[1] Y. Chai. In-sensor computing for machine vision. Nature, 579, 32-33(2020).
[2] S. Dhawan. A review of image compression and comparison of its algorithms. Int. J. Electron. Commun. Technol., 2, 22-26(2011).
[3] F. Mentzer et al. High-fidelity generative image compression, 11913-11924(2020).
[7] M. F. Bear et al. Synaptic plasticity: LTP and LTD. Curr. Opin. Neurobiol., 4, 389-399(1994).
[8] T. Hosoya et al. Dynamic predictive coding by the retina. Nature, 436, 71-77(2005).
[14] Z. Du et al. ShiDianNao: shifting vision processing closer to the sensor, 92-104(2015).
[16] L. M. Chalupa, P. Sterling, J. S. Werner. How retinal circuits optimize the transfer of visual information. The Visual Neurosciences, 234-259(2004).
[20] M. Kiselev. Rate coding vs. temporal coding:is optimum between?, 1355-1359(2016).
[22] J. Benda. Neural adaptation. Curr. Opin. Neurobiol., 31, R110-R116(2021).
[26] N. Waltham. CCD and CMOS Sensors(2013).
[35] W. Pan et al. A future perspective on in-sensor computing. Engineering, 14, 19-21(2022).
[47] T. Tuma et al. Stochastic phase-change neurons. Nat. Nanotechnol., 11, 693-699(2016).
[52] T. Delbruck et al. Utility and feasibility of a center surround event camera, 381-385(2022).
[63] F. Zhou et al. Near-sensor and in-sensor computing. Nat. Electron., 3, 664-671(2020).