Publications
Xu, Q., Liu, S., Ran, X., Li, Y., Shen, J., Tang, H., Liu, J. K., Pan, G., Zhang, Q., Robust Sensory Information Reconstruction and Classification with Augmented Spikes, IEEE Transactions on Neural Networks and Learning Systems. (2024) [PDF] DOI: 10.1109/TNNLS.2024.3404021
Tang, Y., Guo, S., Liu, J., Wan, B., An, L., Liu, J. K., Hierarchical reinforcement learning from imperfect demonstrations through reachable coverage-based subgoal filtering, Knowledge-Based Systems 294, 111736 (2024) [PDF] DOI: 10.1016/j.knosys.2024.111736
Chen, Y., Feng, R., Xiong, Z., Xiao, J., Liu, J. K., High-performance deep spiking neural networks via at-most-two-spike exponential coding, Neural Netw 176, 106346 (2024) [PDF] DOI: 10.1016/j.neunet.2024.106346
Zhang, X., Lian, J., Yu, Z., Tang, H., Liang, D., Liu, J., Liu, J. K., Revealing the Mechanisms of Semantic Satiation with Deep Learning Models, Commun Biol 7, 487 (2024) [PDF] DOI: 10.1038/s42003-024-06162-0
Hong, R., Zheng, T., Marra, V., Yang, D., Liu, J. K., Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration, J. Neural Eng. 21, 021002 (2024) [PDF] DOI: 10.1088/1741-2552/ad3eb4
Peng, X., Ouyang, C., Liu, Y., Yu, Y., Liu, J., Chen, M., Multimodal Drug Target Binding Affinity Prediction using Graph Local Substructure, IEEE Journal of Biomedical and Health Informatics (2024) [PDF] DOI: 10.1109/JBHI.2024.3386815
Ding, J., Yu, Z., Huang, T., Liu, J. K., Enhancing the robustness of spiking neural networks with stochastic gating mechanisms, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2024) [PDF] DOI: 10.1609/aaai.v38i1.27804
Yu, Z., Bu, T., Zhang, Y., Jia, S., Huang, T., Liu, J. K., Robust decoding of rich dynamical visual scenes with retinal spikes. IEEE Transactions on Neural Networks and Learning Systems. (2024) [PDF] DOI: 10.1109/TNNLS.2024.3351120
Li, Y., Fang, X., Gao, Y., Zhou, D., Shen, J., Liu, J. K., Pan, G., Xu, Q., Efficient Structure Slimming for Spiking Neural Networks, IEEE Transactions on Artificial Intelligence Online (2024) [PDF] DOI: 10.1109/TAI.2024.3352533
Li, H., Wan, B., Li, Q., Fang, Y., Liu, J. K., An, L., An FPGA Implementation of Bayesian Inference with Spiking Neural Networks. Frontiers in Neuroscience 17:1291051 (2024) [PDF] DOI: 10.3389/fnins.2023.1291051
An, L., Yan, Z., Wang, W., Liu J. K., Yu K., Enhancing Visual Coding Through Collaborative Perception. IEEE Transactions Cognitive and Developmental Systems. 15(4) 1744-1753 (2023) [PDF] DOI: 10.1109/TCDS.2022.3203422
Yang, R., Zhao, P., Wang, L., Feng, C., Peng, C., Wang, Z., Zhang, Y., Shen, M., Shi, K., Weng, S., Dong, C., Zeng, F., Zhang, T., Chen, X., Wang, S., Wang, Y., Luo, Y., Chen, Q., Chen, Y., Jiang, C., Jia, S., Yu, Z., Liu, J., Wang, F., Jiang, S., Xu, W., Li, L., Wang, G., Mo, X., Zheng, G., Chen, A., Zhou, X., Jiang, C., Yuan, Y., Yan B., Zhang, J. Assessment of visual function in blind mice and monkeys with subretinally implanted nanowire arrays as artificial photoreceptors. Nature Biomedical Engineering (2023) [PDF] DOI: 10.1038/s41551-023-01137-8
Chen, L., Wu, R., Zhou, F., Zhang, H., Liu, J. K., HybridGCN for Protein Solubility Prediction with Adaptive Weighting of Multiple Features. Journal of Cheminformatics 15, 118 (2023) [PDF] DOI: 10.1186/s13321-023-00788-8
Jia, S., Liu, J.K., Yu, Z., Protocol for dissecting cascade computational components in neural networks of a visual system. STAR Protocols 4:102722 (2023) [PDF] DOI: 10.1016/j.xpro.2023.102722
An, L., Yuan, Y., Liu, Y., Zhao, F., Wang, Q., Liu J. K., Flexible Learning Models Utilizing Different Neural Plasticities. IEEE Transactions Cognitive and Developmental Systems. 15(3), 1150-1160 (2023) [PDF] DOI: 10.1109/TCDS.2022.3197463
Shen, J., Zhao, Y., Liu, J. K., Wang, Y., HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 34(9), 5841-5855 (2023) [PDF] DOI: 10.1109/TNNLS.2021.3131356
Xu, Q., Li, Y., Shen, J., Liu, J.K., Tang, H., Pan, G., Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7886-7895 (2023) [PDF] DOI: 10.1109/CVPR52729.2023.00762
Shen, J., Xu, Q., Liu, J.K., Wang, Y., Pan, G., Tang, H., ESL-SNNs: An Evolutionary Structure Learning Strategy For Spiking Neural Networks, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 37(1), 86-93 (2023) [PDF] [Oral] DOI: 10.1609/aaai.v37i1.25079
Zhou Q., Du C., Li D., Wang H., Liu J.K., He H., Neural Encoding and Decoding with a Flow-based Invertible Generative Model. IEEE Transactions Cognitive and Developmental Systems. 15(2):724-736 (2023) [PDF] DOI: 10.1109/TCDS.2022.3176977
Yang, W., Liu, J., Cao, P., Zhu, R., Wang, Y., Liu J.K., Wang, F., Zhang X., Attention guided learnable time-domain filterbanks for speech depression detection. Neural Networks. 165: 135-149 (2023) [PDF] DOI: 10.1016/j.neunet.2023.05.041
Shi, G., Zhu, Y., Liu, J. K., Li, X., HeGCL: Advance Self-Supervised Learning in Heterogeneous Graph-Level Representation. IEEE Transactions on Neural Networks and Learning Systems. (2023) [PDF] DOI: 10.1109/TNNLS.2023.3273255
Li, M., Peng, B. Liu, J. K., Zhai, D. RBNet: An Ultra Fast Rendering-based Architecture for Railway Defects Segmentation, IEEE IEEE Transactions on Instrumentation and Measurement 72 :1-8 (2023) [PDF] DOI: 10.1109/TIM.2023.3269107
Tang Y., Zhang X., An L., Yu Z., Liu, J. K., Diverse role of NMDA receptors for dendritic integration of neural dynamics, PLoS Comput Biol (2023) 19(4): e1011019 (2023) [PDF] [Code] DOI: 10.1371/journal.pcbi.1011019
Ding, J., Bu, T., Yu, Z., Huang, T., Liu, J.K., SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training, Advances in Neural Information Processing Systems (NeurIPS) (2022) [Spotlight] [PDF] [Poster] [Code] [Metapage]
Xu, Q., Li, Y., Shen, J., Zhang, P., Liu, J. K., Tang, H., Pan, G., Hierarchical Spiking-Based Model for Efficient Image Classification With Enhanced Feature Extraction and Encoding. IEEE Transactions on Neural Networks and Learning Systems. (2022) [PDF] DOI: 10.1109/TNNLS.2022.3232106
Yang, Z., Guo, S., Fang, Y., Liu, J.K. Biologically Plausible Variational Policy Gradient with Spiking Recurrent Winner-Take-All Networks, British Machine Vision Conference (BMVC) (2022) [PDF] [Poster] [Video] [Code] [Metapage] DOI: 10.48550/arXiv.2210.13225
Han, C., Wang, T., Wu, Y., Li, H., Wang, E., Zhao, X., Cao, Q., Qian, Q., Wang, Y., Dou, F., Liu, J. K., Sun, L., Xing, D., Compensatory mechanism of attention-deficit/hyperactivity disorder recovery in resting state alpha rhythms, Frontier in Computational Neuroscience 16:883065 (2022) [PDF] DOI: 10.3389/fncom.2022.883065
Zhang, Y., Yu, Z., Liu, J. K., Huang T., Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches. Machine Intelligence Research. 19: 350-365 (2022) [PDF] DOI: 10.1007/s11633-022-1335-2
Jia S, Yu Z, Onken A, Tian Y, Huang T, Liu J. K. Neural System Identification with Spike-triggered Non-negative Matrix Factorization. IEEE Transactions on Cybernetics 52(6): 4772-4783 (2022) [PDF] [Code for STNMF] [Code for Copula] [Data] DOI: 10.1109/TCYB.2020.3042513
Xu, Q., Shen, J., Ran, X., Tang, H., Pan, G., Liu, J. K., Robust Transcoding Sensory Information With Neural Spikes. IEEE Transactions on Neural Networks and Learning Systems. 33 (5): 1935–1946 (2022) [PDF] DOI: 10.1109/TNNLS.2021.3107449
Zhang, Y., Bu, T., Zhang, Y., Tang, S., Yu, Z., Liu, J. K. Huang, T., Decoding Pixel-Level Image Features from Two-Photon Calcium Signals of Macaque Visual Cortex. Neural Computation 34 (6): 1369–1397 (2022) [PDF] DOI: 10.1162/neco_a_01498
Liu, J. K., Karamanlis, D., Gollisch, T., Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration. PLoS Comput Biol 18(3): e1009925 (2022) [PDF] [Data] DOI: 10.1371/journal.pcbi.1009925
Yan Q, Zheng Y, Jia S, Zhang Y, Yu Z, Chen F, Tian Y, Huang T, Liu J.K., Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks. IEEE Transactions on Cybernetics 52(1) :39-50 (2022) [PDF] [Code] [Data] DOI: 10.1109/TCYB.2020.2972983
Jia, S., Li, X., Huang, T., Liu, J. K., Zhao, Y., Representing the dynamics of high-dimensional data with non-redundant wavelets. Patterns. 3(3): 100424 (2022) [PDF] [Code] DOI: 10.1016/j.patter.2021.100424
Jia S., Xing D., Yu Z., Liu, J. K., Dissecting cascade computational components in spiking neural networks, PLoS Comput Biol 17(11): e1009640 (2021) [PDF] [Code] DOI: 10.1371/journal.pcbi.1009640
Zheng Y, Jia S, Yu Z, Liu, J.K, Huang T. Unraveling neural coding of dynamic natural visual scenes via convolutional recurrent neural networks, Patterns 2(10): 100350 (2021) [PDF] [Code] DOI: 10.1016/j.patter.2021.100350
Shen J., Liu, J.K, Wang, Y. Dynamic Spatiotemporal Pattern Recognition with Recurrent Spiking Neural Network, Neural Computation 33(11): 2971-2995 (2021) [PDF] DOI: 10.1162/neco_a_01432
Tang Y., An L., Wang Q., Liu, J. K., Regulating synchronous oscillations of cerebellar granule cells by different types of inhibition, PLoS Comput Biol 17(6): e1009163 (2021) [PDF] [Code] DOI: 10.1371/journal.pcbi.1009163
Tang Y., An L., Yuan Y., Pei Q, Wang Q., Liu, J. K., Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways, PLoS Comput Biol 17(2): e1008670 (2021) [PDF] [Code] DOI: 10.1371/journal.pcbi.1008670
Yu Z, Chen F, Liu J.K., Sampling-Tree Model: Efficient Implementation of Distributed Bayesian Inference in Neural Networks. IEEE Transactions Cognitive and Developmental Systems. 12: 497-510 (2020) [PDF] DOI: 10.1109/TCDS.2019.2927808
Wang M., Liao X., Li R., Liang S., Ding R., Li R., Zhang J., He W., Liu K., Pan J., Zhao Z., Li T., Zhang K., Li X., Lyu J., Zhou Z., Varga Z., Mi Y., Zhou Y., Yan J., Zeng S., Liu, J. K., Konnerth A., Nelken I., Jia H., Chen X. Single-neuron representation of learned complex sounds in the auditory cortex, Nature Communications, 11, 4361 (2020). [PDF] DOI:10.1038/s41467-020-18142-z
Zhou Q, Du C, Li D, Wang H, Liu JK, He H. Simultaneous Neural Spike Encoding and Decoding Based on Cross-modal Dual Deep Generative Model. 2020 International Joint Conference on Neural Networks (IJCNN) [PDF] DOI: 10.1109/IJCNN48605.2020.9207466
Shen J, Zhao Y, Liu JK, Wang Y, Recognizing Scoring in Basketball Game from AER Sequence by Spiking Neural Networks.. 2020 International Joint Conference on Neural Networks (IJCNN) [PDF] DOI: 10.1109/IJCNN48605.2020.9207568
Yu, Z., Liu, J. K., Jia, S., Zhang, Y., Zheng, J., Tian, Y., and Huang, T., Toward the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes. Engineering. 6:449-461 (2020) [PDF] DOI: 10.1016/j.eng.2020.02.004
Zheng Y, Jia S, Yu Z, Huang T, Liu J.K., Tian Y. Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation. Neural Networks. 126:42-51 (2020) [PDF] DOI: 10.1016/j.neunet.2020.03.003
An, L., Tang, Y., Wang D., Jia S., Pei, Q., Wang, Q., Yu Z., and Liu, J. K., Intrinsic and Synaptic Properties Shaping Diverse Behaviors of Neural Dynamics, Frontier in Computational Neuroscience 14:26 (2020) [PDF] DOI: 10.3389/fncom.2020.00026
Zhang Y, Jia S, Zheng Y, Yu Z, Tian Y., Ma S., Huang T, Liu J.K. Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks. Neural Networks. 125:19-30 (2020) [PDF] [Demos] [Code] [Data] DOI: 10.1016/j.neunet.2020.01.033
Yu Z, Guo S, Deng F, Yan Q, Huang K, Liu J.K., Chen F, Emergent Inference of Hidden Markov Models in Spiking Neural Networks Through Winner-Take-All. IEEE Transactions on Cybernetics. 50:1347-1354 (2020) [PDF] DOI: 10.1109/TCYB.2018.2871144
Yang M, Zhou Z, Zhang J, Jia S, Li T, Guan J, Liao X, Leng B, Lyu J, Zhang K, Li M, Gong Y, Zhu Z, Yan J, Zhou Y, Liu J. K., Varga Z., Konnerth A., Tang Y., Gao J., Chen X., Jia H., MATRIEX imaging: multiarea two-photon real-time in vivo explorer. Light: Science & Applications, 8:109 (2019) [PDF] DOI: 10.1038/s41377-019-0219-x
Zhao X, She X, Yang B, Gao Q, Yu Z, Liu J.K., Tian Y, Huang T, Skeleton-based 3D Object Retrieval using Retina-like Feature Descriptor. IEEE Access, 7:157341-157352 (2019) [PDF] DOI: 10.1109/ACCESS.2019.2944307
Fang, Y., Yu, Z., Liu, J. K., Chen, F., A unified neural circuit of causal inference and multisensory integration. Neurocomputing, 358:355–368 (2019). [PDF] DOI: 10.1016/j.neucom.2019.05.067
An, L., Tang, Y., Wang, Q., Pei, Q., Wei, R., Duan, H., and Liu, J. K., Coding Capacity of Purkinje Cells with Different Schemes of Morphological Reduction, Frontier in Computational Neuroscience 13:29 (2019) [PDF] [Code] DOI: 10.3389/fncom.2019.00029
Das, G. P., Vance, P. J., Kerr, D., Coleman, S. A., Mcginnity, T. M., Liu, J. K., Computational modelling of salamander retinal ganglion cells using machine learning approaches, Neurocomputing, 325:101-112 (2019). [PDF] DOI: 10.1016/j.neucom.2018.10.004
Yu Z, Tian Y, Huang T, Liu J.K. Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits. arXiv:1808.00675 [PDF].
Yue Y., He L., He G., Liu J.K., Du K., Tian Y., Huang T. A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina. arXiv:1806.05882 [PDF].
Yu Z., Huang T., Liu, J. K., Implementation of Bayesian inference in distributed neural networks. Proc. of 26th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP2018), Cambridge (UK) 21-23 March 2018, pp. 666-673, IEEE Comp. Society Press. [PDF] DOI: 10.1109/PDP2018.2018.00111
Vance, P., Das, G., Kerr, D., Coleman, S., McGinnity, T., Gollisch, T., Liu, J. K., Bio-Inspired Approach to Modelling Retinal Ganglion Cells using System Identification Techniques. IEEE Transactions on Neural Networks and Learning Systems. 29(5):1796-1808 (2018) [PDF] DOI: 10.1109/TNNLS.2017.2690139
Yan Q., Yu, Z., Chen F., and Liu, J. K., Revealing structure components of the retina by deep learning networks. NIPS Symposium on Interpretable Machine Learning, 2017. arXiv:1711.02837. [PDF] DOI: 10.1101/216010
Liu, J. K., Schreyer, H.M., Onken, A., Rozenblit, F., Khani, M.H., Krishnamoorthy, V., Panzeri, S., and Gollisch, T. Inference of neuronal functional circuitry with spike-triggered non-negative matrix factorization, Nature Communications, 8(1):149 (2017). [PDF] [Code] [Data] [Striking Image] [Press release in German] [Selected as Gö-VIP (Göttingen Very Important Publication)] DOI:10.1038/s41467-017-00156-9
Onken A., Liu J. K., Karunasekara C. R., Delis I., Gollisch T. and Panzeri S. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains. PLoS Comput Biol 12(11): e1005189 (2016) [PDF] [Code] [Data] DOI: 10.1371/journal.pcbi.1005189
Zampini, V.*, Liu, J. K.*, Diana, M.A., Maldonado, P.P., Brunel, N., Dieudonné, S. (*These authors contributed equally to this work), Mechanisms and functional roles of glutamatergic synapse diversity in a cerebellar circuit. eLife, 5:e15872 (2016) [PDF] [Code] DOI: 10.7554/eLife.15872
Liu, J. K., Gollisch, T., Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina, PLoS Comput Biol 11(7): e1004425 (2015). [PDF] [Data] [Striking Image] (DOI: 10.1371/journal.pcbi.1004425)
Liu, J. K., Learning Rule of Homeostatic Synaptic Scaling: Presynaptic Dependent or Not, Neural Computation, 23(12):3145-3161. (2011) [PDF] DOI: 10.1162/NECO_a_00210
Liu, J. K., Buonomano, D. V., Embedding Multiple Trajectories in Simulated Recurrent Neural Networks in a Self-Organizing Manner, J. Neuroscience, 29(42):13172-13181. (2009) [PDF] [Suppl. Movie] DOI: 10.1523/JNEUROSCI.2358-09.2009
Liu, J. K., She, Z.-S., A Spike-Timing Pattern Based Neural Network Model for the Study of Memory Dynamics, PLoS ONE 4(7): e6247. (2009) [PDF]
Jiang, X.-Q., Gong, H., Liu, J.K., Zhou M.-D. & She, Z.-S., Hierarchical structures in a free shear flow, J. Fluid Mech., 569, 259 (2006) [PDF]
Ouyang, Z., Liu, J.K. & She, Z.-S. Hierarchical structure analysis describing abnormal base composition of genomes, Phys. Rev. E., 72(4), 041915 (2005) [PDF]
Liu, J., She, Z.-S., Guo, H.Y., Li, L. and Ouyang, Q., Hierarchical structure description of spatiotemporal chaos, Phys. Rev. E., 70(3), 036215 (2004) [PDF]
Liu, J. and She, Z.-S., A simple phenomenology for the evolution of spatiotemporal chaos, Proceeding of 7th National Conference of Turbulent and Fluid Instability, 166-177 (2004) [PDF]
Liu, J., She, Z.-S., Ouyang, Q. and He, X.-T., Hierarchical structures in spatially extended systems, Inter J. Modern Phys. B., 17, 4139-4148 (2003) [PDF]
Guo, H., Li, L., Liu, J., She, Z.-S. and Ouyang, Q., A systematic study of spirals and spiral turbulence in a reaction-diffusion system, J. Chem. Phys., 118, 5038-5044 (2003) [PDF]