Multi-neuron Information Fusion for Direct Training Spiking Neural Networks
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8126-7_36
Reference22 articles.
1. Roy, K., Jaiswal, A., Panda, P.: Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784), 607–617 (2019)
2. Stromatias, E., Neil, D., Pfeiffer, M., Galluppi, F., Furber, S.B., Liu, S.C.: Robustness of spiking deep belief networks to noise and reduced bit precision of neuro-inspired hardware platforms. Front. Neurosci. 9, 222 (2015)
3. 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. IEEE (2022)
4. Sengupta, A., Ye, Y., Wang, R., Liu, C., Roy, K.: Going deeper in spiking neural networks: VGG and residual architectures. Front. Neurosci. 13, 95 (2019)
5. Yan, Z., Zhou, J., Wong, W.F.: Near lossless transfer learning for spiking neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 10577–10584 (2021)
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