Jane X. Wang

3.2k total citations · 3 hit papers
19 papers, 1.4k citations indexed

About

Jane X. Wang is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Artificial Intelligence. According to data from OpenAlex, Jane X. Wang has authored 19 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Cognitive Neuroscience, 5 papers in Cellular and Molecular Neuroscience and 3 papers in Artificial Intelligence. Recurrent topics in Jane X. Wang's work include Memory and Neural Mechanisms (11 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neuropharmacology Research (5 papers). Jane X. Wang is often cited by papers focused on Memory and Neural Mechanisms (11 papers), Neural dynamics and brain function (10 papers) and Neuroscience and Neuropharmacology Research (5 papers). Jane X. Wang collaborates with scholars based in United States, United Kingdom and Germany. Jane X. Wang's co-authors include Joel L. Voss, Matthew Botvinick, Zeb Kurth‐Nelson, Demis Hassabis, Anthony J. Ryals, Charles Blundell, Lynn M. Rogers, Molly S. Hermiller, Mehmet E. Dokucu and Joel Z. Leibo and has published in prestigious journals such as Science, Journal of the American Chemical Society and Neuron.

In The Last Decade

Jane X. Wang

19 papers receiving 1.4k citations

Hit Papers

Targeted enhancement of cortical-hippocampal brain networ... 2014 2026 2018 2022 2014 2019 2018 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jane X. Wang United States 12 916 285 237 183 118 19 1.4k
Joseph T. Francis United States 21 1.4k 1.5× 176 0.6× 202 0.9× 623 3.4× 198 1.7× 79 2.0k
KongFatt Wong‐Lin United Kingdom 23 1.6k 1.8× 120 0.4× 234 1.0× 427 2.3× 89 0.8× 89 2.2k
Angelo Arleo France 21 1.0k 1.1× 158 0.6× 98 0.4× 787 4.3× 147 1.2× 81 1.8k
Jochen Ditterich United States 23 2.0k 2.2× 167 0.6× 96 0.4× 257 1.4× 45 0.4× 42 2.3k
Fred H. Hamker Germany 27 1.5k 1.6× 104 0.4× 184 0.8× 340 1.9× 94 0.8× 108 2.1k
Tahamina Begum Malaysia 22 715 0.8× 398 1.4× 83 0.4× 341 1.9× 24 0.2× 60 1.3k
Brian Murphy United States 22 1.2k 1.3× 112 0.4× 459 1.9× 612 3.3× 205 1.7× 77 2.1k
Huifang Wang China 21 867 0.9× 92 0.3× 69 0.3× 155 0.8× 49 0.4× 78 1.6k
Daniele Caligiore Italy 17 790 0.9× 247 0.9× 112 0.5× 212 1.2× 17 0.1× 52 1.5k
Pamela K. Douglas United States 12 571 0.6× 71 0.2× 128 0.5× 77 0.4× 48 0.4× 15 1.2k

Countries citing papers authored by Jane X. Wang

Since Specialization
Citations

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

Fields of papers citing papers by Jane X. Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jane X. Wang. 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 Jane X. Wang. The network helps show where Jane X. Wang may publish in the future.

Co-authorship network of co-authors of Jane X. Wang

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

All Works

19 of 19 papers shown
1.
Binz, Marcel, et al.. (2024). Meta-learning: Data, architecture, and both. Behavioral and Brain Sciences. 47. e170–e170. 2 indexed citations
2.
Duéñez‐Guzmán, Edgar A., Suzanne Sadedin, Jane X. Wang, Kevin R. McKee, & Joel Z. Leibo. (2023). A social path to human-like artificial intelligence. Nature Machine Intelligence. 5(11). 1181–1188. 15 indexed citations
3.
Binz, Marcel, et al.. (2023). Meta-learned models of cognition. Behavioral and Brain Sciences. 47. e147–e147. 17 indexed citations
4.
Wang, Jane X., Michael C. King, Zeb Kurth‐Nelson, et al.. (2021). Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. arXiv (Cornell University). 4 indexed citations
5.
Botvinick, Matthew, et al.. (2019). Reinforcement Learning, Fast and Slow. Trends in Cognitive Sciences. 23(5). 408–422. 369 indexed citations breakdown →
6.
Schwarb, Hillary, Curtis L. Johnson, Michael R. Dulas, et al.. (2019). Structural and Functional MRI Evidence for Distinct Medial Temporal and Prefrontal Roles in Context-dependent Relational Memory. Journal of Cognitive Neuroscience. 31(12). 1857–1872. 24 indexed citations
7.
Wang, Jane X., Zeb Kurth‐Nelson, Dharshan Kumaran, et al.. (2018). Prefrontal cortex as a meta-reinforcement learning system. Nature Neuroscience. 21(6). 860–868. 317 indexed citations breakdown →
8.
Schwarb, Hillary, Patrick D. Watson, Jim M. Monti, et al.. (2015). Competition and Cooperation among Relational Memory Representations. PLoS ONE. 10(11). e0143832–e0143832. 5 indexed citations
9.
10.
Ryals, Anthony J., Alexandra C. Apple, Jane X. Wang, et al.. (2015). Hippocampal memory impairment in breast cancer survivors after chemotherapy measurement using covert testing.. Journal of Clinical Oncology. 33(15_suppl). 1024–1024. 4 indexed citations
11.
Ryals, Anthony J., Jane X. Wang, Kelly L. Polnaszek, & Joel L. Voss. (2015). Hippocampal contribution to implicit configuration memory expressed via eye movements during scene exploration. Hippocampus. 25(9). 1028–1041. 40 indexed citations
12.
Wang, Jane X., Neal J. Cohen, & Joel L. Voss. (2014). Covert rapid action-memory simulation (CRAMS): A hypothesis of hippocampal–prefrontal interactions for adaptive behavior. Neurobiology of Learning and Memory. 117. 22–33. 62 indexed citations
13.
Wang, Jane X. & Joel L. Voss. (2014). Brain Networks for Exploration Decisions Utilizing Distinct Modeled Information Types during Contextual Learning. Neuron. 82(5). 1171–1182. 19 indexed citations
14.
Wang, Jane X., Lynn M. Rogers, Anthony J. Ryals, et al.. (2014). Targeted enhancement of cortical-hippocampal brain networks and associative memory. Science. 345(6200). 1054–1057. 424 indexed citations breakdown →
15.
Wang, Jane X., James Bartolotti, Luı́s A. Nunes Amaral, & James R. Booth. (2013). Changes in Task-Related Functional Connectivity across Multiple Spatial Scales Are Related to Reading Performance. PLoS ONE. 8(3). e59204–e59204. 14 indexed citations
16.
Wang, Jane X. & Michał Żochowski. (2011). Interactions of Excitatory and Inhibitory Feedback Topologies in Facilitating Pattern Separation and Retrieval. Neural Computation. 24(1). 32–59. 2 indexed citations
17.
Wang, Jane X., et al.. (2010). Memory formation: from network structure to neural dynamics. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 368(1918). 2251–2267. 4 indexed citations
18.
Wang, Jane X., Gina R. Poe, & Michał Żochowski. (2008). From network heterogeneities to familiarity detection and hippocampal memory management. Physical Review E. 78(4). 41905–41905. 4 indexed citations
19.
Wang, Jane X., et al.. (2006). Optically Transparent Au{111} Substrates:  Flat Gold Nanoparticle Platforms for High-Resolution Scanning Tunneling Microscopy. Journal of the American Chemical Society. 128(18). 6052–6053. 45 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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