[1] Bi, G.q., Poo, M.m.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. Journal of neuroscience 18(24), 10464-10472 (1998)
[2] Bohte, S.M.: Error-backpropagation in networks of fractionally predictive spiking neurons. In: International Conference on Artificial Neural Networks. pp. 60-68. Springer (2011)
[3] Bu, T., Ding, J., Yu, Z., Huang, T.: Optimized potential initialization for low-latency spiking neural networks (2022)
[4] Carnevale, N.T., Hines, M.L.: The NEURON book. Cambridge University Press (2006)
[5] Chen, Y., Zhang, S., Ren, S., Qu, H.: Gradual surrogate gradient learning in deep spiking neural networks. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 8927-8931 (2022). https://doi.org/10.1109/ICASSP43922.2022.9746774
[6] Cheng, X., Hao, Y., Xu, J., Xu, B.: Lisnn: Improving spiking neural networks with lateral interactions for robust object recognition. In: IJCAI. pp. 1519-1525 (2020)
[7] Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 3075-3084 (2019)
[8] Deng, S., Gu, S.: Optimal conversion of conventional artificial neural networks to spiking neural networks. arXiv preprint arXiv:2103.00476 (2021)
[9] Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Frontiers in computational neuroscience 9, 99 (2015)
[10] Ding, J., Yu, Z., Tian, Y., Huang, T.: Optimal ann-snn conversion for fast and accurate inference in deep spiking neural networks. arXiv preprint arXiv:2105.11654 (2021)
[11] Fang, W., Yu, Z., Chen, Y., Masquelier, T., Huang, T., Tian, Y.: Incorporating learnable membrane time constant to enhance learning of spiking neural networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2661-2671 (2021)
[12] Ghosh-Dastidar, S., Adeli, H.: Spiking neural networks. International journal of neural systems 19(04), 295-308 (2009)
[13] Guo, Y., Chen, Y.: Neuroclip: Neuromorphic data understanding by clip and snn. arXiv preprint arXiv:2306.12073 (2023)
[14] Guo, Y., Chen, Y., Zhang, L., Liu, X., Wang, Y., Huang, X., Ma, Z.: IM-loss: Information maximization loss for spiking neural networks. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022), https://openreview.net/forum?id=Jw34v_84m2b
[15] Guo, Y., Chen, Y., Zhang, L., Wang, Y., Liu, X., Tong, X., Ou, Y., Huang, X., Ma, Z.: Reducing information loss for spiking neural networks. In: Avidan, S., Brostow, G., Cisse, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. pp. 36–52. Springer Nature Switzerland, Cham (2022)
[16] Guo, Y., Huang, X., Ma, Z.: Direct learning-based deep spiking neural networks: a review. Frontiers in Neuroscience 17, 1209795 (2023)
[17] Guo, Y., Liu, X., Chen, Y., Zhang, L., Peng, W., Zhang, Y., Huang, X., Ma, Z.: Rmp-loss: Regularizing membrane potential distribution for spiking neural networks. arXiv preprint arXiv:2308.06787 (2023)