Yu Shikano

534 total citations
15 papers, 340 citations indexed

About

Yu Shikano is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Molecular Biology. According to data from OpenAlex, Yu Shikano has authored 15 papers receiving a total of 340 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cognitive Neuroscience, 7 papers in Cellular and Molecular Neuroscience and 3 papers in Molecular Biology. Recurrent topics in Yu Shikano's work include Neural dynamics and brain function (7 papers), Memory and Neural Mechanisms (5 papers) and Neuroscience and Neuropharmacology Research (4 papers). Yu Shikano is often cited by papers focused on Neural dynamics and brain function (7 papers), Memory and Neural Mechanisms (5 papers) and Neuroscience and Neuropharmacology Research (4 papers). Yu Shikano collaborates with scholars based in Japan and United States. Yu Shikano's co-authors include Yuji Ikegaya, Takuya Sasaki, Kenichi Makino, Mengxuan Gao, Hiroyuki Hioki, Shigeyoshi Fujisawa, Hiroaki Norimoto, Kazuki Okamoto, Tomoe Ishikawa and Yuya Nishimura and has published in prestigious journals such as Science, Nature Communications and The Journal of Physiology.

In The Last Decade

Yu Shikano

15 papers receiving 338 citations

Peers

Yu Shikano
Ipshita Zutshi United States
Dongye Lu United States
Paul Hoerbelt United States
Jung Ho Hyun South Korea
Ipshita Zutshi United States
Yu Shikano
Citations per year, relative to Yu Shikano Yu Shikano (= 1×) peers Ipshita Zutshi

Countries citing papers authored by Yu Shikano

Since Specialization
Citations

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

Fields of papers citing papers by Yu Shikano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Shikano

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Shikano. A scholar is included among the top collaborators of Yu Shikano 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 Yu Shikano. Yu Shikano is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Ihara, Keiko, et al.. (2024). A reinforcement learning model with choice traces for a progressive ratio schedule. Frontiers in Behavioral Neuroscience. 17. 1302842–1302842. 2 indexed citations
2.
Shikano, Yu, Jun Ogasawara, Kenichi Makino, et al.. (2023). Mesolimbic dopamine release precedes actively sought aversive stimuli in mice. Nature Communications. 14(1). 2433–2433. 15 indexed citations
3.
Shikano, Yu, Sho Yagishita, Kenji F. Tanaka, & Norio Takata. (2023). Slow‐rising and fast‐falling dopaminergic dynamics jointly adjust negative prediction error in the ventral striatum. European Journal of Neuroscience. 58(12). 4502–4522. 4 indexed citations
4.
Shikano, Yu, et al.. (2023). Inhibition of the dorsomedial striatal direct pathway is essential for the execution of action sequences. Neuropsychopharmacology Reports. 43(3). 414–424. 1 indexed citations
5.
Shikano, Yu, Yuji Ikegaya, & Takuya Sasaki. (2021). Minute-encoding neurons in hippocampal-striatal circuits. Current Biology. 31(7). 1438–1449.e6. 15 indexed citations
6.
Nishimura, Yuya, et al.. (2020). Urethane anesthesia suppresses hippocampal subthreshold activity and neuronal synchronization. Brain Research. 1749. 147137–147137. 18 indexed citations
7.
Shikano, Yu, et al.. (2020). Acute Effects of Ethanol on Hippocampal Spatial Representation and Offline Reactivation. Frontiers in Cellular Neuroscience. 14. 571175–571175. 7 indexed citations
8.
Kuga, Nahoko, et al.. (2019). Sniffing behaviour‐related changes in cardiac and cortical activity in rats. The Journal of Physiology. 597(21). 5295–5306. 11 indexed citations
9.
Shikano, Yu, Takuya Sasaki, & Yuji Ikegaya. (2018). Simultaneous Recordings of Cortical Local Field Potentials, Electrocardiogram, Electromyogram, and Breathing Rhythm from a Freely Moving Rat. Journal of Visualized Experiments. 15 indexed citations
10.
Shikano, Yu, et al.. (2018). Monitoring brain neuronal activity with manipulation of cardiac events in a freely moving rat. Neuroscience Research. 136. 56–62. 3 indexed citations
11.
Norimoto, Hiroaki, Kenichi Makino, Mengxuan Gao, et al.. (2018). Hippocampal ripples down-regulate synapses. Science. 359(6383). 1524–1527. 163 indexed citations
13.
Shikano, Yu, et al.. (2018). Time‐varying synchronous cell ensembles during consummatory periods correlate with variable numbers of place cell spikes. Hippocampus. 28(7). 471–483. 12 indexed citations
14.
Shikano, Yu, et al.. (2018). Vagus nerve spiking activity associated with locomotion and cortical arousal states in a freely moving rat. European Journal of Neuroscience. 49(10). 1298–1312. 10 indexed citations
15.
Kanazawa, Takehiko, Atsuko Era, Naoki Minamino, et al.. (2015). SNARE Molecules inMarchantia polymorpha: Unique and Conserved Features of the Membrane Fusion Machinery. Plant and Cell Physiology. 57(2). 307–324. 56 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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