Pinto, L. et al. Job-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. Data processing by graded-potential transmission by tonically lively synapses. Traits 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 common 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. Totally 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 components 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 Methods 130–133 (IEEE, 2009).
Låte, E., Vatanjou, A. A., Ytterdal, T. & Aunet, S. Comparative evaluation of flip-flop architectures for subthreshold purposes in 28 nm FDSOI. In Nordic Circuits and Methods 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 primarily based 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 Units 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 features 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 primarily based on a multi-phototransistor–one-memristor array. Nat. Electron. 7, 991–1003 (2024).
Huang, H. et al. Totally 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 metallic 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).
Website positioning, S. et al. Synthetic optic-neural synapse for coloured and color-mixed sample recognition. Nat. Commun. 9, 5106 (2018).
Ahmed, T. et al. Totally light-controlled reminiscence and neuromorphic computation in layered black phosphorus. Adv. Mater. 33, e2004207 (2021).
Hou, Y. X. et al. Massive-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 reminiscences. J. Phys. D: Appl. Phys. 51, 403001 (2018).
Tsuruoka, T. et al. Results of moisture on the switching traits of oxide‐primarily based, 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 result 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 metallic halide perovskite supplies. ACS Power 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 metallic 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 reminiscences. ACS Appl. Mater. Interfaces 9, 19287–19295 (2017).
Dudek, P. et al. Sensor-level pc 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 completely 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).