Fang Han

1.6k total citations
46 papers, 440 citations indexed

About

Fang Han is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Fang Han has authored 46 papers receiving a total of 440 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Cognitive Neuroscience, 17 papers in Cellular and Molecular Neuroscience and 16 papers in Statistical and Nonlinear Physics. Recurrent topics in Fang Han's work include Neural dynamics and brain function (30 papers), stochastic dynamics and bifurcation (16 papers) and Nonlinear Dynamics and Pattern Formation (10 papers). Fang Han is often cited by papers focused on Neural dynamics and brain function (30 papers), stochastic dynamics and bifurcation (16 papers) and Nonlinear Dynamics and Pattern Formation (10 papers). Fang Han collaborates with scholars based in China, United Kingdom and Denmark. Fang Han's co-authors include Zhijie Wang, Ying Yu, Qingyun Wang, Marian Wiercigroch, Qingyun Wang, Qishao Lu, Jian‐an Fang, Xiaojuan Sun, Yubo Fan and Guoming Luan and has published in prestigious journals such as European Journal of Neuroscience, Neurocomputing and Neural Networks.

In The Last Decade

Fang Han

43 papers receiving 429 citations

Peers

Fang Han

SNP

CMN

CNC

EEE

Diogo C. Soriano Brazil

Peter F. Rowat United States

Wondimu Teka United States

Robert Clewley United States

Shenquan Liu China

S. Binczak France

Yakov Kazanovich Russia

Toru Aonishi Japan

Zhuoqin Yang China

Lubomir Kostal Czechia

Diogo C. Soriano Brazil

Fang Han

179 ×0.7

117 ×0.6

SNP

98 ×0.8

CMN

81 ×0.7

CNC

64 ×0.9

EEE

Citations per year, relative to Fang Han Fang Han (= 1×) peers Diogo C. Soriano

Countries citing papers authored by Fang Han

Since Specialization

Citations

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

Fields of papers citing papers by Fang Han

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fang Han

This figure shows the co-authorship network connecting the top 25 collaborators of Fang Han. A scholar is included among the top collaborators of Fang Han 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 Fang Han. Fang Han 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:

20 of 20 papers shown

Luo, Wan, Xiang Wang, Fang Han, et al.. (2025). Research on LSTM-PPO Obstacle Avoidance Algorithm and Training Environment for Unmanned Surface Vehicles. Journal of Marine Science and Engineering. 13(3). 479–479. 2 indexed citations

Han, Fang, et al.. (2024). A spinal circuit model with asymmetric cervical-lumbar layout controls backward locomotion and scratching in quadrupeds. Neural Networks. 178. 106422–106422. 1 indexed citations

Han, Fang, et al.. (2024). A large‐scale neuronal network modelling study: Stimulus size modulates gamma oscillations in the primary visual cortex by long‐range connections. European Journal of Neuroscience. 60(3). 4224–4243. 3 indexed citations

Yu, Ying, et al.. (2024). Dynamic modeling and closed-loop modulation for absence seizures caused by abnormal glutamate uptake from astrocytes. Nonlinear Dynamics. 112(5). 3903–3916. 27 indexed citations

Hu, Faping, Tianbo Yu, Hao Chen, et al.. (2024). Evolution of deformation mechanisms and their orientation dependence in fine-grained Mg-3Gd during tension. Journal of Magnesium and Alloys. 12(12). 5095–5107. 7 indexed citations

Han, Fang, et al.. (2023). Obsession, compulsion and learning in obsessive-compulsive disorder: A multilevel computational model. Neurocomputing. 549. 126461–126461. 2 indexed citations

Han, Fang, et al.. (2023). A TAN-dopamine interaction mechanism based computational model of basal ganglia in action selection. Cognitive Neurodynamics. 18(5). 2127–2144. 1 indexed citations

Han, Fang, Hanxi Wang, Xuan Luo, et al.. (2023). Microstructure and Texture Evolution of Hot-Rolled Mg-3Gd Alloy during Recrystallization. Metals. 13(7). 1216–1216. 1 indexed citations

Han, Fang, et al.. (2023). Task-relevant brain dynamics among cognitive subsystems induced by regional stimulation in a whole-brain computational model. Physical review. E. 108(4). 44402–44402. 5 indexed citations

10.

Han, Fang, et al.. (2022). A computational network dynamical modeling for abnormal oscillation and deep brain stimulation control of obsessive–compulsive disorder. Cognitive Neurodynamics. 17(5). 1167–1184. 19 indexed citations

11.

Yuan, Yuanyuan, et al.. (2022). Transition of Chimera States and Synchronization in Two-Layer Networks of Coupled Hindmarsh–Rose Neurons. International Journal of Bifurcation and Chaos. 32(1). 5 indexed citations

12.

Wang, Qingyun, et al.. (2022). Synaptic Role in Facilitating Synchronous Theta Oscillations in a Hybrid Hippocampal Neuronal Network. Frontiers in Computational Neuroscience. 16. 791189–791189. 1 indexed citations

13.

Yu, Ying, Fang Han, Qishao Wang, & Qingyun Wang. (2021). Model-based optogenetic stimulation to regulate beta oscillations in Parkinsonian neural networks. Cognitive Neurodynamics. 16(3). 667–681. 22 indexed citations

14.

Han, Fang, Zhijie Wang, Hong Fan, & Yaopeng Zhang. (2020). High-Frequency Synchronization Improves Firing Rate Contrast and Information Transmission Efficiency in E/I Neuronal Networks. Neural Plasticity. 2020. 1–11. 2 indexed citations

15.

Han, Fang, et al.. (2020). Dependency analysis of frequency and strength of gamma oscillations on input difference between excitatory and inhibitory neurons. Cognitive Neurodynamics. 15(3). 501–515. 16 indexed citations

16.

Han, Fang, Zhijie Wang, & Hong Fan. (2017). Determine Neuronal Tuning Curves by Exploring Optimum Firing Rate Distribution for Information Efficiency. Frontiers in Computational Neuroscience. 11. 10–10. 3 indexed citations

17.

Shi, Qi, et al.. (2015). Burst synchronization of inhibitory spiking neuronal networks. 129–134. 1 indexed citations

18.

Wang, Zhijie, Hong Fan, & Fang Han. (2014). A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory–inhibitory networks. Cognitive Neurodynamics. 8(4). 335–344. 4 indexed citations

19.

Du, Ying, et al.. (2012). Firing pattern and synchronization property analysis in a network model of the olfactory bulb. Cognitive Neurodynamics. 6(2). 203–209. 9 indexed citations

20.

Han, Fang. (2010). Reactive power optimization based on the improved dual population ant colony algorithm.

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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