Pinto, L. et al. Activity-dependent modifications within the large-scale dynamics and necessity of cortical areas. Neuron 104, 810–824.e819 (2019).
Juusola, M., French, A. S., Uusitalo, R. O. & Weckström, M. Info processing by graded-potential transmission by means of tonically energetic synapses. Tendencies Neurosci. 19, 292–297 (1996).
Joselevitch, C. Human retinal circuitry and physiology. Psychol. Neurosci. 1, 141–165 (2008).
Haag, J. & Borst, A. Encoding of visible movement data and reliability in spiking and graded potential neurons. J. Neurosci. 17, 4809–4819 (1997).
Pei, J. et al. In direction of synthetic basic intelligence with hybrid Tianjic chip structure. Nature 572, 106–111 (2019).
Zhou, F. et al. Optoelectronic resistive random entry reminiscence for neuromorphic imaginative and prescient sensors. Nat. Nanotechnol. 14, 776–782 (2019).
Huang, H. Absolutely built-in multi-mode optoelectronic memristor array for diversified in-sensor computing. Nat. Nanotechnol. 20, 93–103 (2025).
Kumar, S., Williams, R. S. & Wang, Z. Third-order nanocircuit parts for neuromorphic engineering. Nature 585, 518–523 (2020).
Mahmoud, S. A. A brand new current-mode analog multiplier circuit. In Worldwide Midwest Symposium on Circuits and Programs 130–133 (IEEE, 2009).
Låte, E., Vatanjou, A. A., Ytterdal, T. & Aunet, S. Comparative evaluation of flip-flop architectures for subthreshold functions in 28 nm FDSOI. In Nordic Circuits and Programs Convention: NORCHIP & Worldwide Symposium on System-on-Chip 1–4 (IEEE, 2015).
Chen, X. et al. CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks. Appl. Phys. Lett. 122, 053502 (2023).
Nguyen, V. T., Trinh, Q. Ok., Zhang, R. & Nakashima, Y. STT-BSNN: an in-memory deep binary spiking neural community based mostly on STT-MRAM. IEEE Entry 9, 151373–151385 (2021).
Park, J. H., Tan, J. S. Y., Wu, H., Dong, Y. & Yoo, J. 1225-channel neuromorphic retinal-prosthesis SoC with localized temperature-regulation. IEEE Trans. Biomed. Circuits Syst. 14, 1230–1240 (2020).
Indiveri, G., Chicca, E. & Douglas, R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17, 211–221 (2006).
Han, J.-Ok. et al. 3D stackable broadband photoresponsive InGaAs biristor neuron for a neuromorphic visible system with close to 1 V operation. In Worldwide Electron Gadgets Assembly 1–4 (IEEE, 2021).
Wang, X. et al. Vertically built-in spiking cone photoreceptor arrays for coloration notion. Nat. Commun. 14, 3444 (2023).
Mennel, L. et al. Ultrafast machine imaginative and prescient with 2D materials neural community picture sensors. Nature 579, 62–66 (2020).
Fu, Y. et al. Reconfigurable synaptic and neuronal capabilities in a V/VOx/HfWOx/Pt memristor for nonpolar spiking convolutional neural community. Adv. Funct. Mater. 32, 2111996 (2022).
Dang, B. et al. Reconfigurable in-sensor processing based mostly on a multi-phototransistor–one-memristor array. Nat. Electron. 7, 991–1003 (2024).
Huang, H. et al. Absolutely built-in multi-mode optoelectronic memristor array for diversified in-sensor computing. Nat. Nanotechnol. 20, 93–103 (2025).
John, R. A. et al. Optogenetics impressed transition steel dichalcogenide neuristors for in-memory deep recurrent neural networks. Nat. Commun. 11, 3211 (2020).
Wu, Q. et al. Spike encoding with optic sensory neurons allow a pulse coupled neural community for ultraviolet picture segmentation. Nano Lett. 20, 8015–8023 (2020).
Chen, J. et al. Optoelectronic graded neurons for bioinspired in-sensor movement notion. Nat. Nanotechnol. 18, 882–888 (2023).
Search engine optimisation, S. et al. Synthetic optic-neural synapse for coloured and color-mixed sample recognition. Nat. Commun. 9, 5106 (2018).
Ahmed, T. et al. Absolutely light-controlled reminiscence and neuromorphic computation in layered black phosphorus. Adv. Mater. 33, e2004207 (2021).
Hou, Y. X. et al. Giant-scale and versatile optical synapses for neuromorphic computing and built-in seen data sensing reminiscence processing. ACS Nano 15, 1497–1508 (2021).
Valov, I. & Tsuruoka, T. Results of moisture and redox reactions in VCM and ECM resistive switching recollections. J. Phys. D: Appl. Phys. 51, 403001 (2018).
Tsuruoka, T. et al. Results of moisture on the switching traits of oxide‐based mostly, gapless‐kind atomic switches. Adv. Funct. Mater. 22, 70–77 (2011).
Milano, G. et al. Water-mediated ionic migration in memristive nanowires with a tunable resistive switching mechanism. ACS Appl. Mater. Interfaces 12, 48773–48780 (2020).
Milano, G. et al. Ionic modulation {of electrical} conductivity of ZnO as a consequence of ambient moisture. Adv. Mater. Interfaces 6, 1900803 (2019).
Duan, T., Wang, W., Cai, S. & Zhou, Y. On-chip light-incorporated in situ transmission electron microscopy of steel halide perovskite supplies. ACS Vitality Lett. 8, 3048–3053 (2023).
Cai, S. et al. Growth of in situ optical-electrical MEMS platform for semiconductor characterization. Ultramicroscopy 194, 57–63 (2018).
Tan, H., Verbeeck, J., Abakumov, A. & Van Tendeloo, G. Oxidation state and chemical shift investigation in transition steel oxides by EELS. Ultramicroscopy 116, 24–33 (2012).
Lübben, M., Wiefels, S., Waser, R. & Valov, I. Processes and results of oxygen and moisture in resistively switching TaOx and HfOx. Adv. Electron. Mater. 4, 1700458 (2017).
Cho, D. Y., Luebben, M., Wiefels, S., Lee, Ok. S. & Valov, I. Interfacial metal-oxide interactions in resistive switching recollections. ACS Appl. Mater. Interfaces 9, 19287–19295 (2017).
Dudek, P. et al. Sensor-level laptop imaginative and prescient with pixel processor arrays for agile robots. Sci. Robotic. 7, eabl7755 (2022).
Zhong, X., Regulation, M.-Ok., Tsui, C.-Y. & Bermak, A. A totally dynamic multi-mode CMOS imaginative and prescient sensor with mixed-signal cooperative movement sensing and object segmentation for adaptive edge computing. IEEE J. Strong-State Circuits 55, 1684–1697 (2020).
