Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes

Author:

Stöckl ChristophORCID,Maass WolfgangORCID

Funder

Human Brain Project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference38 articles.

1. García-Martín, E., Rodrigues, C. F., Riley, G. & Grahn, H. Estimation of energy consumption in machine learning. J. Parallel Distrib. Comput. 134, 75–88 (2019).

2. Ling, J. Power of a human brain. The Physics Factbook https://hypertextbook.com/facts/2001/JacquelineLing.shtml (2001).

3. Bellec, G. et al. A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11, 3625 (2020).

4. Rueckauer, B., Lungu, I. A., Hu, Y., Pfeiffer, M. & Liu, S. C. Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682 (2017).

5. Tan, M. & Le, Q. EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 6105–6114 (2019).

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