A vision processing system that leverages reconfigurable memristive devices to implement different bioinspired neural networks can efficiently sense and process static and dynamic visual information.
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References
Hosoya, T. et al. Nature 436, 71–77 (2005).
Berry, M. J. et al. Proc. Natl Acad. Sci. USA 94, 5411–5416 (1997).
Gu, L. et al. Nature 581, 278–282 (2020).
Chen, J. et al. Nat. Nanotechnol. 18, 882–888 (2023).
Mead, C. A. et al. Neural Netw. 1, 91–97 (1988).
Mennel, L. et al. Nature 579, 62–66 (2020).
Tan, H. & van Dijken, S. Nat. Commun. 14, 2169 (2023).
Zhou, Y. et al. Nat. Electron. 6, 870–878 (2023).
Dang, B. et al. Nat. Electron. https://doi.org/10.1038/s41928-024-01280-3 (2024).
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
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Tan, H., van Dijken, S. A universal neuromorphic vision processing system. Nat Electron 7, 946–947 (2024). https://doi.org/10.1038/s41928-024-01288-9
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DOI: https://doi.org/10.1038/s41928-024-01288-9
