主持上海市自然科学基金（No. 21ZR1443900，20万元，2021.9-2024.8）     

主持国家自然科学基金“时变网络结构下的生物钟模型研究” （面上项目，No.11875042, 60万元，2019.1-2022.12）

主持国家自然科学基金“双层网络下的振子集体行为研究：以生物钟神经元网络为例” （青年项目，No.11505114, 21.3万元，2016.1-2018.12）

     上海市教委“青年东方学者”人才计划 (No. QD2015016, 60万元研究经费, 2015.5-2017.12)

     沪江领军人才计划 (35万元研究经费, 2017.1-2019.12)

     主持上海高校青年教师资助计划(No.10-16-303-806，4.5万经费， 2016.1-2017.12）

     主持华东师范大学优秀博士基金 “不同光照条件下的哺乳动物近日节律”（No.2010027, 2009.12-2011.06）

作为主要研究成员（排名第二）完成刘宗华教授主持的国家自然科学基金“基于复杂网络的生物节律模型探索”（No.10975053, 2010.01-  2012.12） 在该课题资助下本人以第一作者身份发表 PLoS ONE 文章一篇，Journal of Biological Rhythms 文章一篇, Phy. Rew E 文章三篇，Chin. Phy. B 文章一篇。

          作为主要研究人员完成荷兰国家自然科学基金 “THE NEURONAL NETWORK ORGANIZATION OF THE BIOLOGICAL CLOCK” NWO   grant (No.010840, 2011.06-2015.06)。

          作为主要研究成员完成何大韧教授主持的国家自然科学基金“合作网络及合作-竞争网络的共性” （No.70671089, 2007.01-2009.12）。

在PNAS等期刊发表SCI论文80篇，研究成果被纽约时报、美国物理联合会主页等媒体报道（截止2021.8）：

1. ChenX, Weng T, Gu C, & Yang H (2019) Synchronizing hyperchaotic subsystems witha single variable: A reservoir computing approach. Physica A 534.

2.  Chen X, et al. (2020) Mapping topologicalcharacteristics of dynamical systems into neural networks: A reservoircomputing approach. Physical Review E 102(3).

3.  Deng S, Ren H, Weng T, Gu C, & Yang H(2019) Information on evolutionary age in redundancy of complex network. ModPhys Lett B 33(27).

4.  Ding W-X, Gu C-G, & Liang X-M (2016) ASimple Structure for Signal Amplification. Commun Theor Phys 65(2):189-192.

5.  Feng J & Gu C (2020) Scale invariance inthe series of Chinese-character lengths. International Journal of ModernPhysics C 31(1).

6.  Feng W, Yang Y, Yuan Q, Gu C, & Yang H(2019) EVOLUTION OF SCALING BEHAVIORS IN CURRENCY EXCHANGE RATE SERIES.Fractals 27(2).

7.  Gao J & Gu C (2019) Super Multi-Armed andSegmented Spiral Pattern in a Reaction-Diffusion Model. Ieee Access7:140391-140401.

8.  Gao J, Gu C, & Yang H (2020) Spiral waveswith interfacial oscillatory chemical reactions emerge in a model ofreaction-diffusion systems. Chem Phys 528.

9.  Gao J, Gu C, & Yang H (2021) Applying aglobal pulse disturbance to eliminate spiral waves in models of cardiacmuscle*. Chinese Phys B 30(7).

10.Gao J, Gu C, Yang H, & Wang M (2021) Aflight formation mechanism: The weight of repulsive force. Commun Nonlinear Sci95.

11.Gao J, Gu C, Yang H, & Weng T (2019) Sizeof a steady disturbance source affects the frequency of a target wave. Aip Adv9(8).

12.Gao J, Gu C, Yang H, & Weng T (2020)Excited state of spiral waves in oscillatory reaction-diffusion systems causedby a pulse. Physical Review E 101(4).

13.Gao J, Gu C, Yang H, & Weng T (2020)Prediction of spatial distribution of invasive alien pests in two-dimensionalsystems based on a discrete time model. Ecol Eng 143.

14.Gao J, Gu C, Yang H, & Weng T (2020) A typeof bi-stable spiral wave in a single -period oscillatory medium. CommunNonlinear Sci 85.

15.Gu C, et al. (2015) Lack of exercise leads tosignificant and reversible loss of scale invariance in both aged and youngmice. Proceedings of the National Academy of Sciences of the United States ofAmerica 112(8):2320-2324.

16.Gu C, et al. (2019) Splitting between twosubgroups of the SCN neurons with instantaneous feedback. Nonlinear Dynam97(2):1245-1251.

17.Gu C, et al. (2019) Disassortative NetworkStructure Improves the Synchronization between Neurons in the SuprachiasmaticNucleus. J Biol Rhythm 34(5):515-524.

18.Gu C, Liang X, Yang H, & Rohling JHT (2016)Heterogeneity induces rhythms of weakly coupled circadian neurons. ScientificReports 6.

19.Gu C, Liu Z, Schwartz WJ, & Indic P (2012)Photic Desynchronization of Two Subgroups of Circadian Oscillators in a NetworkModel of the Suprachiasmatic Nucleus with Dispersed Coupling Strengths. PLoSOne 7(5).

20.Gu C, Ramkisoensing A, Liu Z, Meijer JH, &Rohling JHT (2014) The Proportion of Light-Responsive Neurons Determines theLimit Cycle Properties of the Suprachiasmatic Nucleus. J Biol Rhythm29(1):16-27.

21.Gu C, Rohling JHT, Liang X, & Yang H (2016)Impact of dispersed coupling strength on the free running periods of circadianrhythms. Physical Review E 93(3).

22.Gu C, Tang M, Rohling JHT, & Yang H (2016)The effects of non-self-sustained oscillators on the en-trainment ability ofthe suprachiasmatic nucleus. Scientific Reports 6.

23.Gu C, Tang M, & Yang H (2016) Thesynchronization of neuronal oscillators determined by the directed networkstructure of the suprachiasmatic nucleus under different photoperiods.Scientific Reports 6.

24.Gu C, Wang J, & Liu Z (2009) Free-runningperiod of neurons in the suprachiasmatic nucleus: Its dependence on thedistribution of neuronal coupling strengths. Physical Review E 80(3).

25.Gu C, Wang J, Wang J, & Liu Z (2011)Mechanism of phase splitting in two coupled groups of suprachiasmatic-nucleusneurons. Physical Review E 83(4).

26.Gu C, Wang P, Weng T, Yang H, & Rohling J(2019) Heterogeneity of neuronal properties determines the collective behaviorof the neurons in the suprachiasmatic nucleus. Math Biosci Eng 16(4):1893-1913.

27.Gu C, Xu J, Liu Z, & Rohling JHT (2013)Entrainment range of nonidentical circadian oscillators by a light-dark cycle.Physical Review E 88(2).

28.Gu C, Xu J, Rohling J, Yang H, & Liu Z(2015) Noise Induces Oscillation and Synchronization of the Circadian Neurons.PLoS One 10(12).

29.Gu C & Yang H (2016) The circadian rhythminduced by the heterogeneous network structure of the suprachiasmatic nucleus.Chaos 26(5).

30.Gu C & Yang H (2017) The asymmetry of theentrainment range induced by the difference in intrinsic frequencies betweentwo subgroups within the suprachiasmatic nucleus. Chaos 27(6).

31.Gu C & Yang H (2017) Differences inintrinsic amplitudes of neuronal oscillators improve synchronization in thesuprachiasmatic nucleus. Chaos 27(9).

32.Gu C, Yang H, Meijer JH, & Rohling JHT(2018) Dependence of the entrainment on the ratio of amplitudes between twosubgroups in the suprachiasmatic nucleus. Physical Review E 97(6).

33.Gu C, Yang H, & Rohling JHT (2017)Dissociation between two subgroups of the suprachiasmatic nucleus affected bythe number of damped oscillated neurons. Physical Review E 95(3).

34.Gu C, Yang H, & Ruan Z (2017) Entrainmentrange of the suprachiasmatic nucleus affected by the difference in the neuronalamplitudes between the light-sensitive and light-insensitive regions. PhysicalReview E 95(4).

35.Gu C, Yang H, & Wang M (2017) Dispersion ofthe intrinsic neuronal periods affects the relationship of the entrainmentrange to the coupling strength in the suprachiasmatic nucleus. Physical ReviewE 96(5).

36.Gu C, Yang H, Wang M, & Rohling JHT (2019)Heterogeneity in relaxation rate improves the synchronization of oscillatoryneurons in a model of the SCN. Chaos 29(1).

37.Gu C-G, Wang P, & Yang H-J (2019)Entrainment range affected by the heterogeneity in the amplitude relaxationrate of suprachiasmatic nucleus neurons. Chinese Phys B 28(1).

38.Gu C-G, Yang H-J, & Wang M (2018) RatioBetween Sensitive Strength to Light Information and Coupling Strength AffectsEntrainment Range of Suprachiasmatic Nucleus. Commun Theor Phys 70(6):771-776.

39.Gu C-G, Zhang X-H, & Liu Z-H (2014)Collective behaviors of suprachiasm nucleus neurons under different light-darkcycles. Chinese Phys B 23(7).

40.Gu C-G, et al. (2011) Onset of cooperationbetween layered networks. Physical Review E 84(2).

41.Gu Q-C, Qin G-Q, Wang Y-Q, Gu C-G, & YangH-J (2019) Scale-Invariance Exists in the Series of Character Intervals in theFour Great Chinese Novels. Commun Theor Phys 71(9):1139-1142.

42.Li J, Gu C, & Yang H (2020) Noise inducesoscillation in the two weakly coupled subgroups of the suprachiasmatic nucleus.Nonlinear Dynam 102(4):2759-2766.

43.Li W-J, Jiang L-L, Gu C, & Yang H (2017)The influence of migration speed on cooperation in spatial games. J StatMech-Theory E.

44.Li W-J, Jiang L-L, Gu C, & Yang H (2018)Influentials promote cooperation in spatial snowdrift games. J Stat Mech-TheoryE.

45.Liu K, Weng T, Gu C, & Yang H (2020)Visibility graph analysis of Bitcoin price series. Physica A 538.

46.Liu Z, Xiao Q, Zhan Q, Gu C, & Yang H(2017) Network-based landscape of research strengths of universities inMainland China. Physica A 478:49-62.

47.Mutua S, Gu C, & Yang H (2016) Visibilitygraphlet approach to chaotic time series. Chaos 26(5).

48.Qiu L, Gu C, Xiao Q, Yang H, & Wu G (2018)State network approach to characteristics of financial crises. Physica A492:1120-1128.

49.Qiu L, Yang T, Yin Y, Gu C, & Yang H (2016)Multifractals embedded in short time series: An unbiased estimation ofprobability moment. Physical Review E 94(6).

50.Ramkisoensing A, et al. (2014) Enhanced PhaseResetting in the Synchronized Suprachiasmatic Nucleus Network. J Biol Rhythm29(1):4-15.

51.Ren H, Yang Y, Gu C, Weng T, & Yang H(2018) A Patient Suffering From Neurodegenerative Disease May Have aStrengthened Fractal Gait Rhythm. Ieee T Neur Sys Reh 26(9):1765-1772.

52.Ren H, et al. (2020) Pattern interdependentnetwork of cross-correlation in multivariate time series. Phys Lett A 384(30).

53.Ruan Z, Tang M, Gu C, & Xu J (2017) Epidemicspreading between two coupled subpopulations with inner structures. Chaos27(10).

54.Song J, Weng T-F, Gu C-G, & Yang H-J (2020)Patterns of cross-correlation in time series: A case study of gait trails*.Chinese Phys B 29(8).

55.Stephen M, Gu C, & Yang H (2015) VisibilityGraph Based Time Series Analysis. PLoS One 10(11).

56.Wang Y, Ca X, Weng T, Yang H, & Gu C (2021)Lowest-degree preference random walks on complex networks. Physica A 577.

57.Wang Y, Cao X, Weng T, Yang H, & Gu C(2021) A convex principle of search time for a multi-biased random walk oncomplex networks. Chaos Solitons & Fractals 147.

58.Wang Y, et al. (2014) An Automatic HighEfficient Method for Dish Concentrator Alignment. Mathematical Problems inEngineering 2014.

59.Wang Y, Weng T, Deng S, Gu C, & Yang H(2019) Sampling frequency dependent visibility graphlet approach to timeseries. Chaos 29(2).

60.Weng T, et al. (2020) Synchronization ofreservoir computers with applications to communications. Physica A 544.

61.Weng T, et al. (2021) Representing complexnetworks without connectivity via spectrum series. Information Sciences563:16-22.

62.Weng T, et al. (2019) Predator-prey games oncomplex networks. Commun Nonlinear Sci 79.

63.Weng T, Yang H, Gu C, Zhang J, & Small M (2019)Synchronization of chaotic systems and their machine-learning models. PhysicalReview E 99(4).

64.Wu G, Gu C, Qiu L, & Yang H (2017) Auniform framework of projection and community detection for one-mode network inbipartite networks. Chinese Phys B 26(12).

65.Wu G, Gu C, Qiu L, & Yang H (2018)Community detection based on preferred mode in bipartite networks. Mod PhysLett B 32(27).

66.Wu J, Zheng M, Wang W, Yang H, & Gu C(2018) Double transition of information spreading in a two-layered network. Chaos28(8).

67.Wu J, Zheng M, Xu K, & Gu C (2020) Effectsof two channels on explosive information spreading. Nonlinear Dynam99(3):2387-2397.

68.Wu J, et al. (2018) A model of spreading ofsudden events on social networks. Chaos 28(3).

69.Xu J, Gu C, Pumir A, Garnier N, & Liu Z(2012) Entrainment of the suprachiasmatic nucleus network by a light-darkcycle. Physical Review E 86(4).

70.Yang H, Gu C, Tang M, Cai S-M, & Lai Y-C(2019) Suppression of epidemic spreading in time-varying multiplex networks. ApplMath Model 75:806-818.

71.Yang T, Gu C, & Yang H (2016) Long-RangeCorrelations in Sentence Series from A Story of the Stone. PLoS One 11(9).

72.Yang Y, Gu C, Xiao Q, & Yang H (2017)Evolution of scaling behaviors embedded in sentence series from A Story of theStone. PLoS One 12(2).

73.Yang Y, et al. (2017) Scaling invarianceembedded in very short time series: A factorial moment based diffusion entropyapproach. Chinese Journal of Physics 55(6):2325-2335.

74.Yu X, Weng T, Gu C, & Yang H (2020) Comparisonof gene regulatory networks to identify pathogenic genes for lymphoma. J BioinfComput Biol 18(5).

75.Yuan Q, Gu C, Weng T, & Yang H (2018)Unbiased detrended fluctuation analysis: Long-range correlations in very shorttime series. Physica A 505:179-189.

76.Yuan Q, et al. (2021) Multi-scale transitionmatrix approach to time series. Physica A 578.

77.Zhang K, et al. (2021) Synchronization ofchaotic systems and long short-term memory networks by sharing a singlevariable. Mod Phys Lett B 35(6).

78.Zhao Y, Gu C, & Yang H (2021)Visibility-graphlet approach to the output series of a Hodgkin-Huxley neuron.Chaos 31(4).

79.Zhou L, Qiu L, Gu C, & Yang H (2018)Immediate causality network of stock markets. Epl-Europhys Lett 121(4).

80.Zhu B, Zhou J, Jia M, Yang H, & Gu C (2020)Entrainment range affected by the difference in sensitivity tolight-information between two groups of SCN neurons. Chinese Phys B 29(6).