Takanori Uka

2.3k total citations
54 papers, 1.7k citations indexed

About

Takanori Uka is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Takanori Uka has authored 54 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Cognitive Neuroscience, 18 papers in Cellular and Molecular Neuroscience and 4 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Takanori Uka's work include Neural dynamics and brain function (35 papers), Visual perception and processing mechanisms (29 papers) and Neurobiology and Insect Physiology Research (14 papers). Takanori Uka is often cited by papers focused on Neural dynamics and brain function (35 papers), Visual perception and processing mechanisms (29 papers) and Neurobiology and Insect Physiology Research (14 papers). Takanori Uka collaborates with scholars based in Japan, United States and France. Takanori Uka's co-authors include Gregory C. DeAngelis, Shigeru Kitazawa, Ichiro Fujita, Hiroki Tanaka, Tatsuya Kimura, Masayuki Watanabe, Tadayoshi Kohno, Kenji Yoshiyama, Makoto Katô and Kiyoto Kasai and has published in prestigious journals such as Nature Communications, Neuron and Journal of Neuroscience.

In The Last Decade

Takanori Uka

50 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takanori Uka Japan 20 1.6k 386 158 150 145 54 1.7k
Ian E. Holliday United Kingdom 25 1.9k 1.2× 299 0.8× 174 1.1× 213 1.4× 81 0.6× 49 2.3k
Saumil S. Patel United States 20 1.2k 0.8× 663 1.7× 84 0.5× 266 1.8× 274 1.9× 71 1.8k
Melanie Wilke Germany 22 2.3k 1.5× 348 0.9× 176 1.1× 68 0.5× 119 0.8× 49 2.6k
Ikuya Murakami Japan 25 1.7k 1.1× 225 0.6× 186 1.2× 128 0.9× 200 1.4× 86 1.8k
Simo Vanni Finland 27 2.1k 1.3× 197 0.5× 101 0.6× 127 0.8× 135 0.9× 64 2.4k
Benoit R. Cottereau France 20 1.4k 0.9× 203 0.5× 92 0.6× 99 0.7× 100 0.7× 57 1.6k
Aniruddha Das United States 17 1.9k 1.2× 703 1.8× 82 0.5× 61 0.4× 239 1.6× 28 2.2k
John B. Reppas United States 8 1.8k 1.1× 422 1.1× 110 0.7× 67 0.4× 327 2.3× 9 1.9k
Xoana G. Troncoso United States 15 1.3k 0.8× 214 0.6× 125 0.8× 111 0.7× 211 1.5× 28 1.6k
Sang‐Hun Lee South Korea 15 1.1k 0.7× 194 0.5× 104 0.7× 92 0.6× 101 0.7× 46 1.2k

Countries citing papers authored by Takanori Uka

Since Specialization
Citations

This map shows the geographic impact of Takanori Uka'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 Takanori Uka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takanori Uka more than expected).

Fields of papers citing papers by Takanori Uka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Takanori Uka. 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 Takanori Uka. The network helps show where Takanori Uka may publish in the future.

Co-authorship network of co-authors of Takanori Uka

This figure shows the co-authorship network connecting the top 25 collaborators of Takanori Uka. A scholar is included among the top collaborators of Takanori Uka 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 Takanori Uka. Takanori Uka 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.
Kunii, Naoto, Mariko Tada, Kenji Kirihara, et al.. (2024). Auditory prediction and prediction error responses evoked through a novel cascade roving paradigm: a human ECoG study. Cerebral Cortex. 34(2). 2 indexed citations
2.
Kunii, Naoto, Mariko Tada, Kenji Kirihara, et al.. (2024). Auditory prediction errors in sound frequency and duration generated different cortical activation patterns in the human brain: an ECoG study. Cerebral Cortex. 34(3). 1 indexed citations
3.
Ebina, Teppei, Shin-Ichiro Terada, Takanori Uka, et al.. (2023). Change detection in the primate auditory cortex through feedback of prediction error signals. Nature Communications. 14(1). 6981–6981. 7 indexed citations
4.
Tada, Mariko, Takeshi Matsuo, Keisuke Kawasaki, et al.. (2022). Prediction-Related Frontal-Temporal Network for Omission Mismatch Activity in the Macaque Monkey. Frontiers in Psychiatry. 13. 557954–557954. 12 indexed citations
5.
Uka, Takanori, et al.. (2022). The NMDA receptor antagonist ketamine impairs and delays context-dependent decision making in the parietal cortex. Communications Biology. 5(1). 690–690. 4 indexed citations
6.
Mitani, Akinori, et al.. (2021). Task-specific employment of sensory signals underlies rapid task switching. Cerebral Cortex. 32(21). 4657–4670. 3 indexed citations
7.
Kunii, Naoto, Misako Komatsu, Mariko Tada, et al.. (2020). Spatiotemporal Differentiation of MMN From N1 Adaptation: A Human ECoG Study. Frontiers in Psychiatry. 11. 586–586. 8 indexed citations
8.
Uka, Takanori, et al.. (2020). Auditory Mismatch Negativity Under Predictive Coding Framework and Its Role in Psychotic Disorders. Frontiers in Psychiatry. 11. 557932–557932. 25 indexed citations
9.
Tada, Mariko, Kenji Kirihara, Zhilei Zhao, et al.. (2019). Auditory Steady State Response; nature and utility as a translational science tool. Scientific Reports. 9(1). 8454–8454. 36 indexed citations
10.
Tada, Mariko, Kenji Kirihara, Takanori Uka, et al.. (2019). Mismatch negativity (MMN) as a tool for translational investigations into early psychosis: A review. International Journal of Psychophysiology. 145. 5–14. 57 indexed citations
11.
Kunii, Naoto, Mariko Tada, Kenji Kirihara, et al.. (2018). Deviance detection is the dominant component of auditory contextual processing in the lateral superior temporal gyrus: A human ECoG study. Human Brain Mapping. 40(4). 1184–1194. 27 indexed citations
12.
13.
Uka, Takanori, et al.. (2016). Context-Dependent Accumulation of Sensory Evidence in the Parietal Cortex Underlies Flexible Task Switching. Journal of Neuroscience. 36(48). 12192–12202. 19 indexed citations
14.
Shimoji, Keigo, Takanori Uka, Yoshifumi Tamura, et al.. (2014). Diffusional kurtosis imaging analysis in patients with hypertension. Japanese Journal of Radiology. 32(2). 98–104. 9 indexed citations
15.
Shimoji, Keigo, Osamu Abe, Takanori Uka, et al.. (2012). White Matter Alteration in Metabolic Syndrome. Diabetes Care. 36(3). 696–700. 32 indexed citations
16.
17.
Uka, Takanori & Gregory C. DeAngelis. (2006). Linking Neural Representation to Function in Stereoscopic Depth Perception: Roles of the Middle Temporal Area in Coarse versus Fine Disparity Discrimination. Journal of Neuroscience. 26(25). 6791–6802. 100 indexed citations
18.
Uka, Takanori, Seiji Tanabe, Masayuki Watanabe, & Ichiro Fujita. (2005). Neural Correlates of Fine Depth Discrimination in Monkey Inferior Temporal Cortex. Journal of Neuroscience. 25(46). 10796–10802. 75 indexed citations
19.
Uka, Takanori & Gregory C. DeAngelis. (2004). Contribution of Area MT to Stereoscopic Depth Perception. Neuron. 42(2). 297–310. 161 indexed citations
20.
Yoshiyama, Kenji, Takanori Uka, Hiroki Tanaka, & Ichiro Fujita. (2003). Architecture of binocular disparity processing in monkey inferior temporal cortex. Neuroscience Research. 48(2). 155–167. 5 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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