Young-Cho Kim

2.5k total citations
33 papers, 1.7k citations indexed

About

Young-Cho Kim is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Endocrine and Autonomic Systems. According to data from OpenAlex, Young-Cho Kim has authored 33 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cellular and Molecular Neuroscience, 13 papers in Cognitive Neuroscience and 6 papers in Endocrine and Autonomic Systems. Recurrent topics in Young-Cho Kim's work include Neurobiology and Insect Physiology Research (11 papers), Neural dynamics and brain function (8 papers) and Neuroscience and Music Perception (8 papers). Young-Cho Kim is often cited by papers focused on Neurobiology and Insect Physiology Research (11 papers), Neural dynamics and brain function (8 papers) and Neuroscience and Music Perception (8 papers). Young-Cho Kim collaborates with scholars based in United States, South Korea and Brazil. Young-Cho Kim's co-authors include Kyung‐An Han, Hyun-Gwan Lee, Nandakumar S. Narayanan, Chang-Soo Seong, Eric B. Emmons, K L Parker, Stephanie L. Alberico, Ronald L. Davis, Benjamin J. De Corte and Fred W. Wolf and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and Journal of Neuroscience.

In The Last Decade

Young-Cho Kim

33 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
Young-Cho Kim United States 20 1.1k 471 426 304 262 33 1.7k
H. Inagaki United States 20 1.3k 1.2× 780 1.7× 445 1.0× 406 1.3× 185 0.7× 39 2.3k
Thomas Riemensperger Germany 19 1.3k 1.2× 141 0.3× 544 1.3× 349 1.1× 321 1.2× 26 1.6k
Laurent Seugnet France 22 905 0.8× 353 0.7× 319 0.7× 114 0.4× 113 0.4× 34 1.7k
Thomas A. Cleland United States 33 1.8k 1.7× 591 1.3× 326 0.8× 148 0.5× 124 0.5× 79 3.3k
Adam Claridge‐Chang Singapore 18 1.3k 1.2× 182 0.4× 535 1.3× 393 1.3× 280 1.1× 36 1.9k
James J. L. Hodge United Kingdom 24 1.0k 0.9× 202 0.4× 311 0.7× 132 0.4× 192 0.7× 51 1.6k
Richard A. Baines United Kingdom 31 2.1k 1.9× 220 0.5× 478 1.1× 255 0.8× 284 1.1× 99 3.2k
Julie A. Williams United States 16 1.1k 1.0× 531 1.1× 326 0.8× 98 0.3× 141 0.5× 22 1.9k
Pavel M. Itskov Portugal 15 757 0.7× 532 1.1× 231 0.5× 190 0.6× 248 0.9× 21 1.7k
Florence Friggi‐Grelin France 9 1.5k 1.4× 96 0.2× 735 1.7× 512 1.7× 523 2.0× 9 1.8k

Countries citing papers authored by Young-Cho Kim

Since Specialization
Citations

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

Fields of papers citing papers by Young-Cho Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young-Cho Kim. 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 Young-Cho Kim. The network helps show where Young-Cho Kim may publish in the future.

Co-authorship network of co-authors of Young-Cho Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Young-Cho Kim. A scholar is included among the top collaborators of Young-Cho Kim 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 Young-Cho Kim. Young-Cho Kim 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.
Singh, Uday, Kenji Saito, Michael Z. Khan, et al.. (2023). Collateralizing ventral subiculum melanocortin 4 receptor circuits regulate energy balance and food motivation. Physiology & Behavior. 262. 114105–114105. 1 indexed citations
2.
Kim, Young-Cho, et al.. (2022). Phase-adaptive brain stimulation of striatal D1 medium spiny neurons in dopamine-depleted mice. Scientific Reports. 12(1). 21780–21780. 4 indexed citations
3.
Emmons, Eric B., et al.. (2021). Experience‐related enhancements in striatal temporal encoding. European Journal of Neuroscience. 54(3). 5063–5074. 8 indexed citations
4.
Emmons, Eric B., et al.. (2020). Temporal Learning Among Prefrontal and Striatal Ensembles. Cerebral Cortex Communications. 1(1). tgaa058–tgaa058. 17 indexed citations
5.
Kim, Young-Cho, et al.. (2020). Concerted Actions of Octopamine and Dopamine Receptors Drive Olfactory Learning. Journal of Neuroscience. 40(21). 4240–4250. 26 indexed citations
6.
Corte, Benjamin J. De, et al.. (2019). Cerebellar D1DR-expressing neurons modulate the frontal cortex during timing tasks. Neurobiology of Learning and Memory. 170. 107067–107067. 6 indexed citations
7.
Zhang, Qiang, et al.. (2019). Scopolamine and Medial Frontal Stimulus-Processing during Interval Timing. Neuroscience. 414. 219–227. 9 indexed citations
8.
Emmons, Eric B., et al.. (2019). Corticostriatal stimulation compensates for medial frontal inactivation during interval timing. Scientific Reports. 9(1). 14371–14371. 19 indexed citations
9.
Leibold, Nicole, Benton S. Purnell, Nicole M. Bode, et al.. (2018). Dorsal Raphe Serotonin Neurons Mediate CO2-Induced Arousal from Sleep. Journal of Neuroscience. 38(8). 1915–1925. 64 indexed citations
10.
Kim, Young-Cho, Sang Woo Han, Megan S. Keiser, et al.. (2017). RNA Interference of Human α-Synuclein in Mouse. Frontiers in Neurology. 8. 13–13. 20 indexed citations
11.
Han, Sang Woo, Young-Cho Kim, & Nandakumar S. Narayanan. (2017). Projection targets of medial frontal D1DR-expressing neurons. Neuroscience Letters. 655. 166–171. 13 indexed citations
12.
Alberico, Stephanie L., et al.. (2016). Axial levodopa-induced dyskinesias and neuronal activity in the dorsal striatum. Neuroscience. 343. 240–249. 21 indexed citations
13.
Kim, Young-Cho, Stephanie L. Alberico, Eric B. Emmons, & Nandakumar S. Narayanan. (2015). New therapeutic strategies targeting D1-type dopamine receptors for neuropsychiatric disease. Frontiers in Biology. 10(3). 230–238. 16 indexed citations
14.
Zhang, Qiang, Young-Cho Kim, & Nandakumar S. Narayanan. (2015). Disease-modifying therapeutic directions for Lewy-Body dementias. Frontiers in Neuroscience. 9. 293–293. 19 indexed citations
15.
Kim, Young-Cho, et al.. (2013). Appetitive Learning Requires the Alpha1-Like Octopamine Receptor OAMB in theDrosophilaMushroom Body Neurons. Journal of Neuroscience. 33(4). 1672–1677. 58 indexed citations
16.
Lebestky, Tim, H. Dankert, Young-Cho Kim, et al.. (2009). Two Different Forms of Arousal in Drosophila Are Oppositely Regulated by the Dopamine D1 Receptor Ortholog DopR via Distinct Neural Circuits. Neuron. 64(4). 522–536. 205 indexed citations
17.
Lee, Hyun-Gwan, et al.. (2008). Recurring Ethanol Exposure Induces Disinhibited Courtship in Drosophila. PLoS ONE. 3(1). e1391–e1391. 55 indexed citations
18.
Kim, Young-Cho, Hyun-Gwan Lee, & Kyung‐An Han. (2007). D1Dopamine Receptor dDA1 Is Required in the Mushroom Body Neurons for Aversive and Appetitive Learning inDrosophila. Journal of Neuroscience. 27(29). 7640–7647. 275 indexed citations
19.
Kim, Young-Cho, et al.. (2006). Classical reward conditioning in Drosophila melanogaster. Genes Brain & Behavior. 6(2). 201–207. 22 indexed citations
20.
Park, Dongkook, Mei Han, Young-Cho Kim, Kyung‐An Han, & Paul H. Taghert. (2004). Ap-let neurons—a peptidergic circuit potentially controlling ecdysial behavior in Drosophila. Developmental Biology. 269(1). 95–108. 39 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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