References
[1] Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., & Fergus, R. (2013, February). Regularization of neural networks using dropconnect. In International conference on machine learning (pp. 1058-1066).
[2] Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., & Houlsby, N. (2019). Big transfer (bit): General visual representation learning. arXiv preprint arXiv:1912.11370, 6, 3.
[3] Bouvier, M., Valentian, A., Mesquida, T., Rummens, F., Reyboz, M., Vianello, E., & Beigne, E. (2019). Spiking neural networks hardware implementations and challenges: A survey. ACM Journal on Emerging Technologies in Computing Systems (JETC), 15(2), 1-35.
[4] Deng, L., Wu, Y., Hu, X., Liang, L., Ding, Y., Li, G., … & Xie, Y. (2020). Rethinking the performance comparison between SNNS and ANNS. Neural Networks, 121, 294-307.
[5] Hazan, H., Saunders, D. J., Khan, H., Patel, D., Sanghavi, D. T., Siegelmann, H. T., & Kozma, R. (2018). Bindsnet: A machine learning-oriented spiking neural networks library in python. Frontiers in neuroinformatics, 12, 89.
[6] Yann LeCun, Corinna Cortes, and CJ Burges. “MNIST handwritten digit database”. In:AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist2 (2010). url:http://yann.lecun.com/exdb/mnist/.
[7] Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.
[8] Orchard, G., Jayawant, A., Cohen, G. K., & Thakor, N. (2015). Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience, 9, 437.
[9] Li, H., Liu, H., Ji, X., Li, G., & Shi, L. (2017). CIFAR10-DVS: An event-stream dataset for object classification. Frontiers in neuroscience, 11, 309.
[10] Diehl, P. U., & Cook, M. (2015). Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Frontiers in computational neuroscience, 9, 99.