Theoretical Foundations
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65549-4_2
Reference19 articles.
1. S. Srivastava, S.S. Rathod, Silicon neuron-analog CMOS VLSI implementation and analysis at 180 nm, in 2016 3rd International Conference on Devices, Circuits and Systems (ICDCS) (IEEE, 2016, March), pp. 28–32. https://doi.org/10.1109/ICDCSyst.2016.7570617
2. Y. Ding, Y. Zhang, X. Zhang, P. Chen, Z. Zhang, Y. Yang, Q. Liu et al., Engineering spiking neurons using threshold switching devices for high-efficient neuromorphic computing. Front. Neurosci. 15, 786694 (2022). https://doi.org/10.3389/fnins.2021.786694
3. S.A. Aamir, Y. Stradmann, P. Müller, C. Pehle, A. Hartel, A. Grübl, K. Meier et al., An accelerated LIF neuronal network array for a large-scale mixed-signal neuromorphic architecture. IEEE Trans. Circuits Syst. I Regul. Pap. 65(12), 4299–4312 (2018). https://doi.org/10.1109/TCSI.2018.2840718
4. S. Furber, Large-scale neuromorphic computing systems. J. Neural Eng. 13(5), 051001 (2016). https://doi.org/10.1088/1741-2560/13/5/051001
5. A. Basu, L. Deng, C. Frenkel, X. Zhang, Spiking neural network integrated circuits: a review of trends and future directions, in 2022 IEEE Custom Integrated Circuits Conference (CICC) (IEEE, 2022, April), pp. 1–8. https://doi.org/10.1109/CICC53496.2022.9772783
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