Department of Medicine

University of California, USA

Project Scientist

Email: [email protected]

Qualifications

1984 Postdoctoral training, Department of Physiology, University of Birmingham, UK

1955 M.D., Faculty of Medicine, Shanghai First Medical College, China

Publications (Selected)

1. Zhang, W., Geng, H., & Li, P. (2024). Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization. Frontiers in Neuroscience, 18, Article 1412559.
2. Ma, H., Zhou, D., Li, P., et al. (2023). EVOAC-HP: An Efficient and Verifiable Outsourced Access Control Scheme with Hidden Policy. Sensors, 23(9), Article 4384.
3. Zhang, W., & Li, P. (2021). Skip-Connected Self-Recurrent Spiking Neural Networks With Joint Intrinsic Parameter and Synaptic Weight Training. Neural Computation, 33(7), 1886-1913.
4. Sheng, Y., Huang, T., Zeng, Z., et al. (2021). Exponential Stabilization of Inertial Memristive Neural Networks With Multiple Time Delays. IEEE Transactions on Cybernetics, 51(2), 579-588.
5. Sun, S., An, X., Zhao, J., et al. (2021). Modeling and Simulation of Lane-Changing Management Strategies at On-Ramp and Off-Ramp Pair Areas Based on Cellular Automaton. IEEE Access, 9, 35034-35044.
6. Riad, J., Li, P., & Sánchez-Sinencio, E. (2020). A Stabilizing Centralized Controller for On-Chip Power Delivery Networks. IEEE Transactions on Circuits and Systems II-Express Briefs, 67(4), 680-684.
7. Lee, J., Zhang, R., Zhang, W., et al. (2020). Spike-Train Level Direct Feedback Alignment: Sidestepping Backpropagation for On-Chip Training of Spiking Neural Nets. Frontiers in Neuroscience, 14, Article 143.
8. Sun, B., Wen, S., Wang, S., et al. (2020). Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm. Neurocomputing, 380, 133-140.
9. Xu, C., Zhang, W., Liu, Y., et al. (2020). Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators Using Time Compression Supporting Multiple Spike Codes. Frontiers in Neuroscience, 14, Article 104.
10. Deng, L., Wu, Y., Hu, X., et al. (2020). Rethinking the performance comparison between SNNS and ANNS. Neural Networks, 121, 294-307.
11. Li, P., Wang, R., Wang, Y., et al. (2020). Evaluation of the ICP Algorithm in 3D Point Cloud Registration. IEEE Access, 8, 68030-68048.
12. Wang, S., Cao, Y., Huang, T., et al. (2020). Sliding mode control of neural networks via continuous or periodic sampling event-triggering algorithm. Neural Networks, 121, 140-147.
13. Zhan, X., Chen, J., Sánchez-Sinencio, E., et al. (2019). Power Management for Multicore Processors via Heterogeneous Voltage Regulation and Machine Learning Enabled Adaptation. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27(11), 2641-2654.
14. Liu, Y., Zhang, W., & Li, P. (2019). Enabling Non-Hebbian Learning in Recurrent Spiking Neural Processors With Hardware-Friendly On-Chip Intrinsic Plasticity. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9(3), 465-474.
15. Zhan, X., Li, P., & Sánchez-Sinencio, E. (2019). Taming the Stability-Constrained Performance Optimization Challenge of Distributed On-Chip Voltage Regulation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 38(8), 1571-1584.
16. Liu, Y., Yenamachintala, S. S., & Li, P. (2019). Energy-efficient FPGA Spiking Neural Accelerators with Supervised and Unsupervised Spike-timing-dependent-Plasticity. ACM Journal on Emerging Technologies in Computing Systems, 15(3), Article 27.
17. Zhang, W., & Li, P. (2019). Information-Theoretic Intrinsic Plasticity for Online Unsupervised Learning in Spiking Neural Networks. Frontiers in Neuroscience, 13, Article 31.
18. Zhan, X., Riad, J., Li, P., et al. (2018). Design Space Exploration of Distributed On-Chip Voltage Regulation Under Stability Constraint. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 26(8), 1580-1584.
19. Xu, C., Liu, Y., Li, P., et al. (2018). Unified multi-objective mapping for network-on-chip using genetic-based hyper-heuristic algorithms. IET Computers & Digital Techniques, 12(4), 158-166.
20. Yamamoto, A. Y., Sundqvist, K. M., Li, P., et al. (2018). Simulation of a Multidimensional Input Quantum Perceptron. Quantum Information Processing, 17(6), Article 128.
21. Chen, J., Li, P., Song, G., et al. (2018). Feedback Control for Structural Health Monitoring in a Smart Aggregate Based Sensor Network. International Journal of Structural Stability and Dynamics, 18(5), Article 1850064.
22. Liu, Y., Jin, Y., & Li, P. (2018). Online Adaptation and Energy Minimization for Hardware Recurrent Spiking Neural Networks. ACM Journal on Emerging Technologies in Computing Systems, 14(1), Article 11.
23. Chen, J., Li, P., Song, G., et al. (2018). Systematic Development of a Wireless Sensor Network for Piezo-Based Sensing. Journal of Sensors, 2018, Article 9315364.
24. Chen, J., Zhao, Q., Liang, Z., et al. (2017). Fractional Calculus Guidance Algorithm in a Hypersonic Pursuit-Evasion Game. Defence Science Journal, 67(6), 688-697.
25. Chen, J., Liang, Z., Bai, C., et al. (2017). Guidance and control laws design for a switched system with discontinuous actuators. Optimal Control Applications and Methods, 38(5), 829-846.

Profile Details
https://www.faculty.uci.edu/profile/?facultyId=5206
https://www.researchgate.net/profile/Peng-Li-173
https://www.lsi.umich.edu/science/our-labs/li-lab/peng-li-phd

WOS ResearcherID: HQB-1132-2023