Daoyun Ji

3.7k total citations · 1 hit paper
30 papers, 2.6k citations indexed

About

Daoyun Ji is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Daoyun Ji has authored 30 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Cellular and Molecular Neuroscience, 25 papers in Cognitive Neuroscience and 6 papers in Molecular Biology. Recurrent topics in Daoyun Ji's work include Neuroscience and Neuropharmacology Research (24 papers), Memory and Neural Mechanisms (20 papers) and Neural dynamics and brain function (9 papers). Daoyun Ji is often cited by papers focused on Neuroscience and Neuropharmacology Research (24 papers), Memory and Neural Mechanisms (20 papers) and Neural dynamics and brain function (9 papers). Daoyun Ji collaborates with scholars based in United States, China and Hong Kong. Daoyun Ji's co-authors include Matthew Wilson, John A. Dani, Remigijus Lapė, Fu-Ming Zhou, Daniel Christopher Haggerty, Caleb Kemere, Xiang Mou, Matthew A. Wilson, Huda Y. Zoghbi and Mariella De Biasi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and Journal of Neuroscience.

In The Last Decade

Daoyun Ji

28 papers receiving 2.5k citations

Hit Papers

Coordinated memory replay in the visual cortex and hippoc... 2006 2026 2012 2019 2006 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daoyun Ji United States 16 1.7k 1.6k 842 178 144 30 2.6k
Eleanor H. Simpson United States 31 1.1k 0.7× 1.6k 1.0× 1.1k 1.3× 164 0.9× 159 1.1× 59 3.1k
Marshall G. Hussain Shuler United States 19 2.0k 1.2× 1.8k 1.1× 585 0.7× 143 0.8× 107 0.7× 34 3.1k
Timothy Spellman United States 16 1.5k 0.9× 1.3k 0.8× 388 0.5× 114 0.6× 139 1.0× 21 2.5k
Elizabeth P. Bauer United States 21 2.1k 1.2× 2.1k 1.3× 669 0.8× 87 0.5× 98 0.7× 29 3.1k
Donna J. Calu United States 23 1.5k 0.9× 1.8k 1.1× 673 0.8× 164 0.9× 101 0.7× 39 2.6k
Rutsuko Ito Canada 21 1.5k 0.9× 1.7k 1.1× 688 0.8× 157 0.9× 99 0.7× 49 2.6k
Stéphane Ciocchi Switzerland 13 2.4k 1.4× 2.2k 1.4× 538 0.6× 141 0.8× 114 0.8× 21 3.5k
Talia N. Lerner United States 18 1.1k 0.7× 1.5k 0.9× 618 0.7× 91 0.5× 130 0.9× 28 2.4k
Derek L. Buhl United States 20 2.4k 1.4× 2.9k 1.8× 758 0.9× 135 0.8× 131 0.9× 42 4.0k
Thomas Seidenbecher Germany 30 2.3k 1.4× 2.2k 1.4× 645 0.8× 148 0.8× 83 0.6× 55 3.5k

Countries citing papers authored by Daoyun Ji

Since Specialization
Citations

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

Fields of papers citing papers by Daoyun Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daoyun Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Daoyun Ji. A scholar is included among the top collaborators of Daoyun Ji 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 Daoyun Ji. Daoyun Ji 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.
Ji, Daoyun, et al.. (2023). Memory rescue and learning in synaptic impaired neuronal circuits. iScience. 26(7). 106931–106931. 4 indexed citations
2.
Zobaer, M. S., et al.. (2022). Rapid Spectral Dynamics in Hippocampal Oscillons. Frontiers in Computational Neuroscience. 16. 880742–880742.
3.
Mou, Xiang, et al.. (2022). Tetrode recording of rat CA1 place cells in an observational spatial working memory task. STAR Protocols. 3(3). 101501–101501. 1 indexed citations
4.
Mou, Xiang, et al.. (2021). Observational learning promotes hippocampal remote awake replay toward future reward locations. Neuron. 110(5). 891–902.e7. 10 indexed citations
5.
Haggerty, Daniel Christopher, et al.. (2021). LSD degrades hippocampal spatial representations and suppresses hippocampal-visual cortical interactions. Cell Reports. 36(11). 109714–109714. 7 indexed citations
6.
Sanders, Honi, Daoyun Ji, Takuya Sasaki, et al.. (2018). Temporal coding and rate remapping: Representation of nonspatial information in the hippocampus. Hippocampus. 29(2). 111–127. 25 indexed citations
7.
Mou, Xiang, et al.. (2018). Comparing Mouse and Rat Hippocampal Place Cell Activities and Firing Sequences in the Same Environments. Frontiers in Cellular Neuroscience. 12. 332–332. 15 indexed citations
8.
Haggerty, Daniel Christopher, et al.. (2017). Hippocampal awake replay in fear memory retrieval. Nature Neuroscience. 20(4). 571–580. 138 indexed citations
9.
Lü, Hui, Ryan T. Ash, Ling-jie He, et al.. (2016). Loss and Gain of MeCP2 Cause Similar Hippocampal Circuit Dysfunction that Is Rescued by Deep Brain Stimulation in a Rett Syndrome Mouse Model. Neuron. 91(4). 739–747. 79 indexed citations
10.
Ji, Daoyun, et al.. (2016). A Topological Model of the Hippocampal Cell Assembly Network. Frontiers in Computational Neuroscience. 10. 50–50. 14 indexed citations
11.
Zhao, Rong, Michael J. Yetman, Alexander Lam, et al.. (2016). Impaired Recall of Positional Memory following Chemogenetic Disruption of Place Field Stability. Cell Reports. 16(3). 793–804. 21 indexed citations
12.
Ali, Yousuf, et al.. (2015). Progressive Functional Impairments of Hippocampal Neurons in a Tauopathy Mouse Model. Journal of Neuroscience. 35(21). 8118–8131. 34 indexed citations
13.
Haggerty, Daniel Christopher & Daoyun Ji. (2015). Activities of visual cortical and hippocampal neurons co-fluctuate in freely moving rats during spatial behavior. eLife. 4. 53 indexed citations
14.
Ji, Daoyun, et al.. (2013). Rigid firing sequences undermine spatial memory codes in a neurodegenerative mouse model. eLife. 2. e00647–e00647. 62 indexed citations
15.
Ji, Daoyun & Matthew A. Wilson. (2008). Firing Rate Dynamics in the Hippocampus Induced by Trajectory Learning. Journal of Neuroscience. 28(18). 4679–4689. 27 indexed citations
16.
Ji, Daoyun & Matthew Wilson. (2006). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nature Neuroscience. 10(1). 100–107. 1128 indexed citations breakdown →
17.
Broide, Ron S., Ramiro Salas, Daoyun Ji, et al.. (2002). Increased Sensitivity to Nicotine-Induced Seizures in Mice Expressing the L250T α7 Nicotinic Acetylcholine Receptor Mutation. Molecular Pharmacology. 61(3). 695–705. 65 indexed citations
18.
Broide, Ron S., Ramiro Salas, Daoyun Ji, et al.. (2002). Increased Sensitivity to Nicotine-Induced Seizures in Mice Expressing the L250T α7 Nicotinic Acetylcholine Receptor Mutation. Molecular Pharmacology. 61(3). 695–705. 3 indexed citations
19.
Ji, Daoyun, Remigijus Lapė, & John A. Dani. (2001). Timing and Location of Nicotinic Activity Enhances or Depresses Hippocampal Synaptic Plasticity. Neuron. 31(1). 131–141. 366 indexed citations
20.
Dani, John A., Daoyun Ji, & Fu-Ming Zhou. (2001). Synaptic Plasticity and Nicotine Addiction. Neuron. 31(3). 349–352. 224 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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