Jinwei Xing

1.1k total citations
9 papers, 181 citations indexed

About

Jinwei Xing is a scholar working on Cognitive Neuroscience, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Jinwei Xing has authored 9 papers receiving a total of 181 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Cognitive Neuroscience, 3 papers in Artificial Intelligence and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Jinwei Xing's work include Neural dynamics and brain function (6 papers), Advanced Memory and Neural Computing (3 papers) and Memory and Neural Mechanisms (2 papers). Jinwei Xing is often cited by papers focused on Neural dynamics and brain function (6 papers), Advanced Memory and Neural Computing (3 papers) and Memory and Neural Mechanisms (2 papers). Jinwei Xing collaborates with scholars based in United States and China. Jinwei Xing's co-authors include Jeffrey L. Krichmar, Hirak J. Kashyap, Nikil Dutt, Xinyun Zou, Michael Beyeler, Takashi Nagata, Emre Neftci, Huajin Tang, Hongying Li and Anup Das and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neural Networks and Cognitive Computation.

In The Last Decade

Jinwei Xing

9 papers receiving 177 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jinwei Xing United States	8	111	90	58	40	17	9	181
Adarsh Kumar Kosta United States	8	167 1.5×	69 0.8×	79 1.4×	21 0.5×	24 1.4×	18	222
Gregor Lenz France	5	274 2.5×	134 1.5×	126 2.2×	73 1.8×	18 1.1×	12	361
Stanisław Woźniak Switzerland	9	265 2.4×	102 1.1×	120 2.1×	95 2.4×	9 0.5×	20	334
Guangzhi Tang Netherlands	7	146 1.3×	81 0.9×	53 0.9×	44 1.1×	11 0.6×	18	173
Amar Shrestha United States	10	233 2.1×	120 1.3×	97 1.7×	67 1.7×	13 0.8×	16	271
Alan G. Millard United Kingdom	9	110 1.0×	71 0.8×	36 0.6×	84 2.1×	26 1.5×	18	211
Jianbiao Xiao China	7	161 1.5×	120 1.3×	62 1.1×	48 1.2×	11 0.6×	16	317
Xiao Ming-qing China	8	104 0.9×	101 1.1×	59 1.0×	16 0.4×	33 1.9×	25	209
Aditya Shukla India	6	183 1.6×	62 0.7×	130 2.2×	73 1.8×	18 1.1×	18	273
Jan Stuijt Netherlands	10	246 2.2×	65 0.7×	57 1.0×	81 2.0×	9 0.5×	21	293

Countries citing papers authored by Jinwei Xing

Since Specialization

Citations

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

Fields of papers citing papers by Jinwei Xing

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinwei Xing

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

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Xing, Jinwei, Takashi Nagata, Xinyun Zou, Emre Neftci, & Jeffrey L. Krichmar. (2023). Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization. Neural Networks. 161. 228–241. 9 indexed citations

2.

Attili, Sarojini M., et al.. (2022). Robust Resting-State Dynamics in a Large-Scale Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. Cognitive Computation. 15(4). 1190–1210. 13 indexed citations

3.

Xing, Jinwei, et al.. (2022). CARLsim 6: An Open Source Library for Large-Scale, Biologically Detailed Spiking Neural Network Simulation. 2022 International Joint Conference on Neural Networks (IJCNN). 1–10. 29 indexed citations

4.

Xing, Jinwei, Elizabeth R. Chrastil, Douglas A. Nitz, & Jeffrey L. Krichmar. (2022). Linking global top-down views to first-person views in the brain. Proceedings of the National Academy of Sciences. 119(45). e2202024119–e2202024119. 2 indexed citations

5.

Xing, Jinwei, et al.. (2021). Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. Proceedings of the AAAI Conference on Artificial Intelligence. 35(12). 10452–10459. 23 indexed citations

6.

Kashyap, Hirak J., et al.. (2020). Neurorobots as a Means Toward Neuroethology and Explainable AI. Frontiers in Neurorobotics. 14. 570308–570308. 16 indexed citations

7.

Xing, Jinwei, Xinyun Zou, & Jeffrey L. Krichmar. (2020). Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation. 1–8. 7 indexed citations

8.

Kashyap, Hirak J., et al.. (2018). CARLsim 4: An Open Source Library for Large Scale, Biologically Detailed Spiking Neural Network Simulation using Heterogeneous Clusters. 1–8. 61 indexed citations

9.

Tang, Huajin, et al.. (2017). Spike trains encoding and threshold rescaling method for deep spiking neural networks. 1–6. 21 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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