Naoshige Uchida

21.2k total citations · 10 hit papers
118 papers, 14.3k citations indexed

About

Naoshige Uchida is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Naoshige Uchida has authored 118 papers receiving a total of 14.3k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Cellular and Molecular Neuroscience, 49 papers in Cognitive Neuroscience and 39 papers in Molecular Biology. Recurrent topics in Naoshige Uchida's work include Neural dynamics and brain function (42 papers), Neurotransmitter Receptor Influence on Behavior (21 papers) and Olfactory and Sensory Function Studies (18 papers). Naoshige Uchida is often cited by papers focused on Neural dynamics and brain function (42 papers), Neurotransmitter Receptor Influence on Behavior (21 papers) and Olfactory and Sensory Function Studies (18 papers). Naoshige Uchida collaborates with scholars based in United States, Japan and Portugal. Naoshige Uchida's co-authors include Zachary F. Mainen, Mitsuko Watabe‐Uchida, Ádám Kepecs, Jeremiah Y. Cohen, Sachie K. Ogawa, Linh Vong, Bradford B. Lowell, Sebastian Haesler, Ju Tian and Lisa Zhu and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Naoshige Uchida

115 papers receiving 14.1k citations

Hit Papers

Neuron-type-specific signals for reward and punishme... 2003 2026 2010 2018 2012 2012 2008 2003 2014 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Neuron-type-specific signals for reward and punishment in the ventral tegmental area
    2012 946 citations Naoshige Uchida et al. Nature profile →
  2. Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons
    2012 894 citations Mitsuko Watabe‐Uchida, Naoshige Uchida et al. Neuron profile →
  3. Neural correlates, computation and behavioural impact of decision confidence
    2008 576 citations Ádám Kepecs, Naoshige Uchida et al. Nature profile →
  4. Speed and accuracy of olfactory discrimination in the rat
    2003 517 citations Naoshige Uchida, Zachary F. Mainen Nature Neuroscience profile →
  5. An excitatory paraventricular nucleus to AgRP neuron circuit that drives hunger
    2014 473 citations Mitsuko Watabe‐Uchida, Naoshige Uchida et al. Nature profile →
  6. Demixed principal component analysis of neural population data
    2016 311 citations Dmitry Kobak, Wieland Brendel et al. eLife profile →
  7. Functional circuit architecture underlying parental behaviour
    2018 275 citations Bénédicte M. Babayan, Naoshige Uchida et al. Nature profile →
  8. A distributional code for value in dopamine-based reinforcement learning
    2020 236 citations Naoshige Uchida et al. Nature profile →
  9. Spontaneous behaviour is structured by reinforcement without explicit reward
    2023 90 citations Jeffrey E. Markowitz, Sandra Romero Pinto et al. Nature profile →
  10. A hypothalamic circuit underlying the dynamic control of social homeostasis
    2025 14 citations Ding Liu, Mohammed Mostafizur Rahman et al. Nature profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Naoshige Uchida United States 62 6.6k 5.4k 4.2k 2.1k 1.1k 118 14.3k
Shigeyoshi Itohara Japan 84 7.2k 1.1× 2.5k 0.5× 10.2k 2.4× 906 0.4× 903 0.8× 277 25.4k
Eric J. Huang United States 67 6.2k 0.9× 1.5k 0.3× 8.5k 2.0× 724 0.3× 488 0.4× 173 20.9k
Ann M. Graybiel United States 112 25.8k 3.9× 16.5k 3.0× 11.7k 2.8× 1.4k 0.6× 401 0.4× 352 43.7k
Hongkui Zeng United States 58 6.9k 1.1× 4.5k 0.8× 6.6k 1.6× 839 0.4× 92 0.1× 128 16.6k
Leszek Kaczmarek Poland 65 5.8k 0.9× 2.6k 0.5× 7.1k 1.7× 347 0.2× 410 0.4× 295 15.3k
Arturo Álvarez-Buylla United States 109 17.7k 2.7× 2.3k 0.4× 24.1k 5.7× 2.1k 1.0× 205 0.2× 223 52.1k
Derek van der Kooy Canada 86 11.9k 1.8× 3.0k 0.5× 12.5k 3.0× 872 0.4× 86 0.1× 316 25.7k
Rainer W. Friedrich Germany 43 3.5k 0.5× 1.6k 0.3× 1.4k 0.3× 2.5k 1.2× 157 0.1× 94 6.4k
José Manuel García‐Verdugo Spain 87 12.8k 2.0× 1.4k 0.3× 18.0k 4.3× 873 0.4× 199 0.2× 310 40.8k
David D. Ginty United States 79 12.9k 2.0× 2.3k 0.4× 12.6k 3.0× 1.1k 0.5× 95 0.1× 145 24.5k

Countries citing papers authored by Naoshige Uchida

Since Specialization
Citations

This map shows the geographic impact of Naoshige Uchida's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Naoshige Uchida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Naoshige Uchida more than expected).

Fields of papers citing papers by Naoshige Uchida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Naoshige Uchida. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Naoshige Uchida. The network helps show where Naoshige Uchida may publish in the future.

Co-authorship network of co-authors of Naoshige Uchida

This figure shows the co-authorship network connecting the top 25 collaborators of Naoshige Uchida. A scholar is included among the top collaborators of Naoshige Uchida based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Naoshige Uchida. Naoshige Uchida is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pinto, Sandra Romero & Naoshige Uchida. (2025). Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model. Nature Communications. 16(1). 7529–7529. 1 indexed citations
2.
Hennig, Jay A., et al.. (2025). Prospective contingency explains behavior and dopamine signals during associative learning. Nature Neuroscience. 28(6). 1280–1292. 2 indexed citations
3.
Tsutsui‐Kimura, Iku, Ryunosuke Amo, Yulong Li, et al.. (2025). Dopamine in the tail of the striatum facilitates avoidance in threat–reward conflicts. Nature Neuroscience. 28(4). 795–810. 8 indexed citations
4.
Liu, Ding, Mohammed Mostafizur Rahman, Ryunosuke Amo, et al.. (2025). A hypothalamic circuit underlying the dynamic control of social homeostasis. Nature. 640(8060). 1000–1010. 14 indexed citations breakdown →
5.
Qiao, Zheng, et al.. (2025). An opponent striatal circuit for distributional reinforcement learning. Nature. 639(8055). 717–726. 3 indexed citations
6.
Amo, Ryunosuke, Naoshige Uchida, & Mitsuko Watabe‐Uchida. (2024). Glutamate inputs send prediction error of reward, but not negative value of aversive stimuli, to dopamine neurons. Neuron. 112(6). 1001–1019.e6. 9 indexed citations
7.
Cai, Xintong, Changliang Liu, Iku Tsutsui‐Kimura, et al.. (2024). Dopamine dynamics are dispensable for movement but promote reward responses. Nature. 635(8038). 406–414. 19 indexed citations
8.
Amo, Ryunosuke, Sara Matias, Akihiro Yamanaka, et al.. (2022). A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning. Nature Neuroscience. 25(8). 1082–1092. 44 indexed citations
9.
Tsutsui‐Kimura, Iku, Yu Xie, Alexander Mathis, et al.. (2022). Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction. Neuron. 110(22). 3789–3804.e9. 50 indexed citations
10.
Mikhael, John G., HyungGoo R. Kim, Naoshige Uchida, & Samuel J. Gershman. (2022). The role of state uncertainty in the dynamics of dopamine. Current Biology. 32(5). 1077–1087.e9. 37 indexed citations
11.
12.
Menegas, William, et al.. (2018). Dopamine neurons projecting to the posterior striatum reinforce avoidance of threatening stimuli. Nature Neuroscience. 21(10). 1421–1430. 228 indexed citations
13.
Menegas, William, Bénédicte M. Babayan, Naoshige Uchida, & Mitsuko Watabe‐Uchida. (2017). Opposite initialization to novel cues in dopamine signaling in ventral and posterior striatum in mice. eLife. 6. 165 indexed citations
14.
Matsumoto, Hideyuki, Ju Tian, Naoshige Uchida, & Mitsuko Watabe‐Uchida. (2016). Midbrain dopamine neurons signal aversion in a reward-context-dependent manner. eLife. 5. 78 indexed citations
15.
Kobak, Dmitry, Wieland Brendel, Christos Constantinidis, et al.. (2016). Demixed principal component analysis of neural population data. eLife. 5. 311 indexed citations breakdown →
16.
Uchida, Naoshige. (2008). Odor concentration invariance by chemical ratio coding. Frontiers in Systems Neuroscience. 1. 3–3. 44 indexed citations
17.
Kelly, Stephen, Tonya Bliss, Guohua Sun, et al.. (2004). Transplanted human fetal neural stem cells survive, migrate, and differentiate in ischemic rat cerebral cortex. Proceedings of the National Academy of Sciences. 101(32). 11839–11844. 488 indexed citations
18.
Uchida, Naoshige, et al.. (1998). Synergy of the combination of nedaplatin with etoposide in murine and human lung carcinoma.. PubMed. 18(1A). 247–52. 21 indexed citations
19.
Weissman, Irving L., Sean J. Morrison, Samuel Cheshier, & Naoshige Uchida. (1997). Hematopoietc stem cells: Biology and transplantation. Experimental Hematology. 25(8). 1 indexed citations
20.
Sasaki, Dennis T., et al.. (1995). Development of a Clinically Applicable High-Speed Flow Cytometer for the Isolation of Transplantable Human Hematopoietic Stem Cells. Journal of Hematotherapy. 4(6). 503–514. 25 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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