Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks-Reference-Cited by-同舟云学术

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Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

Published:2021-10 Issue:10 Volume:3 Page:905-913
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Yin Bojian^ORCID,Corradi Federico^ORCID,Bohté Sander M.^ORCID

Funder

BY is funded by the NWO-TTW Programme “Efficient Deep Learning” (EDL) P16-25.

Publisher

Springer Science and Business Media LLC

Subject

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

Link

https://www.nature.com/articles/s42256-021-00397-w.pdf

Reference50 articles.

1. Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

2. Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659–1671 (1997).

3. Gerstner, W., Kempter, R., Van Hemmen, J. L. & Wagner, H. A neuronal learning rule for sub-millisecond temporal coding. Nature 383, 76–78 (1996).

4. Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

5. Bohte, S. M., Kok, J. N. & La Poutré, J. A. SpikeProp: backpropagation for networks of spiking neurons. In European Symposium on Artificial Neural Networks (ESANN) Vol. 48, 17–37 (ESANN, 2000).

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