmari, S. E., & Veenstra-VanderWeele, J. M. (2020) · Psychopharmacology · doi:10.1007/s00213-020-05635-4 · Cited by 30
Nonlinear recurrent inhibition through facilitating serotonin release in the raphe
Lynn, M. B., Geddes, S. D., Chahrour, M. A., Maillé, S., Caya-Bissonnette, L., Harkin, E. F., Harvey‐Girard, E., Haj‐Dahmane, S., Naud, R., & Béïque, J.-C. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-01912-7 · Cited by 28
Expectancy-Related Changes in Dopaminergic Error Signals Are Impaired by Cocaine Self-Administration
Takahashi, Y. K., Stalnaker, T. A., Marrero-Garcia, Y., Rada, R. M., & Schoenbaum, G. M. (2019) · Neuron · doi:10.1016/j.neuron.2018.11.025 · Cited by 28
Gordon-Fennell, A., Will, R. G., Ramachandra, V. S., Gordon-Fennell, L., Dominguez, J. M., Zahm, D. S., & Marinelli, M. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01433 · Cited by 28
Cortical circuits for transforming whisker sensation into goal-directed licking
Esmaeili, V., Tamura, K., Foustoukos, G., Oryshchuk, A., Crochet, S., & Petersen, C. C. H. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.08.003 · Cited by 28
Cholinergic activity reflects reward expectations and predicts behavioral responses
Hegedüs, P., Sviatkó, K., Király, B., Martínez‐Bellver, S., & Hangya, B. (2022) · iScience · doi:10.1016/j.isci.2022.105814 · Cited by 28
Dopaminergic neurons dynamically update sensory values during olfactory maneuver
Kato, A., Ohta, K., Okanoya, K., & Kazama, H. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113122 · Cited by 28
The dynamic state of a prefrontal–hypothalamic–midbrain circuit commands behavioral transitions
Chen, C., Altafi, M., Corbu, M.-A., Trenk, A., van den Munkhof, H. E., Weineck, K., Bender, F., Carús-Cadavieco, M., Bakhareva, A., Korotkova, T. M., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01598-3 · Cited by 28
Lesions of the lateral habenula facilitate active avoidance learning and threat extinction
Song, M., Jo, Y. S., Lee, Y., & Choi, J.-S. (2016) · Behavioural Brain Research · doi:10.1016/j.bbr.2016.10.013 · Cited by 28
Bariselli, S., Contestabile, A., Tzanoulinou, S., Musardo, S., & Bellone, C. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00360 · Cited by 28
Orsini, C. A., Hernandez, C. M., Bizon, J. L., & Setlow, B. (2018) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-018-00649-0 · Cited by 28
Primate Nigrostriatal Dopamine System Regulates Saccadic Response Inhibition
Ogasawara, T., Nejime, M., Takada, M., & Matsumoto, M. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.025 · Cited by 28
Wulff, S., Nielsen, M. Ø., Rostrup, E., Svarer, C., Jensen, L. T., Pinborg, L. H., & Glenthoj, B. Y. (2019) · Psychological Medicine · doi:10.1017/s0033291718004099 · Cited by 28
Montardy, Q., Zhou, Z., Lei, Z., Liu, X., Zeng, P., Chen, C., Liu, Y., Sanz‐Leon, P., Huang, K., & Wang, L. (2019) · 中国科学通报：英文版 · doi:10.1016/j.scib.2019.05.005 · Cited by 28
Striatal Dopamine Signals and Reward Learning
Bech, P., Crochet, S., Dard, R. F., Ghaderi, P., Liu, Y., Malekzadeh, M., Petersen, C. C. H., Pulin, M., Renard, A., & Sourmpis, C. (2023) · Function · doi:10.1093/function/zqad056 · Cited by 27
In vivo localization of chronically implanted electrodes and optic fibers in mice
Király, B., Balázsfi, D., Horváth, I., Solari, N., Sviatkó, K., Lengyel, K., Birtalan, E., Babos, M., Bagaméry, G., Máthé, D., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18472-y · Cited by 27
Drosophila reward system - A summary of current knowledge
Dvořáček, J., & Kodrík, D. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.12.032 · Cited by 25
Langlois, L. D., Berman, R. Y., Shepard, R. D., Simmons, S. C., Tsuda, M. C., Gouty, S., Choi, K. H., & Nugent, F. S. (2021) · Addiction Biology · doi:10.1111/adb.13064 · Cited by 27
“Liking” as an early and editable draft of long-run affective value
Dayan, P. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001476 · Cited by 27
An internal expectation guides Drosophila egg-laying decisions
Vijayan, V., Wang, Z., Chandra, V., Chakravorty, A., Li, R., Sarbanes, S. L., Akhlaghpour, H., & Maimon, G. (2022) · Science Advances · doi:10.1126/sciadv.abn3852 · Cited by 29
Neural circuits provide insights into reward and aversion
Chen, W. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.1002485 · Cited by 28
Finan, P. H., Remeniuk, B., & Dunn, K. E. (2017) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2017.07.029 · Cited by 27
Cortical recruitment determines learning dynamics and strategy
Ceballo, S., Bourg, J., Kempf, A., Piwkowska, Z., Daret, A., Pinson, P., Deneux, T., Rumpel, S., & Bathellier, B. (2019) · Nature Communications · doi:10.1038/s41467-019-09450-0 · Cited by 27
Ott, T., Stein, A. M., & Nieder, A. (2023) · Nature Communications · doi:10.1038/s41467-023-43271-6 · Cited by 26
Prefrontal and lateral entorhinal neurons co-dependently learn item–outcome rules
Jun, H., Lee, J. Y., Bleza, N. R., Ichii, A., Donohue, J. D., & Igarashi, K. M. (2024) · Nature · doi:10.1038/s41586-024-07868-1 · Cited by 26
Estrogen modulates reward prediction errors and reinforcement learning
Golden, C. E. M., Martin, A. C., Kaur, D., Mah, A., Levy, D. H., Yamaguchi, T., Lasek, A. W., Lin, D., Aoki, C. J., & Constantinople, C. M. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02104-z · Cited by 26
Trujillo‐Pisanty, I., Conover, K. L., Solis, P., Palacios, D., & Shizgal, P. (2020) · PLoS ONE · doi:10.1371/journal.pone.0226722 · Cited by 26
Sun, Y., Cao, J., Xu, C., Liu, X., Wang, Z., & Zhao, H. (2020) · Experimental Neurology · doi:10.1016/j.expneurol.2020.113433 · Cited by 26
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
Relative salience signaling within a thalamo-orbitofrontal circuit governs learning rate
Namboodiri, V. M. K., Hobbs, T. G., Trujillo‐Pisanty, I., Simon, R. C., Hjort, M. M., & Stuber, G. D. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.037 · Cited by 26
How do real animals account for the passage of time during associative learning?
Namboodiri, V. M. K. (2022) · Behavioral Neuroscience · doi:10.1037/bne0000516 · Cited by 26
Navailles, S., Guillem, K., Vouillac‐Mendoza, C., & Ahmed, S. H. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu112 · Cited by 26
Beloate, L. N., & Coolen, L. M. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.10.024 · Cited by 26
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 26
Zhang, Q., Xue, J., Tang, J., Wu, S., Liu, Z., Wu, C., Liu, C., Liu, Y., Lin, J., Han, J., et al. (2024) · Science Advances · doi:10.1126/sciadv.ado0866 · Cited by 26
Kawashima, T., Wei, Z., Haruvi, R., Shainer, I., Narayan, S., Baier, H., & Ahrens, M. B. (2025) · Neuron · doi:10.1016/j.neuron.2025.05.017 · Cited by 21
A mathematical model of reward-mediated learning in drug addiction
Chou, T., & D'Orsogna, M. R. (2022) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0082997 · Cited by 25
The neural basis of delayed gratification
Gao, Z., Wang, H., Lu, C., Lu, T., Froudist‐Walsh, S., Chen, M., Wang, X., Hu, J., & Sun, W. (2021) · Science Advances · doi:10.1126/sciadv.abg6611 · Cited by 25
Dopamine error signal to actively cope with lack of expected reward
Ishino, S., Kamada, T., Sarpong, G. A., Kitano, J., Tsukasa, R., Mukohira, H., Sun, F., Li, Y., Kobayashi, K., Naoki, H., et al. (2023) · Science Advances · doi:10.1126/sciadv.ade5420 · Cited by 25
Kutlikova, H. H., Zhang, L., Eisenegger, C., van Honk, J., & Lamm, C. (2023) · Neuropsychopharmacology · doi:10.1038/s41386-023-01570-y · Cited by 25
Petykó, Z., Gálosi, R., Tóth, A., Máté, K., Szabó, I., Szabó, I., Karádi, Z., & Lénárd, L. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.03.034 · Cited by 25
López, J. M., & González, A. (2017) · The Journal of Comparative Neurology · doi:10.1002/cne.24266 · Cited by 25
Learning rules for aversive associative memory formation
Ozawa, T., & Johansen, J. P. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.02.010 · Cited by 25
Early Life Stress- and Drug-Induced Histone Modifications Within the Ventral Tegmental Area
Shepard, R. D., & Nugent, F. S. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.588476 · Cited by 25
Morello, F., Võikar, V., Parkkinen, P., Panhelainen, A. E., Rosenholm, M., Makkonen, A., Rantamäki, T., Piepponen, P. T., Aitta‐aho, T., & Partanen, J. M. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-01033-8 · Cited by 25
Dopamine encodes deep network teaching signals for individual learning trajectories
Garcia, S. L., Laffere, A., Toschi, C., Schilling, L., Moretti, J., Podlaski, J., Fritsche, M., Zatka-Haas, P., Li, Y., Bogacz, R., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.025 · Cited by 26
Pinto, S. R., & Uchida, N. (2025) · Nature Communications · doi:10.1038/s41467-025-62280-1 · Cited by 24
Self-citationBilingual neurons release glutamate and GABA
Uchida, N. (2014) · Nature Neuroscience · doi:10.1038/nn.3840 · Cited by 24
Self-citationSex-specific parenting and depression evoked by preoptic inhibitory neurons
Dimén, D., Puska, G., Szendi, V., Sipos, E., Zelena, D., & Dobolyi, Á. (2021) · iScience · doi:10.1016/j.isci.2021.103090 · Cited by 24
Winkelmeier, L., Filosa, C., Hartig, R., Scheller, M. F., Sack, M., Reinwald, J., Becker, R., Wolf, D., Gerchen, M. F., Sartorius, A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30978-1 · Cited by 24
Ma, S., Zhong, H., Liu, X., & Wang, L. (2023) · Journal of Molecular Neuroscience · doi:10.1007/s12031-023-02121-2 · Cited by 24
Viral vector-based tools advance knowledge of basal ganglia anatomy and physiology
Sizemore, R. J., Seeger-Armbruster, S., Hughes, S. M., & Parr‐Brownlie, L. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.01131.2015 · Cited by 24
Deciphering Memory Function with Optogenetics
Beyeler, A., Eckhardt, C. A., & Tye, K. M. (2014) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-420170-5.00012-x · Cited by 24
Mesencephalic representations of recent experience influence decision making
Thompson, J. A., Costabile, J. D., & Felsen, G. (2016) · eLife · doi:10.7554/elife.16572 · Cited by 24
Rubin, J. E., Vich, C., Clapp, M., Noneman, K. K., & Verstynen, T. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14745 · Cited by 24
Learning differentially shapes prefrontal and hippocampal activity during classical conditioning
Klee, J. L., Souza, B. C., & Battaglia, F. P. (2021) · eLife · doi:10.7554/elife.65456 · Cited by 24
Bidirectional role of dopamine in learning and memory-active forgetting
Díaz, F. C., Caffino, L., & Fumagalli, F. (2021) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2021.10.011 · Cited by 24
Food-Related Odors and the Reward Circuit: Functional MRI
Frasnelli, J., Hummel, C., Bojanowski, V., Warr, J., Gerber, J. C., & Hummel, T. R. (2015) · Chemosensory Perception · doi:10.1007/s12078-015-9193-8 · Cited by 24
Dopamine system: manager of neural pathways
Hong, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00854 · Cited by 24
Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders
Liebenow, B., Jones, R., DiMarco, E. K., Trattner, J. D., Humphries, J. B., Sands, L. P., Spry, K. P., Johnson, C. K., Farkas, E. B., Jiang, A., et al. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.886297 · Cited by 23
Amo, R., Uchida, N., & Watabe‐Uchida, M. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.019 · Cited by 23
Self-citationTestosterone dynamics during encounter: role of emotional factors
Chichinadze, K., Lazarashvili, A., Chichinadze, N., & Gachechiladze, L. (2012) · Journal of Comparative Physiology A · doi:10.1007/s00359-012-0726-1 · Cited by 23
Alpha 1 -adrenergic receptor blockade in the VTA modulates fear memories and stress responses
Solecki, W., Szklarczyk, K., Klasa, A., Pradel, K., Dobrzański, G., & Przewłocki, R. (2017) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2017.05.008 · Cited by 23
A nanofabricated optoelectronic probe for manipulating and recording neural dynamics
Li, B., Lee, K., Masmanidis, S. C., & Li, M. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aabc94 · Cited by 23
Solari, N., Sviatkó, K., Laszlovszky, T., Hegedüs, P., & Hangya, B. (2018) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00018 · Cited by 23
Parise, L. F., Warren, B. L., Parise, E. M., Iñiguez, S. D., & Bolaños‐Guzmán, C. A. (2014) · Psychopharmacology · doi:10.1007/s00213-014-3434-4 · Cited by 23
Reward Anticipation Is Encoded Differently by Adolescent Ventral Tegmental Area Neurons
Kim, Y., Simon, N. W., Wood, J. T., & Moghaddam, B. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.04.026 · Cited by 23
Bai, M., Zhu, X., Zhang, L., Zhang, Y., Xue, L., Wang, Y., Zhong, M., & Zhang, X. (2017) · Brain and Behavior · doi:10.1002/brb3.808 · Cited by 23
Neuropixels Opto: combining high-resolution electrophysiology and optogenetics
Lakunina, A. A., Socha, K. Z., Ladd, A., Bowen, A. J., Chen, S., Colonell, J. I., Doshi, A., Karsh, B., Krumin, M., Kulik, P., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03076-z · Cited by 22
Impact of distinct neurotransmitter release modes on neuronal signaling
Zhang, K. J., Monteggia, L. M., & Kavalali, E. T. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03373-7 · Cited by 22
The Basal Ganglia and Hierarchical Control in Voluntary Behavior
Yin, H. H. (2016) · Innovations in cognitive neuroscience · doi:10.1007/978-3-319-42743-0_20 · Cited by 22
Synaptic dysfunction of Aldh1a1 neurons in the ventral tegmental area causes impulsive behaviors
Li, X., Chen, W., Huang, X., Jing, W., Zhang, T., Yu, Q., Yu, H., Li, H., Tian, Q., Ding, Y., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00494-9 · Cited by 23
Suhaimi, A. M. M., Lim, A. W. H., Chia, X. W., Li, C., & Makino, H. (2022) · Science Advances · doi:10.1126/sciadv.abn0984 · Cited by 22
Yamada, K., & Toda, K. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.1045764 · Cited by 22
Dopamine transients encode reward prediction errors independent of learning rates
Mah, A., Golden, C. E. M., & Constantinople, C. M. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114840 · Cited by 22
A ventral hippocampal-lateral septum pathway regulates social novelty preference
Rashid, M., Thomas, S., Isaac, J., Karkare, S. C., Klein, H., & Murugan, M. (2024) · eLife · doi:10.7554/elife.97259.2 · Cited by 22
Arco, A. D., Park, J., & Moghaddam, B. (2020) · eNeuro · doi:10.1523/eneuro.0029-20.2020 · Cited by 23
Synaptic inhibition in the lateral habenula shapes reward anticipation
Lalive, A. L., Congiu, M., Lewis, C., Groos, D., Clerke, J. A., Tchenio, A., Ge, Y., Helmchen, F., & Mameli, M. (2022) · Current Biology · doi:10.1016/j.cub.2022.02.035 · Cited by 22
Wolf, D., Hartig, R., Zhuo, Y., Scheller, M. F., Articus, M., Moor, M., Grinevich, V., Linster, C., Russo, E., Weber‐Fahr, W., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50113-6 · Cited by 21
Gestational immune activation disrupts hypothalamic neurocircuits of maternal care behavior
Zambon, A., Rico, L. C., Herman, M., Gundacker, A., Telalovic, A., Hartenberger, L.-M., Kuehn, R., Romanov, R. A., Hussaini, S. A., Harkany, T., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01602-x · Cited by 21
Post, R. J., Bulkin, D. A., Bulkin, D. A., Ebitz, R. B., Lee, V., Han, K., & Warden, M. R. (2022) · Current Biology · doi:10.1016/j.cub.2022.08.016 · Cited by 21
Igarashi, K. M., Lee, J. Y., & Jun, H. (2022) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2022.102641 · Cited by 22
Undermatching Is a Consequence of Policy Compression
Bari, B. A., & Gershman, S. J. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1003-22.2022 · Cited by 21
Coordinating brain-distributed network activities in memory resistant to extinction
Clarke-Williams, C. J., Lopes‐dos‐Santos, V., Lefèvre, L., Brizee, D., Causse, A. A., Rothaermel, R., Hartwich, K., Perestenko, P. V., Toth, R. A., McNamara, C. G., et al. (2024) · Cell · doi:10.1016/j.cell.2023.12.018 · Cited by 21
Silkstone, M., & Brudzyński, S. M. (2019) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2019.172815 · Cited by 21
Accumbal D2R-medium spiny neurons regulate aversive behaviors through PKA-Rap1 pathway
Lin, Y.-H., Yamahashi, Y., Kuroda, K., Faruk, M. O., Zhang, X., Yamada, K., Yamanaka, A., Nagai, T., & Kaibuchi, K. (2020) · Neurochemistry International · doi:10.1016/j.neuint.2020.104935 · Cited by 21
Flerlage, W. J., Langlois, L. D., Rusnak, M., Simmons, S. C., Gouty, S., Armstrong, R. C., Cox, B. M., Symes, A. J., Tsuda, M. C., & Nugent, F. S. (2022) · Journal of Neurotrauma · doi:10.1089/neu.2022.0224 · Cited by 21
Cavalcanti, J. R. L. D. P., de Pontes, A. L. B., Fiuza, F. P., Silva, K. D. A., Gúzen, F. P., Lucena, E. E. D. S., do Nascimento, E. S., Cavalcante, J. C., Costa, M. S. M. O., Engelberth, R. C. G. J., et al. (2016) · Journal of Chemical Neuroanatomy · doi:10.1016/j.jchemneu.2016.05.010 · Cited by 21
The Computational Nature of Memory ModificationPreprint
Gershman, S. J., Monfils, M., Norman, K. A., & Niv, Y. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/036442 · Cited by 21
Neural Networks With Motivation
Shuvaev, S. A., Tran, N. B., Stephenson‐Jones, M., Li, B., & Koulakov, A. A. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2020.609316 · Cited by 20
Stressed and wired: The effects of stress on the VTA circuits underlying motivated behavior
Lowes, D. C., & Harris, A. Z. (2022) · Current Opinion in Endocrine and Metabolic Research · doi:10.1016/j.coemr.2022.100388 · Cited by 20
Mitten, E. H., Souders, A., de Velasco, E. M. F., & Wickman, K. (2024) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2024.1425607 · Cited by 20
Reward Bases: A simple mechanism for adaptive acquisition of multiple reward types
Millidge, B., Song, Y., Lak, A., Walton, M. E., & Bogacz, R. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012580 · Cited by 20
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
