Li，Xiumin, Associate Professor of Chongqing University, China

She received her Ph.D. degree in Electronic and Information Engineering from Hong Kong Polytechnic University, Hong Kong, in 2011. She is currently the Associate Professor of the School of Automation, Chongqing University, Chongqing, China.

Her research interests include computational neuroscience, neurodynamics, neural network modeling, and brain-inspired intelligent computation. She has published more than 20 papers in international journals and hosted several projects of the National Natural Science Foundation of China and the Natural Science Foundation of Chongqing. She is the reviewer of international journals such as IEEE Trans. on Neural Networks, Neural Networks, Neurocomputing, and Cognitive Neurodynamics.

Contact Information

Email: xmli@cqu.edu.cn

Address: Shazheng Street174#, Shapingba District,Chongqing, 400044,China

Education

Ph.D., (E.E.), Hong Kong Polytechnic University, Hong Kong, 2011.

M.S., (E.E.), Tianjin University, Tianjin, P. R. China, 2007.

B.S., (E.E.), Taiyuan University of Technology, Tianyuan, P. R. China, 2005.

Academic Experience

Visiting Scholar, 1/2019 – 12/2019, University of California, Irvine, USA

Associate Professor, 9/2014- present, Chongqing University, Chongqing, P. R. China

Lecturer, 7/2011-8/2014, Chongqing University, Chongqing, P. R. China

Research Associate, 10/2010-10/2011, Hong Kong Polytechnic University, Hong Kong

Visiting PhD Student, 9/2008-12/2008, University of Cambridge, Cambridge, UK

Research Interests

Computational Neuroscience

Neural networks

Brain-inspired Intelligent Computing

Publications

Part I- Journal Papers

[1] X. Li*, H. Yi, S. Luo. Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning[J]. Neural Plasticity, 2020, 2020(2):1-11.

[2] X. Li*, S. Luo, F. Xue. Effects of synaptic integration on the dynamics and computational performance of spiking neural network. Cognitive Neurodynamics 14, 2020, 347–357.

[3] A. Zhang, H. Zhou, X. Li* etc. Fast and robust learning in Spiking Feed-forward Neural Networks based on Intrinsic Plasticity mechanism. Neurocomputing. 2019 Nov 6;365:102-12.

[4] X. Li*,W. Wan, F. Xue, et al. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity[J]. Physica A Statistical Mechanics & Its Applications, 2018, 491.

[5] X. Li*, Q Chen, F. Xue, Biological modeling of computational spiking neural network with neuronal avalanches. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2017 May 15;375(2096):20160286.

[6] X. Li*, H. Liu, F. Xue, H. Zhou, Y. Song. Liquid computing of spiking neural network with multi-clustered and active-neuron-dominant structure. Neurocomputing, 2017, 243.

[7] F. Xue, Q. Li, X. Li*. The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.[J]. Plos One, 2017, 12(7):e0181816.

[8] F. Xue, Q. Li,  H. Zhou, X. Li*. Reservoir Computing with Both Neuronal Intrinsic Plasticity and Multi-Clustered Structure[J]. Cognitive Computation, 2017(5):1-11.

[9] X. Li*, Q Chen, F. Xue, Bursting dynamics remarkably improve the performance of neural networks on liquid computing. Cognitive Neurodynamics, 2016, 10(5):1-7.

[10] X. Li, L. Zhong, F. Xue*, A. Zhang. A Priori Data-Driven Multi-Clustered Reservoir Generation Algorithm for Echo State Network. PLOS ONE, 10(4): e0120750,  2015.

[11] H. Liu, Y. Song, F. Xue, X. Li*. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule[J].  Chaos 25(11): 113108- 113108, 2015.

[12] X. Li*. Signal integration on the dendrites of a pyramidal neuron model. Cognitive Neurodynamics, 8 (1), 81-85, 2014.

[13] X. Li, Kenji Morita, Hugh Robinson*, Michael Small. Control of layer 5 pyramidal cell spiking by oscillatory inhibition in the distal apical dendrites: a computational modeling study. Journal of neurophysiology, 109 (11), 2739-2756, 2013.

[14] F. Xue*, Z. Hou, and X. Li. Computational capability of liquid state machines with spike-timing-dependent plasticity. Neurocomputing, 122: 324-329, 2013.

[15] X. Li* and Michael Small. “Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure”. Chaos, 22 (2): 023104, 2012.

[16] X. Li*, K. Morita, H. P. C. Robinson and M. Small, “Impact of gamma-oscillatory inhibition on the signal transmission of a cortical pyramidal neuron”. Cognitive Neurodynamics 5 (2011) Issue 3 Page 241-251.

[17] X. Li* and M. Small, “Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity,”New Journal of Physics 12 (2010) 083045.

[18] X. Li*, J. Zhang and M. Small, “Self-organization of a neural network with heterogeneous neurons enhances coherence and stochastic resonance,” Chaos 19 (2009) 013126.

[19] J. Zhou, W. Yu, X. Li, M. Small and J. Lu*, “Identifying the topology of a coupled FitzHugh-Nagumo neurobiological network via a pinning mechanism,” IEEE Transactions on Neural Networks 20 (2009).

[20] X. Li, J. Wang, W. Hu,  “Effects of chemical synapses on the enhancement of signal propagation in coupled neurons near the canard regime”, Physical Review E 76 (2007) 041902.  

[21] F. Xue, Z. Hou, X. Li, N. Li. State switching optimization and global stability control strategy for underactuated two-link manipulator[J]. Chinese Journal of Scientific Instrument, 2012, 33(5):1035-1040.

[22] W. Hu, G. Xiao, and X. Li, “An analytical method for PID controller tuning with specified gain and phase margins for integral plus time delay processes”, ISA Transactions 50: 268–276 (2011).  

[23] W. Hu, J. Wang, X. Li, “An approach of partial control design for system control and synchronization”, Chaos, Solitons & Fractals 39: 1410–1417 (2009).Dai Xin. Sun Yue, An Accurate Frequency Tracking Method Based on Short Current Detection for Inductive Power Transfer System. IEEE Transactions on Industrial Electronics, 61(2): 776-783, 2014

Part II- Conference Papers

Over 20 Conference papers including International Symposium on Autonomous Systems (ISAS), International Symposium on Neural Networks (ISNN), Chinese Conference on Control and Decision (CCDC) , International Conference on Cognitive Neurodynamics (ICCN), etc.

Research Grants

1. Chongqing Natural Science Foundation: Visual cognitive computing model for small sample image recognition, 2019-2022, presided.

2. International (regional) Cooperation and Exchange Project of NSFC: Control theory based on brain operation characteristics and its application in flexible dexterous under drive robot system, 2019-2021, participated.

3. Chongqing Natural Science Foundation: Event-driven spiking neural network learning method research, 2016-2019, presided.

4. National Natural Science Foundation of China: Small reserve pool computing for Robot Oriented spiking neural network, 2015-2018, participated.

6. National Natural Science Foundation of China: Self-organization evolution of neural network and formation of complex hierarchical structure under hybrid learning mechanism, 2014-2016, presided.

8. National Natural Science Foundation of China: Network reconstruction method based on communication dynamics, 2012-2014, participated.