2024
Yang, Z., Wang, T., Lin, Y., Chen, Y., Zeng, H., Pei, J., … & Shi, L. (2024). A vision chip with complementary pathways for open-world sensing. Nature, 629(8014), 1027-1033.
Zheng, H., Zheng, Z., Hu, R., Xiao, B., Wu, Y., Yu, F., … & Deng, L. (2024). Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics. Nature Communications, 15(1), 277.
Liu, F., Zheng, H., Ma, S., Zhang, W., Liu, X., Chua, Y., … & Zhao, R. (2024). Advancing brain-inspired computing with hybrid neural networks. National Science Review, 11(5), nwae066.
Baek, E., Song, S., Baek, C. K., Rong, Z., Shi, L., & Cannistraci, C. V. (2024). Neuromorphic dendritic network computation with silent synapses for visual motion perception. Nature Electronics, 1-12.
Zhang, W., Ma, S., Ji, X., Liu, X., Cong, Y., & Shi, L. (2024). The development of general-purpose brain-inspired computing. Nature Electronics, 1-12.
Niu, T., Huang, H., Du, Y., Zhang, W., Shi, L., & Zhao, R. (2024). General Automatic Solution Generation of Social Problems. Machine Intelligence Research
Niu, T., Zhang, W., & Zhao, R. (2024). Solution-oriented Agent-based Models Generation with Verifier-assisted Iterative In-context Learning. 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
Du, Y., Liu, X., & Chua, Y. (2024, April). Spiking structured state space model for monaural speech enhancement. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 766-770). IEEE.
Yin, H., Zheng, H., Mao, J., Ding, S., Liu, X., Xu, M., … & Deng, L. (2024). Understanding the Functional Roles of Modelling Components in Spiking Neural Networks. Neuromorphic Computing and Engineering
Xu, M., Liu, F., Hu, Y., Li, H., Wei, Y., Zhong, S., … & Deng, L. (2024). Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness. IEEE Transactions on Neural Networks and Learning Systems.
2023
Li, H., Ma, S., Wang, T., Zhang, W., Wang, G., Song, C., … & Zhao, R. (2023). HASP: Hierarchical asynchronous parallelism for multi-NN tasks. IEEE Transactions on Computers.
Yu, F., Wu, Y., Ma, S., Xu, M., Li, H., Qu, H., … & Shi, L. (2023). Brain-inspired multimodal hybrid neural network for robot place recognition. Science Robotics, 8(78), eabm6996.
Pei, J., Deng, L., Ma, C., Liu, X., & Shi, L. (2023). Multi-grained system integration for hybrid-paradigm brain-inspired computing. Science China Information Sciences, 66(4), 142403.
Guo, Y., Duan, W., Liu, X., Wang, X., Wang, L., Duan, S., … & Li, H. (2023). Generative complex networks within a dynamic memristor with intrinsic variability. Nature Communications, 14(1), 6134.
Li, L., Shi, L., & Zhao, R. (2023, May). A Vertical-Horizontal Integrated Neuro-Symbolic Framework Towards Artificial General Intelligence. In International Conference on Artificial General Intelligence（ICAGI） (pp. 197-206). Cham: Springer Nature Switzerland.
Zheng, H., & Shi, L. (2023, May). Coherence in Intelligent Systems. In International Conference on Artificial General Intelligence (pp. 357-366). Cham: Springer Nature Switzerland.
Xu, M., Zheng, H., Pei, J., & Deng, L. (2023, May). A Unified Structured Framework for AGI: Bridging Cognition and Neuromorphic Computing. In International Conference on Artificial General Intelligence (pp. 345-356). Cham: Springer Nature Switzerland.
Zheng, H., Lin, H., & Zhao, R. (2024). GUST: combinatorial generalization by unsupervised grouping with neuronal coherence. Advances in Neural Information Processing Systems, 36.
Li, H., Xu, M., Pei, J., & Zhao, R. (2023, August). Efficient GCN Deployment with Spiking Property on Spatial-Temporal Neuromorphic Chips. In Proceedings of the 2023 International Conference on Neuromorphic Systems (pp. 1-8).
Lin, J., Qu, H., Ma, S., Ji, X., Li, H., Li, X., … & Zhang, W. (2023). SongC: A compiler for hybrid near-memory and in-memory many-core architecture. IEEE Transactions on Computers.
Liu, J., Hu, Y., Li, G., Pei, J., & Deng, L. (2023). Spike attention coding for spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems.
Zeng, H., & Zhao, R. (2023). Perceptually-guided Dual-mode Virtual Reality System For Motion-adaptive Display. IEEE Transactions on Visualization and Computer Graphics, 29(5), 2249-2257.
Wang, S., Yu, Q., Xie, T., Ma, C., & Pei, J. (2023). Approaching the mapping limit with closed-loop mapping strategy for deploying neural networks on neuromorphic hardware. Frontiers in Neuroscience, 17, 1168864.
Zhang, W., Du, Y., Li, H., Ma, S., & Zhao, R. (2024). General-purpose Dataflow Model with Neuromorphic Primitives. ICONS 2023
Yin, H., Xu, M., Pei, J., & Deng, L. (2024). Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks.5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2023)
2022
Zhao, R., Yang, Z., Zheng, H., Wu, Y., Liu, F., Wu, Z., … & Shi, L. (2022). A framework for the general design and computation of hybrid neural networks. Nature communications, 13(1), 3427.
Wu, Y., Zhao, R., Zhu, J., Chen, F., Xu, M., Li, G., … & Shi, L. (2022). Brain-inspired global-local learning incorporated with neuromorphic computing. Nature Communications, 13(1), 65.
Ma, S., Pei, J., Zhang, W., Wang, G., Feng, D., Yu, F., … & Shi, L. (2022). Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots. Science Robotics, 7(67), eabk2948.
Zheng, H., Lin, H., Zhao, R., & Shi, L. (2022). Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention. Advances in Neural Information Processing Systems, 35, 31430-31443.
2021
Wang, G., Ma, S., Wu, Y., Pei, J., Zhao, R., & Shi, L. (2021). End-to-end implementation of various hybrid neural networks on a cross-paradigm neuromorphic chip. Frontiers in Neuroscience, 15, 615279.
Tian, L., Wu, Z., Wu, S., & Shi, L. (2021). Hybrid neural state machine for neural network. Science China Information Sciences, 64, 1-13.
Tian, L., Wang, Y., Shi, L., & Zhao, R. (2020). High robustness memristor neural state machines. ACS Applied Electronic Materials, 2(11), 3633-3642.
Lim, D. H., Wu, S., Zhao, R., Lee, J. H., Jeong, H., & Shi, L. (2021). Spontaneous sparse learning for PCM-based memristor neural networks. Nature communications, 12(1), 319.
Liu, F., Xu, M., Li, G., Pei, J., Shi, L., & Zhao, R. (2021). Adversarial symmetric GANs: Bridging adversarial samples and adversarial networks. Neural Networks, 133, 148-156.
2020
Zou, Z., Zhao, R., Wu, Y., Yang, Z., Tian, L., Wu, S., … & Shi, L. (2020). A hybrid and scalable brain-inspired robotic platform. Scientific reports, 10(1), 18160.
Zhang, Y., Qu, P., Ji, Y., Zhang, W., Gao, G., Wang, G., … & Shi, L. (2020). A system hierarchy for brain-inspired computing. Nature, 586(7829), 378-384.
Wang, Y., Wu, S., Tian, L., & Shi, L. (2020). SSM: a high-performance scheme for in situ training of imprecise memristor neural networks. Neurocomputing, 407, 270-280.
Deng, L., Wang, G., Li, G., Li, S., Liang, L., Zhu, M., … & Shi, L. (2020). Tianjic: A unified and scalable chip bridging spike-based and continuous neural computation. IEEE Journal of Solid-State Circuits, 55(8), 2228-2246.
Li, G., Tang, P., Chen, X., Xiao, G., Meng, M., Ma, C., & Shi, L. (2020). Target control and expandable target control of complex networks. Journal of the Franklin Institute, 357(6), 3541-3564.
Deng, L., Li, G., Han, S., Shi, L., & Xie, Y. (2020). Model compression and hardware acceleration for neural networks: A comprehensive survey. Proceedings of the IEEE, 108(4), 485-532.
施路平, & 邓磊. (2020). 双 “脑” 驱动人工通用智能发展. 前沿科学, 14(1), 9-12.
施路平, 裴京, & 赵蓉. (2020). 面向人工通用智能的类脑计算. 人工智能, (1), 6-15.
2019
Li, G., Chen, X., Tang, P., Xiao, G., Wen, C., & Shi, L. (2019). Target control of directed networks based on network flow problems. IEEE Transactions on Control of Network Systems, 7(2), 673-685.
Li, H., & Shi, L. (2019). Robust event-based object tracking combining correlation filter and CNN representation. Frontiers in neurorobotics, 13, 82.
Pei, J., Deng, L., Song, S., Zhao, M., Zhang, Y., Wu, S., … & Shi, L. (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 572(7767), 106-111.
Wang, Y., Zhang, Z., Xu, M., Yang, Y., Ma, M., Li, H., … & Shi, L. (2019). Self-doping memristors with equivalently synaptic ion dynamics for neuromorphic computing. ACS applied materials & interfaces, 11(27), 24230-24240.
Lee, J. H., Lim, D. H., Jeong, H., Ma, H., & Shi, L. (2019). Exploring cycle-to-cycle and device-to-device variation tolerance in MLC storage-based neural network training. IEEE Transactions on Electron Devices, 66(5), 2172-2178.
Li, H., Li, G., & Shi, L. (2019). Super-resolution of spatiotemporal event-stream image. Neurocomputing, 335, 206-214.
Zhang, Z., Li, T., Wu, Y., Jia, Y., Tan, C., Xu, X., … & Li, H. (2019). Truly concomitant and independently expressed short‐and long‐term plasticity in a Bi2O2Se‐based three‐terminal memristor. Advanced Materials, 31(3), 1805769.
Jeong, H., & Shi, L. (2018). Memristor devices for neural networks. Journal of Physics D: Applied Physics, 52(2), 023003.
Wang, Y., Zhang, Z., Li, H., & Shi, L. (2019). Realizing bidirectional threshold switching in Ag/Ta 2 O 5/Pt diffusive devices for selector applications. Journal of Electronic Materials, 48, 517-525.
Wu, S., Wang, G., Tang, P., Chen, F., & Shi, L. (2019). Convolution with even-sized kernels and symmetric padding. Advances in Neural Information Processing Systems, 32.
Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., & Shi, L. (2019, July). Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 1311-1318).
2018
Wu, S., Li, G., Deng, L., Liu, L., Wu, D., Xie, Y., & Shi, L. (2018). $ L1 $-norm batch normalization for efficient training of deep neural networks. IEEE transactions on neural networks and learning systems, 30(7), 2043-2051.
Zhang, Y., He, W., Wu, Y., Huang, K., Shen, Y., Su, J., … & Shi, L. (2018). Highly compact artificial memristive neuron with low energy consumption. Small, 14(51), 1802188.
Li, G., Tang, P., Meng, Z., Wen, C., Pei, J., & Shi, L. (2018). Optimization on matrix manifold based on gradient information and its applications in network control. Physica A: Statistical Mechanics and its Applications, 508, 481-500.
Tang, P., Li, G., Ma, C., Wang, R., Xiao, G., & Shi, L. (2018). Matrix function optimization under weighted boundary constraints and its applications in network control. ISA transactions, 80, 232-243.
Li, H., Li, G., Ji, X., & Shi, L. (2018). Deep representation via convolutional neural network for classification of spatiotemporal event streams. Neurocomputing, 299, 1-9.
Li, H., Xu, X., Zhang, Y., Gillen, R., Shi, L., & Robertson, J. (2018). Native point defects of semiconducting layered Bi2O2Se. Scientific Reports, 8(1), 10920.
Zhang, Z., Wang, Y., Li, H., Wu, Y., Wang, G., & Shi, L. (2018). Engineering the Synaptic Kinetic Process into Memristive Device. Advanced Electronic Materials, 4(6), 1800096.
Wu, Y., Deng, L., Li, G., Zhu, J., & Shi, L. (2018). Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in neuroscience, 12, 331.
Li, G., Deng, L., Xiao, G., Tang, P., Wen, C., Hu, W., … & Stanley, H. E. (2018). Enabling controlling complex networks with local topological information. Scientific reports, 8(1), 4593.
Li, G., Deng, L., Tian, L., Cui, H., Han, W., Pei, J., & Shi, L. (2018). Training deep neural networks with discrete state transition. Neurocomputing, 272, 154-162
Shi, L. (2018, November). Brain inspired computing devices, chips and system. In 2018 Asia-Pacific Magnetic Recording Conference (APMRC) (pp. 1-1). IEEE.
Wu, S., Li, G., Chen, F., & Shi, L. (2018). Training and inference with integers in deep neural networks. arXiv preprint arXiv:1802.04680.