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Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).
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E. O. Neftci, C. Augustine, S. Paul, and G. Detorakis, “Event-driven random back-propagation: Enabling neuromorphic deep learning machines,” Frontiers in Neuroscience , vol. 11, p. 324, 2017
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E. O. Neftci, “Data and power efficient intelligence with neuromorphic learning machines,” iScience , vol. 5, pp. 52–68, 2018. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2589004218300865
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M. Davies, N. Srinivasa, T. H. Lin, G. Chinya, P. Joshi, A. Lines, A. Wild, and H. Wang, “Loihi: A neuromorphic manycore processor with on-chip learning,” IEEE Micro , vol. PP, no. 99, pp. 1–1, 2018
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G. Detorakis, S. Sheik, C. Augustine, S. Paul, B. U. Pedroni, N. Dutt, J. Krichmar, G. Cauwenberghs, and E. Neftci, “Neural and synaptic array transceiver: A brain-inspired computing framework for embedded learning,” Frontiers in Neuroscience , vol. 12, p. 583, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fnins.2018.00583
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A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza et al. , “A low power, fully event-based gesture recognition system,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7243–7252
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