# Publications Download 73 citations download- [BibTeX](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=bibtex) - [EndNote X3 XML](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=endnote8) - [EndNote 7 XML](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=endnote7) - [Endnote tagged](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=tagged) - [Marc](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=marc) - [PubMedId](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=pubmed_id) - [RIS](/bibcite/export?pager_style=show_more&number_of_items=40&sort_field=bibcite_year--desc&taxonomy_filters=&&&format=ris) ### 2026 Green, I.; Iyer, E. S.; Kang, A.; Uchida, N.; Watabe-Uchida, M. [Dopamine in the Ventral and Tail of Striatum Supports Global and Local Evaluation in Reward-Threat Conflict](/publication/dopamine-ventral-and-tail-striatum-supports-global-and-local-evaluation-reward-threat). *bioRxiv* **2026**. https://doi.org/https://doi.org/10.64898/2026.05.01.722240. Green, I.; Iyer, E. S.; Kang, A.; Uchida, N.; Watabe-Uchida, M. [Dopamine in the Ventral and Tail of Striatum Supports Global and Local Evaluation in Reward-Threat Conflict](/publication/dopamine-ventral-and-tail-striatum-supports-global-and-local-evaluation-reward-threat). *bioRxiv* **2026**. https://doi.org/https://doi.org/10.64898/2026.05.01.722240. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2026.05.01.722240v1.abstract) Survival requires balancing reward seeking and threat avoidance, yet how distinct dopamine systems coordinate to support this remains unclear. Using a naturalistic foraging paradigm in which mice pursue water reward under threat from a monster object, we... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2026.05.01.722240v1.abstract) Doi, Y.; Asaka, M.; Born, R.; Yanagihara, D.; Uchida, N. [A Novel Behavioral Paradigm Using Mice to Study Predictive Postural Control](/publications/novel-behavioral-paradigm-using-mice-study-predictive-postural-control). *Front Neurosci* **2026**, *20*. https://doi.org/https://doi.org/10.3389/fnins.2026.1790603. Doi, Y.; Asaka, M.; Born, R.; Yanagihara, D.; Uchida, N. [A Novel Behavioral Paradigm Using Mice to Study Predictive Postural Control](/publications/novel-behavioral-paradigm-using-mice-study-predictive-postural-control). *Front Neurosci* **2026**, *20*. https://doi.org/https://doi.org/10.3389/fnins.2026.1790603. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1790603/full) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/2026-05/fnins-20-1790603_0.pdf) Postural control circuitry performs the essential function of maintaining balance and body position in response to perturbations that are either self-generated (e.g., reaching to pick up an object) or externally delivered (e.g., being pushed by another... - [ descriptionPublisher's Version](https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2026.1790603/full) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/2026-05/fnins-20-1790603_0.pdf) Hakim, R.; Jaggi, A.; Heo, G.; Matsumoto, H.; Uchida, N.; Watabe-Uchida, M.; Datta, S. R.; Rusall, S.; Sabatini, B. L. [Spectral Envelopes of Facial Movements Predict Intention, Cortical Representations, and Neural Prosthetic Control](/publication/spectral-envelopes-facial-movements-predict-intention-cortical-representations-and). *bioRxiv* **2026**. https://doi.org/https://doi.org/10.1101/2025.09.10.675423. Hakim, R.; Jaggi, A.; Heo, G.; Matsumoto, H.; Uchida, N.; Watabe-Uchida, M.; Datta, S. R.; Rusall, S.; Sabatini, B. L. [Spectral Envelopes of Facial Movements Predict Intention, Cortical Representations, and Neural Prosthetic Control](/publication/spectral-envelopes-facial-movements-predict-intention-cortical-representations-and). *bioRxiv* **2026**. https://doi.org/https://doi.org/10.1101/2025.09.10.675423. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.09.10.675423v3) Animals, including humans, use coordinated facial movements to sample the environment, ingest nutrients, and communicate. Rodents, in particular, produce rhythmic facial movements during spontaneous behavior and cognitive tasks. Measuring these movements... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.09.10.675423v3) Hennig, J. A.; Burrell, M.; Uchida, N.; Gershman, S. J. [Phasic Dopamine Drives Conditioned Responding Beyond Its Role in Learning](https://www.biorxiv.org/content/10.64898/2026.03.25.714259v1.abstract). *bioRxiv* **2026**. Hennig, J. A.; Burrell, M.; Uchida, N.; Gershman, S. J. [Phasic Dopamine Drives Conditioned Responding Beyond Its Role in Learning](https://www.biorxiv.org/content/10.64898/2026.03.25.714259v1.abstract). *bioRxiv* **2026**. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2026.03.25.714259v1.abstract) Animals exposed to pairings of a neutral stimulus with reward acquire a conditioned response to the neutral stimulus. A prominent hypothesis, formalized in the Temporal Difference (TD) learning algorithm, is that animals learn to predict the future reward... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2026.03.25.714259v1.abstract) Lee, J.; Hennig, J. A.; Frelih, V.; Gershman, S. J.; Uchida, N. [Emergence of Rapid Value Inference through Meta-Reinforcement Learning](/publication/emergence-rapid-value-inference-through-meta-reinforcement-learning). *bioRxiv* **2026**. Lee, J.; Hennig, J. A.; Frelih, V.; Gershman, S. J.; Uchida, N. [Emergence of Rapid Value Inference through Meta-Reinforcement Learning](/publication/emergence-rapid-value-inference-through-meta-reinforcement-learning). *bioRxiv* **2026**. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2025.11.30.691382v3) The ability to estimate the value associated with a specific stimulus or action is essential for adaptive behavior. Value can be updated either incrementally through experience or rapidly by inference based on latent environmental structure. Yet, how the... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.64898/2025.11.30.691382v3) ### 2025 Kamath, T.; Lodder, B.; Bilsel, E.; Green, I.; Dalangin, R.; Raghubardayal, M.; Wang, W.; Capelli, P.; Legister, J.; Timmins, J.; Hulshof, L.; Wallace, J. B.; Tian, L.; Uchida, N.; Watabe-Uchida, M.; Sabatini, B. L. [Hunger Modulates Exploration through Suppression of Dopamine Signaling in the Tail of the Striatum](/publication/hunger-modulates-exploration-through-suppression-dopamine-signaling-tail-striatum-0). *Neuron* **2025**, *113* (23), 4055-4068. Kamath, T.; Lodder, B.; Bilsel, E.; Green, I.; Dalangin, R.; Raghubardayal, M.; Wang, W.; Capelli, P.; Legister, J.; Timmins, J.; Hulshof, L.; Wallace, J. B.; Tian, L.; Uchida, N.; Watabe-Uchida, M.; Sabatini, B. L. [Hunger Modulates Exploration through Suppression of Dopamine Signaling in the Tail of the Striatum](/publication/hunger-modulates-exploration-through-suppression-dopamine-signaling-tail-striatum-0). *Neuron* **2025**, *113* (23), 4055-4068. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.cell.com/neuron/fulltext/S0896-6273(25)00697-X) Caloric depletion induces behavioral changes that help an animal find food and restore its homeostatic balance. Hunger increases exploration and risk-taking behavior, allowing an animal to forage for food despite risks; however, it is unknown which neural... - [ descriptionPublisher's Version](https://www.cell.com/neuron/fulltext/S0896-6273(25)00697-X) Kingsbury, L.; Zhang, G.; Sanguinetti-Scheck, J. I.; Uchida, N. [Context-Specific Configuration of Orthogonal Integrator Dynamics for Flexible Foraging Decisions](/publication/context-specific-configuration-orthogonal-integrator-dynamics-flexible-foraging). *bioRxiv* **2025**. Kingsbury, L.; Zhang, G.; Sanguinetti-Scheck, J. I.; Uchida, N. [Context-Specific Configuration of Orthogonal Integrator Dynamics for Flexible Foraging Decisions](/publication/context-specific-configuration-orthogonal-integrator-dynamics-flexible-foraging). *bioRxiv* **2025**. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.11.14.688540v1.abstract) The capacity to adapt behavior across different contexts is a fundamental feature of intelligence and is crucial for animal life, yet the mechanisms by which neural circuits are contextually reconfigured to alter their function are poorly understood. As a... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.11.14.688540v1.abstract) Bukwich, M.; Campbell, M. G.; Zoltowski, D.; Kingsbury, L.; Tomov, M. S.; Stern, J.; Kim, H. R.; Drugowitsch, J.; Linderman, S. W.; Uchida, N. [Competitive Integration of Time and Reward Explains Value-Sensitive Foraging Decisions and Frontal Cortex Ramping Dynamics](/publication/competitive-integration-time-and-reward-explains-value-sensitive-foraging-decisions-and). *Neuron* **2025**, *113* (20), 3458-3475. Bukwich, M.; Campbell, M. G.; Zoltowski, D.; Kingsbury, L.; Tomov, M. S.; Stern, J.; Kim, H. R.; Drugowitsch, J.; Linderman, S. W.; Uchida, N. [Competitive Integration of Time and Reward Explains Value-Sensitive Foraging Decisions and Frontal Cortex Ramping Dynamics](/publication/competitive-integration-time-and-reward-explains-value-sensitive-foraging-decisions-and). *Neuron* **2025**, *113* (20), 3458-3475. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.cell.com/neuron/abstract/S0896-6273(25)00515-X) - [Preview](https://www.sciencedirect.com/science/article/pii/S0896627325007020) Patch foraging is a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch... - [ descriptionPublisher's Version](https://www.cell.com/neuron/abstract/S0896-6273(25)00515-X) - [Preview](https://www.sciencedirect.com/science/article/pii/S0896627325007020) Campbell, M. G.; Ra, Y.; Chen, Z.; Xu, S.; Burrell, M.; Matias, S.; Watabe-Uchida, M.; Uchida, N. [A Hardwired Neural Circuit for Temporal Difference Learning](/publication/hardwired-neural-circuit-temporal-difference-learning). *bioRxiv* **2025**. Campbell, M. G.; Ra, Y.; Chen, Z.; Xu, S.; Burrell, M.; Matias, S.; Watabe-Uchida, M.; Uchida, N. [A Hardwired Neural Circuit for Temporal Difference Learning](/publication/hardwired-neural-circuit-temporal-difference-learning). *bioRxiv* **2025**. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.09.18.677203v3) The neurotransmitter dopamine plays a major role in learning by acting as a teaching signal to update the brain’s predictions about rewards. A leading theory proposes that this process is analogous to a reinforcement learning algorithm called temporal... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2025.09.18.677203v3) Romero-Pinto, S.; Uchida, N. [Tonic Dopamine and Biases in Value Learning Linked through a Biologically Inspired Reinforcement Learning Model](/publication/tonic-dopamine-and-biases-value-learning-linked-through-biologically-inspired). *Nature Communications* **2025**, *16*, 7529. Romero-Pinto, S.; Uchida, N. [Tonic Dopamine and Biases in Value Learning Linked through a Biologically Inspired Reinforcement Learning Model](/publication/tonic-dopamine-and-biases-value-learning-linked-through-biologically-inspired). *Nature Communications* **2025**, *16*, 7529. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41467-025-62280-1) A hallmark of various psychiatric disorders is biased future predictions. Here we examined the mechanisms for biased value learning using reinforcement learning models incorporating recent findings on synaptic plasticity and opponent circuit mechanisms in... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41467-025-62280-1) Masset, P.; Tano, P.; Kim, H. R.; Malik, A. N.; Pouget, A.; Uchida, N. [Multi-Timescale Reinforcement Learning in the Brain](/publication/multi-timescale-reinforcement-learning-brain). *Nature* **2025**, *642*, 682-690. Masset, P.; Tano, P.; Kim, H. R.; Malik, A. N.; Pouget, A.; Uchida, N. [Multi-Timescale Reinforcement Learning in the Brain](/publication/multi-timescale-reinforcement-learning-brain). *Nature* **2025**, *642*, 682-690. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-025-08929-9) - [Research Briefing](https://www.nature.com/articles/d41586-025-01867-6) To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behaviour can be learned through reinforcement learning, a class of algorithms that has been successful at... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-025-08929-9) - [Research Briefing](https://www.nature.com/articles/d41586-025-01867-6) Tolooshams, B.; Matias, S.; Wu, H.; Temereanca, S.; Uchida, N.; Murthy, V. N.; Masset, P.; Ba, D. [Interpretable Deep Learning for Deconvolutional Analysis of Neural Signals](/publication/interpretable-deep-learning-deconvolutional-analysis-neural-signals). *Neuron* **2025**, *113* (8), 1151-1168. Tolooshams, B.; Matias, S.; Wu, H.; Temereanca, S.; Uchida, N.; Murthy, V. N.; Masset, P.; Ba, D. [Interpretable Deep Learning for Deconvolutional Analysis of Neural Signals](/publication/interpretable-deep-learning-deconvolutional-analysis-neural-signals). *Neuron* **2025**, *113* (8), 1151-1168. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.cell.com/neuron/abstract/S0896-6273(25)00119-9) The widespread adoption of deep learning to model neural activity often relies on “black-box” approaches that lack an interpretable connection between neural activity and network parameters. Here, we propose using algorithm unrolling, a method for... - [ descriptionPublisher's Version](https://www.cell.com/neuron/abstract/S0896-6273(25)00119-9) Qian, L.; Burrell, M.; Hennig, J. A.; Matias, S.; Murthy, V. N.; Gershman, S. J.; Uchida, N. [Prospective Contingency Explains Behavior and Dopamine Signals During Associative Learning](/publication/prospective-contingency-explains-behavior-and-dopamine-signals-during-associative). *Nature Neuroscience* **2025**, *28*, 1280–1292. Qian, L.; Burrell, M.; Hennig, J. A.; Matias, S.; Murthy, V. N.; Gershman, S. J.; Uchida, N. [Prospective Contingency Explains Behavior and Dopamine Signals During Associative Learning](/publication/prospective-contingency-explains-behavior-and-dopamine-signals-during-associative). *Nature Neuroscience* **2025**, *28*, 1280–1292. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-025-01915-4) - [News & Views](https://www.nature.com/articles/s41593-025-01980-9) Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. In the present study, we examined the dopamine activity in the... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-025-01915-4) - [News & Views](https://www.nature.com/articles/s41593-025-01980-9) Tsutsui-Kimura, I.; Tian, Z. M.; Amo, R.; Zhuo, Y.; Li, Y.; Campbell, M. G.; Uchida, N.; Watabe-Uchida, M. [Dopamine in the Tail of the Striatum Facilitates Avoidance in Threat–reward Conflicts](/publication/dopamine-tail-striatum-facilitates-avoidance-threat-reward-conflicts). *Nature Neuroscience* **2025**, *28*, 795-810. Tsutsui-Kimura, I.; Tian, Z. M.; Amo, R.; Zhuo, Y.; Li, Y.; Campbell, M. G.; Uchida, N.; Watabe-Uchida, M. [Dopamine in the Tail of the Striatum Facilitates Avoidance in Threat–reward Conflicts](/publication/dopamine-tail-striatum-facilitates-avoidance-threat-reward-conflicts). *Nature Neuroscience* **2025**, *28*, 795-810. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-025-01902-9) - [Research Highlight](https://www.nature.com/articles/s41583-025-00918-1) Responding appropriately to potential threats before they materializeis critical to avoiding disastrous outcomes. Here we examine howthreat-coping behavior is regulated by the tail of the striatum (TS) and its dopamine input. Mice were presented with a... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-025-01902-9) - [Research Highlight](https://www.nature.com/articles/s41583-025-00918-1) Liu, D.; Rahman, M.; Johnson, A.; Amo, R.; Tsutsui-Kimura, I.; Sullivan, Z. A.; Pena, N.; Talay, M.; Logeman, B. L.; Finkbeiner, S.; Qian, L.; Choi, S.; Capo-Battaglia, A.; Abdus-Saboor, I.; Ginty, D. D.; Uchida, N.; Watabe-Uchida, M.; Dulac, C. [A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis](/publication/hypothalamic-circuit-underlying-dynamic-control-social-homeostasis). *Nature* **2025**, *640*, 1000-1010. Liu, D.; Rahman, M.; Johnson, A.; Amo, R.; Tsutsui-Kimura, I.; Sullivan, Z. A.; Pena, N.; Talay, M.; Logeman, B. L.; Finkbeiner, S.; Qian, L.; Choi, S.; Capo-Battaglia, A.; Abdus-Saboor, I.; Ginty, D. D.; Uchida, N.; Watabe-Uchida, M.; Dulac, C. [A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis](/publication/hypothalamic-circuit-underlying-dynamic-control-social-homeostasis). *Nature* **2025**, *640*, 1000-1010. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-025-08617-8) Social grouping increases survival in many species, including humans1,2. By contrast, social isolation generates an aversive state (‘loneliness’) that motivates social seeking and heightens social interaction upon reunion3,4,5. The observed rebound in... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-025-08617-8) Lowet, A. S.; Zheng, Q.; Meng, M.; Matias, S.; Drugowitsch, J.; Uchida, N. [An Opponent Striatal Circuit for Distributional Reinforcement Learning](/publications/opponent-striatal-circuit-distributional-reinforcement-learning). *Nature* **2025**, *639*, 717-726. Lowet, A. S.; Zheng, Q.; Meng, M.; Matias, S.; Drugowitsch, J.; Uchida, N. [An Opponent Striatal Circuit for Distributional Reinforcement Learning](/publications/opponent-striatal-circuit-distributional-reinforcement-learning). *Nature* **2025**, *639*, 717-726. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-024-08488-5) Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards — an approach known as distributional reinforcement learning (RL)[1](https://www.biorxiv.org/content/10.1101/2024.01.02.573966v1#ref-1)... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-024-08488-5) Campbell, M. G.; Green, I.; Pinto, S. R.; Uchida, N. [Impacts of Dopamine on Learning and Behavior in Health and Disease: Insights from Optogenetics in Rodents](/publication/impacts-dopamine-learning-and-behavior-health-and-disease-insights-optogenetics-rodents). In *Encyclopedia of the Human Brain*; Elsevier, 2025; Vol. 3, pp. 355-386. Campbell, M. G.; Green, I.; Pinto, S. R.; Uchida, N. [Impacts of Dopamine on Learning and Behavior in Health and Disease: Insights from Optogenetics in Rodents](/publication/impacts-dopamine-learning-and-behavior-health-and-disease-insights-optogenetics-rodents). In *Encyclopedia of the Human Brain*; Elsevier, 2025; Vol. 3, pp. 355-386. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/referencework/abs/pii/B9780128204801001315) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/2026-01/PDF.pdf) Dopamine neurons in the ventral midbrain play important roles in various behavioral functions such as learning, motivation, and movement. The exact mechanisms and algorithms by which dopamine regulates these functions, however, remain to be clarified... - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/referencework/abs/pii/B9780128204801001315) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/2026-01/PDF.pdf) Uchida, N.; Watabe-Uchida, M.; Green, I. [Chapter 19. Diversity of Encoding: Reward to Aversion](/publication/chapter-19-diversity-encoding-reward-aversion). In *The Handbook of Dopamine*; Elsevier, 2025; Vol. 32, pp. 237-249. Uchida, N.; Watabe-Uchida, M.; Green, I. [Chapter 19. Diversity of Encoding: Reward to Aversion](/publication/chapter-19-diversity-encoding-reward-aversion). In *The Handbook of Dopamine*; Elsevier, 2025; Vol. 32, pp. 237-249. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/handbook/abs/pii/B9780443298677000232) This chapter discusses diversity in the signaling properties of dopamine neurons, with a particular focus on their activity and function in response to reward and threat. While dopamine neurons were traditionally reported to be excited by reward-related... - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/handbook/abs/pii/B9780443298677000232) Watabe-Uchida, M.; Amo, R. [Chapter 5. Dopamine Neuron Connectomes: Inputs and Outputs](/publication/chapter-5-dopamine-neuron-connectomes-inputs-and-outputs). In *The Handbook of Dopamine*; Elsevier, 2025; Vol. 32, pp. 49-62. Watabe-Uchida, M.; Amo, R. [Chapter 5. Dopamine Neuron Connectomes: Inputs and Outputs](/publication/chapter-5-dopamine-neuron-connectomes-inputs-and-outputs). In *The Handbook of Dopamine*; Elsevier, 2025; Vol. 32, pp. 49-62. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/handbook/abs/pii/B9780443298677000098) In this chapter, we discuss anatomical and functional inputs and outputs of dopamine neurons. Classical tracing studies show that the midbrain areas where dopamine neurons are located receive projections from various brain areas, and dopamine neurons, in... - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/chapter/handbook/abs/pii/B9780443298677000098) ### 2024 Gershman, S. J.; Assad, J.; Datta, S. R.; Linderman, S.; Sabatini, B. L.; Uchida, N.; Wilbrecht, L. [Explaining Dopamine through Prediction Errors and Beyond](/publications/explaining-dopamine-through-prediction-errors-and-beyond). *Nature Neuroscience* **2024**, *27*, 1645-1655. Gershman, S. J.; Assad, J.; Datta, S. R.; Linderman, S.; Sabatini, B. L.; Uchida, N.; Wilbrecht, L. [Explaining Dopamine through Prediction Errors and Beyond](/publications/explaining-dopamine-through-prediction-errors-and-beyond). *Nature Neuroscience* **2024**, *27*, 1645-1655. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-024-01705-4) The most influential account of phasic dopamine holds that it reports reward prediction errors (RPEs). The RPE-based interpretation of dopamine signaling is, in its original form, probably too simple and fails to explain all the properties of phasic... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-024-01705-4) Cai, X.; Liu, C.; Tsutsui-Kimura, I.; Lee, J.-H.; Guo, C.; Banarjee, A.; Lee, J.; Amo, R.; Xie, Y.; Patriarchi, T.; Li, Y.; Watabe-Uchida, M.; Uchida, N.; Kaeser, P. S. [Dopamine Dynamics Are Dispensable for Movement But Promote Reward Responses](/publications/dopamine-dynamics-are-dispensable-movement-promote-reward-responses). *Nature* **2024**. Cai, X.; Liu, C.; Tsutsui-Kimura, I.; Lee, J.-H.; Guo, C.; Banarjee, A.; Lee, J.; Amo, R.; Xie, Y.; Patriarchi, T.; Li, Y.; Watabe-Uchida, M.; Uchida, N.; Kaeser, P. S. [Dopamine Dynamics Are Dispensable for Movement But Promote Reward Responses](/publications/dopamine-dynamics-are-dispensable-movement-promote-reward-responses). *Nature* **2024**. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-024-08038-z) Dopamine signalling modes differ in kinetics and spatial patterns of receptor activation. How these modes contribute to motor function, motivation and learning has long been debated. Here we show that action-potential-induced dopamine release is... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-024-08038-z) Lowet, A. S.; Uchida, N. [Predictive Coding: A Distinction - Without a Difference](/publications/predictive-coding-distinction-without-difference). *Curr. Biol.* **2024**, *34* (20), R926-R929. Lowet, A. S.; Uchida, N. [Predictive Coding: A Distinction - Without a Difference](/publications/predictive-coding-distinction-without-difference). *Curr. Biol.* **2024**, *34* (20), R926-R929. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.cell.com/current-biology/abstract/S0960-9822(24)01237-5) Influential models hold that neurons in the cerebral cortex signal the difference between actual and expected inputs. A new study instead finds that cortical prediction errors reflect not subtraction but rather stimulus-specific amplification of... - [ descriptionPublisher's Version](https://www.cell.com/current-biology/abstract/S0960-9822(24)01237-5) Amo, R.; Uchida, N.; Watabe-Uchida, M. [Glutamate Inputs Send Prediction Error of Reward But Not Negative Value of Aversive Stimuli to Dopamine Neurons](https://www-cell-com.ezp-prod1.hul.harvard.edu/neuron/fulltext/S0896-6273(23)00979-0). *Neuron* **2024**, *112* (6), 1001-1019. Amo, R.; Uchida, N.; Watabe-Uchida, M. [Glutamate Inputs Send Prediction Error of Reward But Not Negative Value of Aversive Stimuli to Dopamine Neurons](https://www-cell-com.ezp-prod1.hul.harvard.edu/neuron/fulltext/S0896-6273(23)00979-0). *Neuron* **2024**, *112* (6), 1001-1019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2023.11.09.566472v1) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/2023.11.09.566472v1.full_.pdf) Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), but the mechanisms underlying RPE computation, particularly the contributions of different neurotransmitters, remain poorly understood. Here, we used a genetically encoded... - [ descriptionPublisher's Version](https://www.biorxiv.org/content/10.1101/2023.11.09.566472v1) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/2023.11.09.566472v1.full_.pdf) ### 2023 Hennig, J.; Pinto, S. R.; Yamaguchi, T.; Linderman, S.; Uchida, N.; Gershman, S. J. [Emergence of Belief-Like Representations through Reinforcement Learning](/publications/emergence-belief-representations-through-reinforcement-learning). *PLoS Comput Biol.* **2023**, *19* (9), e1011067. Hennig, J.; Pinto, S. R.; Yamaguchi, T.; Linderman, S.; Uchida, N.; Gershman, S. J. [Emergence of Belief-Like Representations through Reinforcement Learning](/publications/emergence-belief-representations-through-reinforcement-learning). *PLoS Comput Biol.* **2023**, *19* (9), e1011067. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011067) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/journal.pcbi_.1011067_1.pdf) To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using... - [ descriptionPublisher's Version](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011067) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/journal.pcbi_.1011067_1.pdf) Markowitz, J. E.; Gillis, W.; Jay, M.; Wood, J.; Harris, R.; Cieszkowski, R.; Scott, R.; Brann, D.; Koveal, D.; Kula, T.; Weinreb, C.; Osman, M. A. M.; Pinto, S. R.; Uchida, N.; Linderman, S.; Sabatini, B. L.; Datta, S. R. [Spontaneous Behaviour Is Structured by Reinforcement Without Explicit Reward](/publications/spontaneous-behaviour-structured-reinforcement-without-explicit-reward). *Nature* **2023**, *617* (7946), 108-117. Markowitz, J. E.; Gillis, W.; Jay, M.; Wood, J.; Harris, R.; Cieszkowski, R.; Scott, R.; Brann, D.; Koveal, D.; Kula, T.; Weinreb, C.; Osman, M. A. M.; Pinto, S. R.; Uchida, N.; Linderman, S.; Sabatini, B. L.; Datta, S. R. [Spontaneous Behaviour Is Structured by Reinforcement Without Explicit Reward](/publications/spontaneous-behaviour-structured-reinforcement-without-explicit-reward). *Nature* **2023**, *617* (7946), 108-117. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-022-05611-2) Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences[1](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-022-05611-2#ref-CR1),[2](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-022-05611-2#ref-CR2). However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41586-022-05611-2) ### 2022 Amo, R.; Matias, S.; Yamanaka, A.; Tanaka, K. F.; Uchida, N.; Watabe-Uchida, M. [A Gradual Temporal Shift of Dopamine Responses Mirrors the Progression of Temporal Difference Error in Machine Learning](/publications/gradual-backward-shift-dopamine-responses-during-associative-learning). *Nature Neuroscience* **2022**, *25*, 1082-1092. Amo, R.; Matias, S.; Yamanaka, A.; Tanaka, K. F.; Uchida, N.; Watabe-Uchida, M. [A Gradual Temporal Shift of Dopamine Responses Mirrors the Progression of Temporal Difference Error in Machine Learning](/publications/gradual-backward-shift-dopamine-responses-during-associative-learning). *Nature Neuroscience* **2022**, *25*, 1082-1092. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-022-01109-2) A large body of evidence has indicated that the phasic responses of midbrain dopamine neurons show a remarkable similarity to a type of teaching signal (temporal difference (TD) error) used in machine learning. However, previous studies failed to observe... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-022-01109-2) Mikhael, J. G.; Kim, H. R.; Uchida, N.; Gershman, S. J. [The Role of State Uncertainty in the Dynamics of Dopamine](/publications/role-state-uncertainty-dynamics-dopamine). *Curr Biol* **2022**, *32* (5), 1077-1087. Mikhael, J. G.; Kim, H. R.; Uchida, N.; Gershman, S. J. [The Role of State Uncertainty in the Dynamics of Dopamine](/publications/role-state-uncertainty-dynamics-dopamine). *Curr Biol* **2022**, *32* (5), 1077-1087. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0960982222000367?via%3Dihub) - [Dispatch](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0960982222001324) Reinforcement learning models of the basal ganglia map the phasic dopamine signal to reward prediction errors (RPEs). Conventional models assert that, when a stimulus predicts a reward with fixed delay, dopamine activity during the delay should converge... - [ descriptionPublisher's Version](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0960982222000367?via%3Dihub) - [Dispatch](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0960982222001324) 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. [Striatal Dopamine Explains Novelty-Induced Behavioral Dynamics and Individual Variability in Threat Prediction](/publications/striatal-dopamine-explains-novelty-induced-behavioral-dynamics-and-individual). *Neuron* **2022**, *110* (22), 3789-3804. 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. [Striatal Dopamine Explains Novelty-Induced Behavioral Dynamics and Individual Variability in Threat Prediction](/publications/striatal-dopamine-explains-novelty-induced-behavioral-dynamics-and-individual). *Neuron* **2022**, *110* (22), 3789-3804. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0896627322007589?via%3Dihub) Animals both explore and avoid novel objects in the environment, but the neural mechanisms that underlie these behaviors and their dynamics remain uncharacterized. Here, we used multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq) to... - [ descriptionPublisher's Version](https://www-sciencedirect-com.ezp-prod1.hul.harvard.edu/science/article/pii/S0896627322007589?via%3Dihub) ### 2021 Starkweather, C. K.; Uchida, N. [Dopamine Signals As Temporal Difference Errors: Recent Advances](/publications/dopamine-signals-temporal-difference-errors-recent-advances). *Curr Opin Neurobiol* **2021**, *67*, 95-105. Starkweather, C. K.; Uchida, N. [Dopamine Signals As Temporal Difference Errors: Recent Advances](/publications/dopamine-signals-temporal-difference-errors-recent-advances). *Curr Opin Neurobiol* **2021**, *67*, 95-105. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://doi.org/10.1016/j.conb.2020.08.014) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/starkweather_and_uchida_2020.pdf) In the brain, dopamine is thought to drive reward-based learning by signaling temporal difference reward prediction errors (TD errors), a ‘teaching signal’ used to train computers. Recent studies using optogenetic manipulations have provided multiple... - [ descriptionPublisher's Version](https://doi.org/10.1016/j.conb.2020.08.014) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/starkweather_and_uchida_2020.pdf) ### 2020 Lak, A.; Hueske, E.; Hirokawa, J.; Masset, P.; Ott, T.; Urai, A. E.; Donner, T.; Carandini, M.; Tonegawa, S.; Uchida, N.; Kepecs, A. [Reinforcement Biases Subsequent Perceptual Decisions When Confidence Is Low: A Widespread Behavioral Phenomenon](/publications/reinforcement-biases-subsequent-perceptual-decisions-when-confidence-low). *eLife* **2020**, *9*. Lak, A.; Hueske, E.; Hirokawa, J.; Masset, P.; Ott, T.; Urai, A. E.; Donner, T.; Carandini, M.; Tonegawa, S.; Uchida, N.; Kepecs, A. [Reinforcement Biases Subsequent Perceptual Decisions When Confidence Is Low: A Widespread Behavioral Phenomenon](/publications/reinforcement-biases-subsequent-perceptual-decisions-when-confidence-low). *eLife* **2020**, *9*. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://elifesciences.org/articles/49834) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/elife-49834-v3.pdf) Learning from successes and failures often improves the quality of subsequent decisions. Past outcomes, however, should not influence purely perceptual decisions after task acquisition is complete since these are designed so that only sensory evidence... - [ descriptionPublisher's Version](https://elifesciences.org/articles/49834) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/elife-49834-v3.pdf) Lowet, A. S.; Zheng, Q.; Matias, S.; Drugowitsch, J.; Uchida, N. [Distributional Reinforcement Learning in the Brain](/publications/distributional-reinforcement-learning-brain). *Trends Neurosci.* **2020**, *43*, 980-997. Lowet, A. S.; Zheng, Q.; Matias, S.; Drugowitsch, J.; Uchida, N. [Distributional Reinforcement Learning in the Brain](/publications/distributional-reinforcement-learning-brain). *Trends Neurosci.* **2020**, *43*, 980-997. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://doi.org/10.1016/j.tins.2020.09.004) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/lowet_et_al_tins_2020.pdf) Learning about rewards and punishments is critical for survival. Classical studies have demonstrated an impressive correspondence between the firing of dopamine neurons in the mammalian midbrain and the reward prediction errors of reinforcement learning... - [ descriptionPublisher's Version](https://doi.org/10.1016/j.tins.2020.09.004) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/lowet_et_al_tins_2020.pdf) Tsutsui-Kimura, I.; Matsumoto, H.; Akiti, K.; Yamada, M. M.; Uchida, N.; Watabe-Uchida, M. [Distinct Temporal Difference Error Signals in Dopamine Axons in Three Regions of the Striatum in a Decision-Making Task](/publications/distinct-temporal-difference-error-signals-dopamine-axons-three-regions). *eLife* **2020**, *9:e62390*. Tsutsui-Kimura, I.; Matsumoto, H.; Akiti, K.; Yamada, M. M.; Uchida, N.; Watabe-Uchida, M. [Distinct Temporal Difference Error Signals in Dopamine Axons in Three Regions of the Striatum in a Decision-Making Task](/publications/distinct-temporal-difference-error-signals-dopamine-axons-three-regions). *eLife* **2020**, *9:e62390*. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://elifesciences.org/articles/62390) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/elife-62390-v1.pdf) Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral... - [ descriptionPublisher's Version](https://elifesciences.org/articles/62390) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/elife-62390-v1.pdf) Starkweather, C. K.; Uchida, N. [Dopamine Reward Prediction Errors: The Interplay Between Experiments and Theory](/publications/dopamine-reward-prediction-errors-interplay-between-experiments-and-theory). In *The Cognitive Neuroscience*; MIT Press, 2020. Starkweather, C. K.; Uchida, N. [Dopamine Reward Prediction Errors: The Interplay Between Experiments and Theory](/publications/dopamine-reward-prediction-errors-interplay-between-experiments-and-theory). In *The Cognitive Neuroscience*; MIT Press, 2020. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://mitpress.mit.edu/books/cognitive-neurosciences-sixth-edition) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/starkweather_and_uchida.pdf) Reinforcement-learning theories provide a normative perspective on learning and decision-making. In the 1990s, neurophysiology experiments revealed an exceptional correspondence between the activity of midbrain dopamine neurons and the reward prediction... - [ descriptionPublisher's Version](https://mitpress.mit.edu/books/cognitive-neurosciences-sixth-edition) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/starkweather_and_uchida.pdf) Kim, H. R.; Malik, A. N.; Bech, P.; Tsutsui-Kimura, I.; Sun, F.; Zhang, Y.; Li, Y.; Watabe-Uchida, M.; Gershman, S. J.; Uchida, N. [A Unified Framework for Dopamine Signals across Timescales](/publications/unified-framework-dopamine-signals-across-timescales). *Cell* **2020**, *183* (6), 1600-1616. Kim, H. R.; Malik, A. N.; Bech, P.; Tsutsui-Kimura, I.; Sun, F.; Zhang, Y.; Li, Y.; Watabe-Uchida, M.; Gershman, S. J.; Uchida, N. [A Unified Framework for Dopamine Signals across Timescales](/publications/unified-framework-dopamine-signals-across-timescales). *Cell* **2020**, *183* (6), 1600-1616. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.cell.com/cell/fulltext/S0092-8674(20)31530-0) Rapid phasic activity of midbrain dopamine neurons is thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. However, recent studies describing slowly increasing dopamine signals have instead... - [ descriptionPublisher's Version](https://www.cell.com/cell/fulltext/S0092-8674(20)31530-0) Dabney, W.; Kurth-Nelson, Z.; Uchida, N.; Starkweather, C. K.; Hassabis, D.; Munos, R.; Botvinick, M. [A Distributional Code for Value in Dopamine-Based Reinforcement Learning](/publications/distributional-code-value-dopamine-based-reinforcement-learning). *Nature* **2020**, *577* (7792), 671-675. Dabney, W.; Kurth-Nelson, Z.; Uchida, N.; Starkweather, C. K.; Hassabis, D.; Munos, R.; Botvinick, M. [A Distributional Code for Value in Dopamine-Based Reinforcement Learning](/publications/distributional-code-value-dopamine-based-reinforcement-learning). *Nature* **2020**, *577* (7792), 671-675. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-019-1924-6) Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain[1](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-019-1924-6#ref-CR1),[2](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-019-1924-6#ref-CR2),[3](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-019-1924-6#ref-CR3). According to the now canonical... - [ descriptionPublisher's Version](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41586-019-1924-6) ### 2019 Uchida, N.; Gershman, S. J. [Believing in Dopamine](/publications/believing-dopamine). *Nat Rev Neurosci.* **2019**, *20* (11), 703-714. Uchida, N.; Gershman, S. J. [Believing in Dopamine](/publications/believing-dopamine). *Nat Rev Neurosci.* **2019**, *20* (11), 703-714. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41583-019-0220-7) Midbrain dopamine signals are widely thought to report reward prediction errors that drive learning in the basal ganglia. However, dopamine has also been implicated in various probabilistic computations, such as encoding uncertainty and controlling... - [ descriptionPublisher's Version](https://www-nature-com.ezp-prod1.hul.harvard.edu/articles/s41583-019-0220-7) ### 2018 Menegas, W.; Akiti, K.; Amo, R.; Uchida, N.; Watabe-Uchida, M. [Dopamine Neurons Projecting to the Posterior Striatum Reinforce Avoidance of Threatening Stimuli](/publications/dopamine-neurons-projecting-posterior-striatum-reinforce-avoidance). *Nature Neuroscience* **2018**, *21* (10), 1421–1430. Menegas, W.; Akiti, K.; Amo, R.; Uchida, N.; Watabe-Uchida, M. [Dopamine Neurons Projecting to the Posterior Striatum Reinforce Avoidance of Threatening Stimuli](/publications/dopamine-neurons-projecting-posterior-striatum-reinforce-avoidance). *Nature Neuroscience* **2018**, *21* (10), 1421–1430. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-018-0222-1) Midbrain dopamine neurons are well known for their role in reward-based reinforcement learning. We found that the activity of dopamine axons in the posterior tail of the striatum (TS) scaled with the novelty and intensity of external stimuli, but did not... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41593-018-0222-1) Babayan, B. M.; Uchida, N.; Gershman, S. J. [Belief State Representation in the Dopamine System](/publications/belief-state-representation-dopamine-system). *Nature Communications* **2018**, *9* (1), 1891. Babayan, B. M.; Uchida, N.; Gershman, S. J. [Belief State Representation in the Dopamine System](/publications/belief-state-representation-dopamine-system). *Nature Communications* **2018**, *9* (1), 1891. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](https://www.nature.com/articles/s41467-018-04397-0) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/babayan_uchida_gershman_nc2018_belief_state_representation_in_the_dopamine_system.pdf) Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has... - [ descriptionPublisher's Version](https://www.nature.com/articles/s41467-018-04397-0) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/babayan_uchida_gershman_nc2018_belief_state_representation_in_the_dopamine_system.pdf) Tye, K.; Uchida, N. [Editorial Overview: Neurobiology of Behavior](/publications/editorial-overview-neurobiology-behavior). *Current Opinion in Neurobiology* **2018**, *49* (April 2018), iv-ix. Tye, K.; Uchida, N. [Editorial Overview: Neurobiology of Behavior](/publications/editorial-overview-neurobiology-behavior). *Current Opinion in Neurobiology* **2018**, *49* (April 2018), iv-ix. - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/article/abs/pii/S0959438818300370) - [ descriptionPublisher's Version](https://www.sciencedirect.com/science/article/abs/pii/S0959438818300370) Watabe-Uchida, M.; Uchida, N. [Multiple Dopamine Systems: Weal and Woe of Dopamine](http://symposium.cshlp.org.ezp-prod1.hul.harvard.edu/content/83/83.long). *Cold Spring Harb Symp Quant Biol* **2018**, *83*, 83-95. Watabe-Uchida, M.; Uchida, N. [Multiple Dopamine Systems: Weal and Woe of Dopamine](http://symposium.cshlp.org.ezp-prod1.hul.harvard.edu/content/83/83.long). *Cold Spring Harb Symp Quant Biol* **2018**, *83*, 83-95. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ descriptionPublisher's Version](http://symposium.cshlp.org.ezp-prod1.hul.harvard.edu/content/early/2019/02/20/sqb.2018.83.037648.abstract) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/watabeuchida_uchida_cshs2019_multiple_dopamine_systems.pdf) The ability to predict future outcomes increases the fitness of the animal. Decades of research have shown that dopamine neurons broadcast reward prediction error (RPE) signals—the discrepancy between actual and predicted reward—to drive learning to... - [ descriptionPublisher's Version](http://symposium.cshlp.org.ezp-prod1.hul.harvard.edu/content/early/2019/02/20/sqb.2018.83.037648.abstract) - [ picture\_as\_pdfPDF](/sites/g/files/omnuum8331/files/uchidalab/files/watabeuchida_uchida_cshs2019_multiple_dopamine_systems.pdf) Show morerefresh