[27] Wolf Singer. Neuronal synchronization: A solution to the binding problem. The mind-brain continuum: Sensory processes, pages 101-130, 1996.
[28] Sen Song, Kenneth D. Miller, and L. F. Abbott. Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience, 3:919-926, 2000.
[29] Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, R. Howard, Wayne Hubbard, and Lawrence Jackel. Handwritten digit recognition with a back-propagation network. In D. Touretzky, editor, Advances in Neural Information Processing Systems, volume 2. Morgan-Kaufmann, 1989.
[30] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096-1103, 2008.
[31] Sven Behnke. Learning iterative image reconstruction in the neural abstraction pyramid. International Journal of Computational Intelligence and Applications, 1(04):427-438, 2001.
[32] Andreas K Engel, Pascal Fries, and Wolf Singer. Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews Neuroscience, 2(10):704-716, 2001.
[33] Jonathan D Victor and Keith P Purpura. Nature and precision of temporal coding in visual cortex: a metric-space analysis. Journal of neurophysiology, 76(2):1310-1326, 1996.
[34] Antonio R Damasio. The brain binds entities and events by multiregional activation from convergence zones. Neural computation, 1(1):123-132, 1989.
[35] Antonio R Damasio. Synchronous activation in multiple cortical regions: a mechanism for recall. In Seminars in the Neurosciences, volume 2, 1990.
[36] Lars Buesing, Johannes Bill, Bernhard Nessler, and Wolfgang Maass. Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7, 2011.
[37] Hao Zheng, Hui Lin, Rong Zhao, and Luping Shi. Dance of snn and ann: Solving binding problem by combining spike timing and reconstructive attention. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 31430-31443. Curran Associates, Inc., 2022.
[38] Naftali Tishby and Noga Zaslavsky. Deep learning and the Information Bottleneck principle. In IEEE Information Theory Workshop (ITW), 2015.
[39] Seol-Hyun Noh. Analysis of gradient vanishing of rnns and performance comparison. Inf., 12:442, 2021.
[40] Emre O. Neftci, Hesham Mostafa, and Friedemann Zenke. Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Processing Magazine, 36:51-63, 2019.
[41] Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi. Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in Neuroscience, 12, 2018.
[42] Leonard Kaufman and Peter J. Rousseeuw. Clustering by means of medoids. 1987.
[43] Nguyen Xuan Vinh, Julien Epps, and James Bailey. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. The Journal of Machine Learning Research, 11:2837-2854, 2010.
[44] Peter J Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53-65, 1987.
[45] David P. Reichert, Peggy Series, and Amos J. Storkey. A hierarchical generative model of recurrent object-based attention in the visual cortex. In ICANN, 2011.