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Mainen (2) Search Within Results Search Within Results search ## 73 results Show filters filter\_alt Sort by Year of PublicationAlphabetical A-Z sort ## 73 results Download 73 citations download- [BibTeX](/node/1915001/export?format=bibtex) - [EndNote X3 XML](/node/1915001/export?format=endnote8) - [EndNote 7 XML](/node/1915001/export?format=endnote7) - [Endnote tagged](/node/1915001/export?format=tagged) - [Marc](/node/1915001/export?format=marc) - [PubMedId](/node/1915001/export?format=pubmed_id) - [RIS](/node/1915001/export?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) 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) 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) 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) 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) 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) 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) - Previous page chevron\_left - [1](?page=0 "Current page") - [2](?page=1 "Go to page 2") - [3](?page=2 "Go to page 3") - [ Last page 7 ](?page=6 "Go to last page") - [ Next page chevron\_right ](?page=1 "Go to next page")