Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges
Corresponding Author: Yishu Zhang
Nano-Micro Letters,
Vol. 17 (2025), Article Number: 61
Abstract
Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.
Highlights:
1 Biological fundamentals and recent progress of artificial sensory neurons are systematically reviewed.
2 Basic device, performance metrics, and potential applications of artificial sensory neurons are summarized.
3 Challenges for the future development of artificial sensory neurons are discussed.
Keywords
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- H.A. Elmarakeby, J. Hwang, R. Arafeh, J. Crowdis, S. Gang et al., Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021). https://doi.org/10.1038/s41586-021-03922-4
- S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou et al., Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620–627 (2023). https://doi.org/10.1038/s41586-023-05732-2
- Y.-G. Ham, J.-H. Kim, S.-K. Min, D. Kim, T. Li et al., Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622, 301–307 (2023). https://doi.org/10.1038/s41586-023-06474-x
- Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
- N. Ratledge, G. Cadamuro, B. de la Cuesta, M. Stigler, M. Burke, Using machine learning to assess the livelihood impact of electricity access. Nature 611, 491–495 (2022). https://doi.org/10.1038/s41586-022-05322-8
- R. Nair, Evolution of memory architecture. Proc. IEEE 103, 1331–1345 (2015). https://doi.org/10.1109/JPROC.2015.2435018
- S. Yu, Neuro-inspired computing with emerging nonvolatile memorys. Proc. IEEE 106, 260–285 (2018). https://doi.org/10.1109/JPROC.2018.2790840
- X. Zou, S. Xu, X. Chen, L. Yan, Y. Han, Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology. Sci. China Inf. Sci. 64, 160404 (2021). https://doi.org/10.1007/s11432-020-3227-1
- C.D. Wright, P. Hosseini, J.A.V. Diosdado, Beyond von-Neumann computing with nanoscale phase-change memory devices. Adv. Funct. Mater. 23, 2248–2254 (2013). https://doi.org/10.1002/adfm.201202383
- A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou, Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020). https://doi.org/10.1038/s41565-020-0655-z
- W. Wan, R. Kubendran, C. Schaefer, S.B. Eryilmaz, W. Zhang et al., A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022). https://doi.org/10.1038/s41586-022-04992-8
- J.-Q. Yang, R. Wang, Y. Ren, J.-Y. Mao, Z.-P. Wang et al., Neuromorphic engineering: from biological to spike-based hardware nervous systems. Adv. Mater. 32, 2003610 (2020). https://doi.org/10.1002/adma.202003610
- J. Tang, F. Yuan, X. Shen, Z. Wang, M. Rao et al., Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31, e1902761 (2019). https://doi.org/10.1002/adma.201902761
- K. Roy, A. Jaiswal, P. Panda, Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019). https://doi.org/10.1038/s41586-019-1677-2
- D. Kumar, H. Li, D.D. Kumbhar, M.K. Rajbhar, U.K. Das et al., Highly efficient back-end-of-line compatible flexible Si-based optical memristive crossbar array for edge neuromorphic physiological signal processing and bionic machine vision. Nano-Micro Lett. 16, 238 (2024). https://doi.org/10.1007/s40820-024-01456-8
- Y. Sun, H. Wang, D. Xie, Recent advance in synaptic plasticity modulation techniques for neuromorphic applications. Nano-Micro Lett. 16, 211 (2024). https://doi.org/10.1007/s40820-024-01445-x
- K.C. Kwon, J.H. Baek, K. Hong, S.Y. Kim, H.W. Jang, Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano-Micro Lett. 14, 58 (2022). https://doi.org/10.1007/s40820-021-00784-3
- C. Tan, M. Šarlija, N. Kasabov, Spiking neural networks: background, recent development and the NeuCube architecture. Neural. Process. Lett. 52, 1675–1701 (2020). https://doi.org/10.1007/s11063-020-10322-8
- J.L. Lobo, J. Del Ser, A. Bifet, N. Kasabov, Spiking neural networks and online learning: an overview and perspectives. Neural Netw. 121, 88–100 (2020). https://doi.org/10.1016/j.neunet.2019.09.004
- K. He, C. Wang, Y. He, J. Su, X. Chen, Artificial neuron devices. Chem. Rev. 123, 13796–13865 (2023). https://doi.org/10.1021/acs.chemrev.3c00527
- P.A. Merolla, J.V. Arthur, R. Alvarez-Icaza, A.S. Cassidy, J. Sawada et al., A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014). https://doi.org/10.1126/science.1254642
- M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao et al., Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018). https://doi.org/10.1109/MM.2018.112130359
- S. Choi, J. Yang, G. Wang, Emerging memristive artificial synapses and neurons for energy-efficient neuromorphic computing. Adv. Mater. 32, e2004659 (2020). https://doi.org/10.1002/adma.202004659
- G. Lee, J.-H. Baek, F. Ren, S.J. Pearton, G.-H. Lee et al., Artificial neuron and synapse devices based on 2D materials. Small 17, 2100640 (2021). https://doi.org/10.1002/smll.202100640
- N.K. Upadhyay, H. Jiang, Z. Wang, S. Asapu, Q. Xia et al., Emerging memory devices for neuromorphic computing. Adv. Mater. Technol. 4, 1800589 (2019). https://doi.org/10.1002/admt.201800589
- J. Zhu, T. Zhang, Y. Yang, R. Huang, A comprehensive review on emerging artificial neuromorphic devices. Appl. Phys. Rev. 7, 011312 (2020). https://doi.org/10.1063/1.5118217
- Q. Duan, Z. Jing, X. Zou, Y. Wang, K. Yang et al., Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat. Commun. 11, 3399 (2020). https://doi.org/10.1038/s41467-020-17215-3
- Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya et al., Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
- R.H. Walden, Analog-to-digital converter survey and analysis. IEEE J. Sel. Areas Commun. 17, 539–550 (1999). https://doi.org/10.1109/49.761034
- Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh et al., A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018). https://doi.org/10.1126/science.aao0098
- S. Qu, L. Sun, S. Zhang, J. Liu, Y. Li et al., An artificially-intelligent Cornea with tactile sensation enables sensory expansion and interaction. Nat. Commun. 14, 7181 (2023). https://doi.org/10.1038/s41467-023-42240-3
- C. Jiang, H. Xu, L. Yang, J. Liu, Y. Li et al., Neuromorphic antennal sensory system. Nat. Commun. 15, 2109 (2024). https://doi.org/10.1038/s41467-024-46393-7
- L. Chen, C. Wen, S.-L. Zhang, Z.L. Wang, Z.-B. Zhang, Artificial tactile peripheral nervous system supported by self-powered transducers. Nano Energy 82, 105680 (2021). https://doi.org/10.1016/j.nanoen.2020.105680
- Y. Lee, T.-W. Lee, Organic synapses for neuromorphic electronics: from brain-inspired computing to sensorimotor nervetronics. Acc. Chem. Res. 52, 964–974 (2019). https://doi.org/10.1021/acs.accounts.8b00553
- D. Ielmini, S. Ambrogio, Emerging neuromorphic devices. Nanotechnology 31, 092001 (2020). https://doi.org/10.1088/1361-6528/ab554b
- Q. Wan, Y. Yang, X. Huang, P. Zhou, L. Chen et al., 2022 roadmap on neuromorphic devices and applications research in China. Neuromorph. Comput. Eng. 2, 042501 (2022). https://doi.org/10.1088/2634-4386/ac7a5a
- H. Liu, Y. Qin, H.-Y. Chen, J. Wu, J. Ma et al., Artificial neuronal devices based on emerging materials: neuronal dynamics and applications. Adv. Mater. 35, 2205047 (2023). https://doi.org/10.1002/adma.202205047
- J.-K. Han, S.-Y. Yun, S.-W. Lee, J.-M. Yu, Y.-K. Choi, A review of artificial spiking neuron devices for neural processing and sensing. Adv. Funct. Mater. 32, 2204102 (2022). https://doi.org/10.1002/adfm.202204102
- J. Bian, Z. Liu, Y. Tao, Z. Wang, X. Zhao et al., Advances in memristor based artificial neuron fabrication-materials, models, and applications. Int. J. Extrem. Manuf. 6, 012002 (2024). https://doi.org/10.1088/2631-7990/acfcf1
- Y. Wang, S. Liu, H. Wang, Y. Zhao, X.-D. Zhang, Neuron devices: emerging prospects in neural interfaces and recognition. Microsyst. Nanoeng. 8, 128 (2022). https://doi.org/10.1038/s41378-022-00453-4
- Z. Li, W. Tang, B. Zhang, R. Yang, X. Miao, Emerging memristive neurons for neuromorphic computing and sensing. Sci. Technol. Adv. Mater. 24, 2188878 (2023). https://doi.org/10.1080/14686996.2023.2188878
- C. Wan, G. Chen, Y. Fu, M. Wang, N. Matsuhisa et al., An artificial sensory neuron with tactile perceptual learning. Adv. Mater. 30, 1801291 (2018). https://doi.org/10.1002/adma.201801291
- B. Mu, L. Guo, J. Liao, P. Xie, G. Ding et al., Near-infrared artificial synapses for artificial sensory neuron system. Small 17, e2103837 (2021). https://doi.org/10.1002/smll.202103837
- C. Wan, P. Cai, X. Guo, M. Wang, N. Matsuhisa et al., An artificial sensory neuron with visual-haptic fusion. Nat. Commun. 11, 4602 (2020). https://doi.org/10.1038/s41467-020-18375-y
- H. Long, X. Lin, Y. Wang, H. Mao, Y. Zhu et al., Multifunctional ultraviolet laser induced graphene for flexible artificial sensory neuron. Adv. Mater. Technol. 8, 2201761 (2023). https://doi.org/10.1002/admt.202201761
- H. Ye, Z. Liu, H. Han, T. Shi, G. Liao, Lead-free AgBiI4 perovskite artificial synapses for a tactile sensory neuron system with information preprocessing function. Mater. Adv. 3, 7248–7256 (2022). https://doi.org/10.1039/D2MA00675H
- C.R. Donnelly, C. Ouyang, R.-R. Ji, How do sensory neurons sense danger signals? Trends Neurosci. 43, 822–838 (2020). https://doi.org/10.1016/j.tins.2020.07.008
- V.E. Abraira, D.D. Ginty, The sensory neurons of touch. Neuron 79, 618–639 (2013). https://doi.org/10.1016/j.neuron.2013.07.051
- D. Usoskin, A. Furlan, S. Islam, H. Abdo, P. Lönnerberg et al., Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015). https://doi.org/10.1038/nn.3881
- F.A. Pinho-Ribeiro, W.A. Verri Jr., I.M. Chiu, Nociceptor sensory neuron-immune interactions in pain and inflammation. Trends Immunol. 38, 5–19 (2017). https://doi.org/10.1016/j.it.2016.10.001
- N.M. Dalesio, S.F. Barreto Ortiz, J.L. Pluznick, D.E. Berkowitz, Olfactory, taste, and photo sensory receptors in non-sensory organs: it just makes sense. Front. Physiol. 9, 1673 (2018). https://doi.org/10.3389/fphys.2018.01673
- X. Ren, T. Léveillard, Modulating antioxidant systems as a therapeutic approach to retinal degeneration. Redox Biol. 57, 102510 (2022). https://doi.org/10.1016/j.redox.2022.102510
- H.E. Grossniklaus, E.E. Geisert, J.M. Nickerson, Introduction to the Retina, in Molecular biology of eye disease. ed. by J.F. Hejtmancik, J.M. Nickerson (Academic Press, Cambridge, 2015), pp.383–396
- K.-W. Yau, R.C. Hardie, Phototransduction motifs and variations. Cell 139, 246–264 (2009). https://doi.org/10.1016/j.cell.2009.09.029
- G.L. Fain, R. Hardie, S.B. Laughlin, Phototransduction and the evolution of photoreceptors. Curr. Biol. 20, R114–R124 (2010). https://doi.org/10.1016/j.cub.2009.12.006
- V.P. Pandiyan, A. Maloney-Bertelli, J.A. Kuchenbecker, K.C. Boyle, T. Ling et al., The optoretinogram reveals the primary steps of phototransduction in the living human eye. Sci. Adv. 6, eabc124 (2020). https://doi.org/10.1126/sciadv.abc1124
- M.T.H. Do, K.-W. Yau, Intrinsically photosensitive retinal ganglion cells. Physiol. Rev. 90, 1547–1581 (2010). https://doi.org/10.1152/physrev.00013.2010
- T.D. Lamb, Evolution of phototransduction, vertebrate photoreceptors and retina. Prog. Retin. Eye Res. 36, 52–119 (2013). https://doi.org/10.1016/j.preteyeres.2013.06.001
- S.-H. Woo, V. Lukacs, J.C. de Nooij, D. Zaytseva, C.R. Criddle et al., Piezo2 is the principal mechanotransduction channel for proprioception. Nat. Neurosci. 18, 1756–1762 (2015). https://doi.org/10.1038/nn.4162
- S.S. Ranade, S.-H. Woo, A.E. Dubin, R.A. Moshourab, C. Wetzel et al., Piezo2 is the major transducer of mechanical forces for touch sensation in mice. Nature 516, 121–125 (2014). https://doi.org/10.1038/nature13980
- J. Wu, A.H. Lewis, J. Grandl, Touch, tension, and transduction—the function and regulation of piezo ion channels. Trends Biochem. Sci. 42, 57–71 (2017). https://doi.org/10.1016/j.tibs.2016.09.004
- S.-H. Woo, S. Ranade, A.D. Weyer, A.E. Dubin, Y. Baba et al., Piezo2 is required for merkel-cell mechanotransduction. Nature 509, 622–626 (2014). https://doi.org/10.1038/nature13251
- S.-H. Woo, E.A. Lumpkin, A. Patapoutian, Merkel cells and neurons keep in touch. Trends Cell Biol. 25, 74–81 (2015). https://doi.org/10.1016/j.tcb.2014.10.003
- S. Maksimovic, M. Nakatani, Y. Baba, A.M. Nelson, K.L. Marshall et al., Epidermal Merkel cells are mechanosensory cells that tune mammalian touch receptors. Nature 509, 617–621 (2014). https://doi.org/10.1038/nature13250
- R. Ikeda, M. Cha, J. Ling, Z. Jia, D. Coyle et al., Merkel cells transduce and encode tactile stimuli to drive Aβ-afferent impulses. Cell 157, 664–675 (2014). https://doi.org/10.1016/j.cell.2014.02.026
- D. Deflorio, M. Di Luca, A.M. Wing, Skin and mechanoreceptor contribution to tactile input for perception: a review of simulation models. Front. Hum. Neurosci. 16, 862344 (2022). https://doi.org/10.3389/fnhum.2022.862344
- A. Handler, D.D. Ginty, The mechanosensory neurons of touch and their mechanisms of activation. Nat. Rev. Neurosci. 22, 521–537 (2021). https://doi.org/10.1038/s41583-021-00489-x
- M.J. Caterina, How do you feel? A warm and touching 2021 Nobel tribute. J. Clin. Invest. 131, e156587 (2021). https://doi.org/10.1172/JCI156587
- R.J. Schepers, M. Ringkamp, Thermoreceptors and thermosensitive afferents. Neurosci. Biobehav. Rev. 34, 177–184 (2010). https://doi.org/10.1016/j.neubiorev.2009.10.003
- R. Latorre, S. Brauchi, R. Madrid, P. Orio, A cool channel in cold transduction. Physiology 26, 273–285 (2011). https://doi.org/10.1152/physiol.00004.2011
- K. Lezama-García, D. Mota-Rojas, A.M.F. Pereira, J. Martínez-Burnes, M. Ghezzi et al., Transient receptor potential (TRP) and thermoregulation in animals: structural biology and neurophysiological aspects. Animals 12, 106 (2022). https://doi.org/10.3390/ani12010106
- D.M. Bautista, J. Siemens, J.M. Glazer, P.R. Tsuruda, A.I. Basbaum et al., The menthol receptor TRPM8 is the principal detector of environmental cold. Nature 448, 204–208 (2007). https://doi.org/10.1038/nature05910
- E. Spekker, T. Körtési, L. Vécsei, TRP channels: recent development in translational research and potential therapeutic targets in migraine. Int. J. Mol. Sci. 24, 700 (2022). https://doi.org/10.3390/ijms24010700
- S.A. Gravina, G.L. Yep, M. Khan, Human biology of taste. Ann. Saudi Med. 33, 217–222 (2013). https://doi.org/10.5144/0256-4947.2013.217
- J. Chandrashekar, M.A. Hoon, N.J.P. Ryba, C.S. Zuker, The receptors and cells for mammalian taste. Nature 444, 288–294 (2006). https://doi.org/10.1038/nature05401
- S.D. Roper, N. Chaudhari, Taste buds: cells, signals and synapses. Nat. Rev. Neurosci. 18, 485–497 (2017). https://doi.org/10.1038/nrn.2017.68
- D.A. Yarmolinsky, C.S. Zuker, N.J.P. Ryba, Common sense about taste: from mammals to insects. Cell 139, 234–244 (2009). https://doi.org/10.1016/j.cell.2009.10.001
- N. Chaudhari, S.D. Roper, The cell biology of taste. J. Cell Biol. 190, 285–296 (2010). https://doi.org/10.1083/jcb.201003144
- A. Taruno, K. Nomura, T. Kusakizako, Z. Ma, O. Nureki et al., Taste transduction and channel synapses in taste buds. Pflugers Arch. 473, 3–13 (2021). https://doi.org/10.1007/s00424-020-02464-4
- T.A. Gilbertson, S. Damak, R.F. Margolskee, The molecular physiology of taste transduction. Curr. Opin. Neurobiol. 10, 519–527 (2000). https://doi.org/10.1016/S0959-4388(00)00118-5
- B. Lindemann, Receptors and transduction in taste. Nature 413, 219–225 (2001). https://doi.org/10.1038/35093032
- Y. Ishimaru, H. Inada, M. Kubota, H. Zhuang, M. Tominaga et al., Transient receptor potential family members PKD1L3 and PKD2L1 form a candidate sour taste receptor. Proc. Natl. Acad. Sci. U.S.A. 103, 12569–12574 (2006). https://doi.org/10.1073/pnas.0602702103
- N. Horio, R. Yoshida, K. Yasumatsu, Y. Yanagawa, Y. Ishimaru et al., Sour taste responses in mice lacking PKD channels. PLoS ONE 6, e20007 (2011). https://doi.org/10.1371/journal.pone.0020007
- T.M. Nelson, N.D. Lopezjimenez, L. Tessarollo, M. Inoue, A.A. Bachmanov et al., Taste function in mice with a targeted mutation of the pkd1l3 gene. Chem. Senses 35, 565–577 (2010). https://doi.org/10.1093/chemse/bjq070
- Y.-H. Tu, A.J. Cooper, B. Teng, R.B. Chang, D.J. Artiga et al., An evolutionarily conserved gene family encodes proton-selective ion channels. Science 359, 1047–1050 (2018). https://doi.org/10.1126/science.aao3264
- J. Zhang, H. Jin, W. Zhang, C. Ding, S. O’Keeffe et al., Sour sensing from the tongue to the brain. Cell 179, 392-402.e15 (2019). https://doi.org/10.1016/j.cell.2019.08.031
- B. Teng, C.E. Wilson, Y.-H. Tu, N.R. Joshi, S.C. Kinnamon et al., Cellular and neural responses to sour stimuli require the proton channel Otop1. Curr. Biol. 29, 3647-3656.e5 (2019). https://doi.org/10.1016/j.cub.2019.08.077
- S. Boesveldt, V. Parma, The importance of the olfactory system in human well-being, through nutrition and social behavior. Cell Tissue Res. 383, 559–567 (2021). https://doi.org/10.1007/s00441-020-03367-7
- M. Melis, I.T. Barbarossa, G. Sollai, The implications of taste and olfaction in nutrition and health. Nutrients 15, 3412 (2023). https://doi.org/10.3390/nu15153412
- K. Touhara, L.B. Vosshall, Sensing odorants and pheromones with chemosensory receptors. Annu. Rev. Physiol. 71, 307–332 (2009). https://doi.org/10.1146/annurev.physiol.010908.163209
- G.V. Ronnett, C. Moon, G proteins and olfactory signal transduction. Annu. Rev. Physiol. 64, 189–222 (2002). https://doi.org/10.1146/annurev.physiol.64.082701.102219
- U.B. Kaupp, Olfactory signalling in vertebrates and insects: differences and commonalities. Nat. Rev. Neurosci. 11, 188–200 (2010). https://doi.org/10.1038/nrn2789
- N. Kang, J. Koo, Olfactory receptors in non-chemosensory tissues. BMB Rep. 45, 612–622 (2012). https://doi.org/10.5483/bmbrep.2012.45.11.232
- F. Abbas, F. Vinberg, Transduction and adaptation mechanisms in the Cilium or microvilli of photoreceptors and olfactory receptors from insects to humans. Front. Cell. Neurosci. 15, 662453 (2021). https://doi.org/10.3389/fncel.2021.662453
- M. Schwander, B. Kachar, U. Müller, The cell biology of hearing. J. Cell Biol. 190, 9–20 (2010). https://doi.org/10.1083/jcb.201001138
- S. Jia, P. Dallos, D.Z.Z. He, Mechanoelectric transduction of adult inner hair cells. J. Neurosci. 27, 1006–1014 (2007). https://doi.org/10.1523/JNEUROSCI.5452-06.2007
- W. Zheng, J.R. Holt, The mechanosensory transduction machinery in inner ear hair cells. Annu. Rev. Biophys. 50, 31–51 (2021). https://doi.org/10.1146/annurev-biophys-062420-081842
- P. Kazmierczak, U. Müller, Sensing sound: molecules that orchestrate mechanotransduction by hair cells. Trends Neurosci. 35, 220–229 (2012). https://doi.org/10.1016/j.tins.2011.10.007
- P.G. Gillespie, U. Müller, Mechanotransduction by hair cells: models, molecules, and mechanisms. Cell 139, 33–44 (2009). https://doi.org/10.1016/j.cell.2009.09.010
- Y.C. Wu, A.J. Ricci, R. Fettiplace, Two components of transducer adaptation in auditory hair cells. J. Neurophysiol. 82, 2171–2181 (1999). https://doi.org/10.1152/jn.1999.82.5.2171
- J.A. Assad, D.P. Corey, An active motor model for adaptation by vertebrate hair cells. J. Neurosci. 12, 3291–3309 (1992). https://doi.org/10.1523/JNEUROSCI.12-09-03291.1992
- J. Howard, A.J. Hudspeth, Mechanical relaxation of the hair bundle mediates adaptation in mechanoelectrical transduction by the bullfrog’s saccular hair cell. Proc. Natl. Acad. Sci. U.S.A. 84, 3064–3068 (1987). https://doi.org/10.1073/pnas.84.9.3064
- M. Pascal, D. Bozovic, Y. Choe, A.J. Hudspeth, Spontaneous oscillation by hair bundles of the bullfrog’s sacculus. J. Neurosci. 23, 4533 (2003). https://doi.org/10.1523/JNEUROSCI.23-11-04533.2003
- V. Torre, J.F. Ashmore, T.D. Lamb, A. Menini, Transduction and adaptation in sensory receptor cells. J. Neurosci. 15, 7757–7768 (1995). https://doi.org/10.1523/JNEUROSCI.15-12-07757.1995
- X. Zhang, W. Wang, Q. Liu, X. Zhao, J. Wei et al., An artificial neuron based on a threshold switching memristor. IEEE Electron Device Lett. 39, 308–311 (2018). https://doi.org/10.1109/LED.2017.2782752
- R. Cao, X. Zhang, S. Liu, J. Lu, Y. Wang et al., Compact artificial neuron based on anti-ferroelectric transistor. Nat. Commun. 13, 7018 (2022). https://doi.org/10.1038/s41467-022-34774-9
- L. Gao, P.-Y. Chen, S. Yu, NbOx based oscillation neuron for neuromorphic computing. Appl. Phys. Lett. 111, 103503 (2017). https://doi.org/10.1063/1.4991917
- D. Lee, M. Kwak, K. Moon, W. Choi, J. Park et al., Various threshold switching devices for integrate and fire neuron applications. Adv. Electron. Mater. 5, 1800866 (2019). https://doi.org/10.1002/aelm.201800866
- T. Tuma, A. Pantazi, M. Le Gallo, A. Sebastian, E. Eleftheriou, Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016). https://doi.org/10.1038/nnano.2016.70
- A. Sengupta, P. Panda, P. Wijesinghe, Y. Kim, K. Roy, Magnetic tunnel junction mimics stochastic cortical spiking neurons. Sci. Rep. 6, 30039 (2016). https://doi.org/10.1038/srep30039
- H. Kalita, A. Krishnaprasad, N. Choudhary, S. Das, D. Dev et al., Artificial neuron using vertical MoS2/graphene threshold switching memristors. Sci. Rep. 9, 53 (2019). https://doi.org/10.1038/s41598-018-35828-z
- F. Qian, R.-S. Chen, R. Wang, J. Wang, P. Xie et al., A leaky integrate-and-fire neuron based on hexagonal boron nitride (h-BN) monocrystalline memristor. IEEE Trans. Electron Devices 69, 6049–6056 (2022). https://doi.org/10.1109/TED.2022.3206170
- Y. Zhang, L. Chu, W. Li, A fully-integrated memristor chip for edge learning. Nano-Micro Lett. 16, 166 (2024). https://doi.org/10.1007/s40820-024-01368-7
- H. Zhou, S. Li, K.-W. Ang, Y.-W. Zhang, Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett. 16, 121 (2024). https://doi.org/10.1007/s40820-024-01335-2
- Y. Sun, C. Song, S. Yin, L. Qiao, Q. Wan et al., Design of a controllable redox-diffusive threshold switching memristor. Adv. Electron. Mater. 6, 2000695 (2020). https://doi.org/10.1002/aelm.202000695
- J.-H. Cha, S.Y. Yang, J. Oh, S. Choi, S. Park et al., Conductive-bridging random-access memories for emerging neuromorphic computing. Nanoscale 12, 14339–14368 (2020). https://doi.org/10.1039/D0NR01671C
- Z. Wang, M. Rao, R. Midya, S. Joshi, H. Jiang et al., Threshold switching: threshold switching of Ag or Cu in dielectrics: materials, mechanism, and applications. Adv. Funct. Mater. 28, 1870036 (2018). https://doi.org/10.1002/adfm.201870036
- R. Wang, J.-Q. Yang, J.-Y. Mao, Z.-P. Wang, S. Wu et al., Recent advances of volatile memristors: devices, mechanisms, and applications. Adv. Intell. Syst. 2, 2000055 (2020). https://doi.org/10.1002/aisy.202000055
- W. Sun, B. Gao, M. Chi, Q. Xia, J.J. Yang et al., Understanding memristive switching via in situ characterization and device modeling. Nat. Commun. 10, 3453 (2019). https://doi.org/10.1038/s41467-019-11411-6
- F. Zahoor, T.Z. Azni Zulkifli, F.A. Khanday, Resistive random access memory (RRAM): an overview of materials, switching mechanism, performance, multilevel cell (mlc) storage, modeling, and applications. Nanoscale Res. Lett. 15, 90 (2020). https://doi.org/10.1186/s11671-020-03299-9
- Z. Shen, C. Zhao, Y. Qi, W. Xu, Y. Liu et al., Advances of RRAM devices: resistive switching mechanisms, materials and bionic synaptic application. Nanomaterials 10, 1437 (2020). https://doi.org/10.3390/nano10081437
- F. Pan, S. Gao, C. Chen, C. Song, F. Zeng, Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater. Sci. Eng. R. Rep. 83, 1–59 (2014). https://doi.org/10.1016/j.mser.2014.06.002
- Y. Zhou, S. Ramanathan, Mott memory and neuromorphic devices. Proc. IEEE 103, 1289–1310 (2015). https://doi.org/10.1109/JPROC.2015.2431914
- M.D. Pickett, G. Medeiros-Ribeiro, R.S. Williams, A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013). https://doi.org/10.1038/nmat3510
- S. Kumar, J.P. Strachan, R.S. Williams, Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 548, 318–321 (2017). https://doi.org/10.1038/nature23307
- I. Messaris, T.D. Brown, A.S. Demirkol, A. Ascoli, M.M. Al Chawa et al., NbO2-Mott memristor: a circuit- theoretic investigation. IEEE Trans. Circuits Syst. I Regul. Pap. 68, 4979–4992 (2021). https://doi.org/10.1109/TCSI.2021.3126657
- G. Stefanovich, A. Pergament, D. Stefanovich, Electrical switching and Mott transition in VO2. J. Phys. Condens. Matter 12, 8837–8845 (2000). https://doi.org/10.1088/0953-8984/12/41/310
- M.M. Qazilbash, M. Brehm, B.G. Chae, P.C. Ho, G.O. Andreev et al., Mott transition in VO2 revealed by infrared spectroscopy and nano-imaging. Science 318, 1750–1753 (2007). https://doi.org/10.1126/science.1150124
- H.-T. Kim, B.-G. Chae, D.-H. Youn, S.-L. Maeng, G. Kim et al., Mechanism and observation of Mott transition in VO2-based two- and three-terminal devices. New J. Phys. 6, 52 (2004). https://doi.org/10.1088/1367-2630/6/1/052
- P. Wang, A.I. Khan, S. Yu, Cryogenic behavior of NbO2 based threshold switching devices as oscillation neurons. Appl. Phys. Lett. 116, 162108 (2020). https://doi.org/10.1063/5.0006467
- J. Woo, P. Wang, S. Yu, Integrated crossbar array with resistive synapses and oscillation neurons. IEEE Electron Device Lett. 40, 1313–1316 (2019). https://doi.org/10.1109/LED.2019.2921656
- G. Zhou, Z. Wang, B. Sun, F. Zhou, L. Sun et al., Volatile and nonvolatile memristive devices for neuromorphic computing. Adv. Electron. Mater. 8, 2101127 (2022). https://doi.org/10.1002/aelm.202101127
- S. Chen, T. Zhang, S. Tappertzhofen, Y. Yang, I. Valov, Electrochemical-memristor-based artificial neurons and synapses-fundamentals, applications, and challenges. Adv. Mater. 35, e2301924 (2023). https://doi.org/10.1002/adma.202301924
- W. Yi, K.K. Tsang, S.K. Lam, X. Bai, J.A. Crowell et al., Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018). https://doi.org/10.1038/s41467-018-07052-w
- W. Park, G. Kim, J.H. In, H. Rhee, H. Song et al., High amplitude spike generator in Au nanodot-incorporated NbOx Mott memristor. Nano Lett. 23, 5399–5407 (2023). https://doi.org/10.1021/acs.nanolett.2c04599
- S.K. Nath, X. Sun, S.K. Nandi, X. Chen, Z. Wang et al., Harnessing metal/oxide interlayer to engineer the memristive response and oscillation dynamics of two-terminal memristors. Adv. Funct. Mater. 33, 2306428 (2023). https://doi.org/10.1002/adfm.202306428
- C.-D. Chen, M. Matloubian, R. Sundaresan, B.-Y. Mao, C.C. Wei et al., Single-transistor latch in SOI MOSFETs. IEEE Electron Device Lett. 9, 636–638 (1988). https://doi.org/10.1109/55.20420
- R.J.T. Bunyan, M.J. Uren, N.J. Thomas, J.R. Davis, Degradation in thin-film SOI MOSFET’s caused by single-transistor latch. IEEE Electron Device Lett. 11, 359–361 (1990). https://doi.org/10.1109/55.62955
- J.-W. Jung, J.-K. Han, J.-M. Yu, M.-W. Lee, M.-S. Kim et al., Concealable oscillation-based physical unclonable function with a single-transistor latch. IEEE Electron Device Lett. 43, 1359–1362 (2022). https://doi.org/10.1109/led.2022.3182754
- J.-K. Han, M.-W. Lee, J.-M. Yu, Y.-K. Choi, A single transistor-based threshold switch for a bio-inspired reconfigurable threshold logic. Adv. Electron. Mater. 7, 2100117 (2021). https://doi.org/10.1002/aelm.202100117
- J.K. Han, J.W. Lee, Y. Kim, Y.B. Kim, S.Y. Yun et al., 3D neuromorphic hardware with single thin-film transistor synapses over single thin-body transistor neurons by monolithic vertical integration. Adv. Sci. 10, e2302380 (2023). https://doi.org/10.1002/advs.202302380
- J.K. Han, D.M. Geum, M.W. Lee, J.M. Yu, S.K. Kim et al., Bioinspired photoresponsive single transistor neuron for a neuromorphic visual system. Nano Lett. 20, 8781–8788 (2020). https://doi.org/10.1021/acs.nanolett.0c03652
- V.K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M.E. Beck et al., Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018). https://doi.org/10.1038/nature25747
- Y. Zheng, H. Ravichandran, T.F. Schranghamer, N. Trainor, J.M. Redwing et al., Hardware implementation of Bayesian network based on two-dimensional memtransistors. Nat. Commun. 13, 5578 (2022). https://doi.org/10.1038/s41467-022-33053-x
- A. Wali, S. Das, Two-dimensional memtransistors for non-von Neumann computing: progress and challenges. Adv. Funct. Mater. 34, 2308129 (2024). https://doi.org/10.1002/adfm.202308129
- H.-S. Lee, V.K. Sangwan, W.A.G. Rojas, H. Bergeron, H.Y. Jeong et al., Dual-gated MoS2 memtransistor crossbar array. Adv. Funct. Mater. 30, 2003683 (2020). https://doi.org/10.1002/adfm.202003683
- X. Yan, J.H. Qian, V.K. Sangwan, M.C. Hersam, Progress and challenges for memtransistors in neuromorphic circuits and systems. Adv. Mater. 34, e2108025 (2022). https://doi.org/10.1002/adma.202108025
- A. Dodda, N. Trainor, J.M. Redwing, S. Das, All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors. Nat. Commun. 13, 3587 (2022). https://doi.org/10.1038/s41467-022-31148-z
- L. Wang, W. Liao, S.L. Wong, Z.G. Yu, S. Li et al., Artificial synapses based on multiterminal memtransistors for neuromorphic application. Adv. Funct. Mater. 29, 1901106 (2019). https://doi.org/10.1002/adfm.201901106
- H. Li, J. Hu, Y. Zhang, A. Chen, J. Zhou et al., Single-transistor optoelectronic spiking neuron with optogenetics-inspired spatiotemporal dynamics. Adv. Funct. Mater. 34, 2314456 (2024). https://doi.org/10.1002/adfm.202314456
- J. Jadwiszczak, D. Keane, P. Maguire, C.P. Cullen, Y. Zhou et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13, 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
- H. Li, J. Hu, A. Chen, C. Wang, L. Chen et al., Single-transistor neuron with excitatory-inhibitory spatiotemporal dynamics applied for neuronal oscillations. Adv. Mater. 34, e2207371 (2022). https://doi.org/10.1002/adma.202207371
- Y. Chen, D. Li, H. Ren, Y. Tang, K. Liang et al., Highly linear and symmetric synaptic memtransistors based on polarization switching in two-dimensional ferroelectric semiconductors. Small 18, e2203611 (2022). https://doi.org/10.1002/smll.202203611
- K. Liu, T. Zhang, B. Dang, L. Bao, L. Xu et al., An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing. Nat. Electron. 5, 761–773 (2022). https://doi.org/10.1038/s41928-022-00847-2
- S. Chun, J.-S. Kim, Y. Yoo, Y. Choi, S.J. Jung et al., An artificial neural tactile sensing system. Nat. Electron. 4, 429–438 (2021). https://doi.org/10.1038/s41928-021-00585-x
- V. Amoli, S.Y. Kim, J.S. Kim, H. Choi, J. Koo et al., Biomimetics for high-performance flexible tactile sensors and advanced artificial sensory systems. J. Mater. Chem. C 7, 14816–14844 (2019). https://doi.org/10.1039/c9tc05392a
- L. Shan, H. Zeng, Y. Liu, X. Zhang, E. Li et al., Artificial tactile sensing system with photoelectric output for high accuracy haptic texture recognition and parallel information processing. Nano Lett. 22, 7275–7283 (2022). https://doi.org/10.1021/acs.nanolett.2c02995
- K. Lee, S. Jang, K.L. Kim, M. Koo, C. Park et al., Artificially intelligent tactile ferroelectric skin. Adv. Sci. 7, 2001662 (2020). https://doi.org/10.1002/advs.202001662
- X. Zhang, Y. Zhuo, Q. Luo, Z. Wu, R. Midya et al., An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11, 51 (2020). https://doi.org/10.1038/s41467-019-13827-6
- F. Li, R. Wang, C. Song, M. Zhao, H. Ren et al., A skin-inspired artificial mechanoreceptor for tactile enhancement and integration. ACS Nano 15, 16422–16431 (2021). https://doi.org/10.1021/acsnano.1c05836
- J. Wen, L. Zhang, Y.-Z. Wang, X. Guo, Artificial tactile perception system based on spiking tactile neurons and spiking neural networks. ACS Appl. Mater. Interfaces 16, 998–1004 (2024). https://doi.org/10.1021/acsami.3c12244
- J. Li, R. Bao, J. Tao, Y. Peng, C. Pan, Recent progress in flexible pressure sensor arrays: from design to applications. J. Mater. Chem. C 6, 11878–11892 (2018). https://doi.org/10.1039/C8TC02946F
- Y. Duan, S. He, J. Wu, B. Su, Y. Wang, Recent progress in flexible pressure sensor arrays. Nanomaterials 12, 2495 (2022). https://doi.org/10.3390/nano12142495
- S.L. Fang, C.Y. Han, Z.R. Han, B. Ma, Y.L. Cui et al., An artificial spiking afferent neuron system achieved by 1M1S for neuromorphic computing. IEEE Trans. Electron Devices 69, 2346–2352 (2022). https://doi.org/10.1109/TED.2022.3159270
- W. Ye, J. Lin, X. Zhang, Q. Lian, Y. Liu et al., Self-powered perception system based on triboelectric nanogenerator and artificial neuron for fast-speed multilevel feature recognition. Nano Energy 100, 107525 (2022). https://doi.org/10.1016/j.nanoen.2022.107525
- A. Delorme, L. Perrinet, S.J. Thorpe, Networks of integrate-and-fire neurons using rank order coding B: spike timing dependent plasticity and emergence of orientation selectivity. Neurocomputing 38, 539–545 (2001). https://doi.org/10.1016/S0925-2312(01)00403-9
- H. Tang, D. Cho, D. Lew, T. Kim, J. Park, Rank order coding based spiking convolutional neural network architecture with energy-efficient membrane voltage updates. Neurocomputing 407, 300–312 (2020). https://doi.org/10.1016/j.neucom.2020.05.031
- S. Wang, J.Y. Oh, J. Xu, H. Tran, Z. Bao, Skin-inspired electronics: an emerging paradigm. Acc. Chem. Res. 51, 1033–1045 (2018). https://doi.org/10.1021/acs.accounts.8b00015
- Y. Liu, M. Pharr, G.A. Salvatore, Lab-on-skin: a review of flexible and stretchable electronics for wearable health monitoring. ACS Nano 11, 9614–9635 (2017). https://doi.org/10.1021/acsnano.7b04898
- J.C. Yang, J. Mun, S.Y. Kwon, S. Park, Z. Bao et al., Electronic skin: recent progress and future prospects for skin-attachable devices for health monitoring, robotics, and prosthetics. Adv. Mater. 31, e1904765 (2019). https://doi.org/10.1002/adma.201904765
- S.L. Fang, C.Y. Han, W. Liu, Z.R. Han, B. Ma et al., A bioinspired flexible artificial mechanoreceptor based on VO2 insulator-metal transition memristor. J. Alloys Compd. 911, 165096 (2022). https://doi.org/10.1016/j.jallcom.2022.165096
- J.-K. Han, I.-W. Tcho, S.-B. Jeon, J.-M. Yu, W.-G. Kim et al., Self-powered artificial mechanoreceptor based on triboelectrification for a neuromorphic tactile system. Adv. Sci. 9, e2201831 (2022). https://doi.org/10.1002/advs.202201831
- J. Benda, Neural adaptation. Curr. Biol. 31, R110–R116 (2021). https://doi.org/10.1016/j.cub.2020.11.054
- P.V. Watkins, D.L. Barbour, Specialized neuronal adaptation for preserving input sensitivity. Nat. Neurosci. 11, 1259–1261 (2008). https://doi.org/10.1038/nn.2201
- J. Tuthill, R. Wilson, Mechanosensation and adaptive motor control in insects. Curr. Biol. 26, R1022–R1038 (2016). https://doi.org/10.1016/j.cub.2016.06.070
- P. Delmas, J. Hao, L. Rodat-Despoix, Molecular mechanisms of mechanotransduction in mammalian sensory neurons. Nat. Rev. Neurosci. 12, 139–153 (2011). https://doi.org/10.1038/nrn2993
- Z. Xie, X. Zhu, W. Wang, Z. Guo, Y. Zhang et al., Temporal pattern coding in ionic memristor-based spiking neurons for adaptive tactile perception. Adv. Electron. Mater. 8, 2200334 (2022). https://doi.org/10.1002/aelm.202200334
- M.-W. Lee, J.-K. Han, G.-J. Yun, J.-M. Yu, G.-B. Lee et al., A temperature sensor with a thermillator. IEEE Electron Device Lett. 42, 1654–1657 (2021). https://doi.org/10.1109/led.2021.3111622
- J. Wu, W. Ye, J. Lin, X. Zhang, B. Zeng et al., Temperature regulated artificial neuron based on memristor. IEEE Electron Device Lett. 43, 2001–2004 (2022). https://doi.org/10.1109/led.2022.3206796
- K. Shi, S. Heng, X. Wang, S. Liu, H. Cui et al., An oxide based spiking thermoreceptor for low-power thermography edge detection. IEEE Electron Device Lett. 43, 2196–2199 (2022). https://doi.org/10.1109/LED.2022.3215693
- C.Y. Han, Z.R. Han, S.L. Fang, S.Q. Fan, J.Q. Yin et al., Characterization and modelling of flexible VO2 Mott memristor for the artificial spiking warm receptor. Adv. Mater. Interfaces 9, 2200394 (2022). https://doi.org/10.1002/admi.202200394
- J. Zhao, L. Tong, J. Niu, Z. Fang, Y. Pei et al., A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbOx volatile threshold devices with ultra-low operating current. Nanoscale 15, 17599–17608 (2023). https://doi.org/10.1039/D3NR03034B
- Y. Luo, L. Pu, M. Zuba, Z. Peng, J.-H. Cui, Challenges and opportunities of underwater cognitive acoustic networks. IEEE Trans. Emerg. Top. Comput. 2, 198–211 (2014). https://doi.org/10.1109/TETC.2014.2310457
- B. Mishachandar, S. Vairamuthu, An underwater cognitive acoustic network strategy for efficient spectrum utilization. Appl. Acoust. 175, 107861 (2021). https://doi.org/10.1016/j.apacoust.2020.107861
- K.M.H. Badami, S. Lauwereins, W. Meert, M. Verhelst, A 90 nm CMOS, 6 μW power-proportional acoustic sensing frontend for voice activity detection. IEEE J. Solid State Circuits 51, 291–302 (2016). https://doi.org/10.1109/JSSC.2015.2487276
- C.-T. Chiang, C.-Y. Wu, A CMOS digitized silicon condenser microphone for acoustic applications. IEEE Sens. J. 11, 296–304 (2011). https://doi.org/10.1109/JSEN.2010.2071380
- S.-Y. Yun, J.-K. Han, S.-W. Lee, J.-M. Yu, S.-B. Jeon et al., Self-aware artificial auditory neuron with a triboelectric sensor for spike-based neuromorphic hardware. Nano Energy 109, 108322 (2023). https://doi.org/10.1016/j.nanoen.2023.108322
- Y. Tahara, K. Toko, Electronic tongues—a review. IEEE Sens. J. 13, 3001–3011 (2013). https://doi.org/10.1109/JSEN.2013.2263125
- A. Riul Jr., C.A.R. Dantas, C.M. Miyazaki, O.N. Oliveira Jr., Recent advances in electronic tongues. Anal. Bioanal. Chem. 135, 2481 (2010). https://doi.org/10.1039/c0an00292e
- Y. Vlasov, A. Legin, A. Rudnitskaya, Electronic tongues and their analytical application. Anal. Bioanal. Chem. 373, 136–146 (2002). https://doi.org/10.1007/s00216-002-1310-2
- J.-K. Han, S.-C. Park, J.-M. Yu, J.-H. Ahn, Y.-K. Choi, A bioinspired artificial gustatory neuron for a neuromorphic based electronic tongue. Nano Lett. 22, 5244–5251 (2022). https://doi.org/10.1021/acs.nanolett.2c01107
- S. Ampuero, J.O. Bosset, The electronic nose applied to dairy products: a review. Sens. Actuat. B Chem. 94, 1–12 (2003). https://doi.org/10.1016/S0925-4005(03)00321-6
- L. Cheng, Q.-H. Meng, A.J. Lilienthal, P.-F. Qi, Development of compact electronic noses: a review. Meas. Sci. Technol. 32, 062002 (2021). https://doi.org/10.1088/1361-6501/abef3b
- F. Röck, N. Barsan, U. Weimar, Electronic nose: current status and future trends. Chem. Rev. 108, 705–725 (2008). https://doi.org/10.1021/cr068121q
- A.D. Wilson, M. Baietto, Applications and advances in electronic-nose technologies. Sensors 9, 5099–5148 (2009). https://doi.org/10.3390/s90705099
- X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
- T. Wang, X.-X. Wang, J. Wen, Z.-Y. Shao, H.-M. Huang et al., A bio-inspired neuromorphic sensory system. Adv. Intell. Syst. 4, 2200047 (2022). https://doi.org/10.1002/aisy.202200047
- J.-K. Han, M. Kang, J. Jeong, I. Cho, J.-M. Yu et al., Artificial olfactory neuron for an in-sensor neuromorphic nose. Adv. Sci. 9, e2106017 (2022). https://doi.org/10.1002/advs.202106017
- L. Wollmuth, Structure and gating of the glutamate receptor ion channel. Trends Neurosci. 27, 321–328 (2004). https://doi.org/10.1016/j.tins.2004.04.005
- M.L. Mayer, Glutamate receptor ion channels. Curr. Opin. Neurobiol. 15, 282–288 (2005). https://doi.org/10.1016/j.conb.2005.05.004
- N.C. Spitzer, Activity-dependent neurotransmitter respecification. Nat. Rev. Neurosci. 13, 94–106 (2012). https://doi.org/10.1038/nrn3154
- S.L. Foote, R. Freedman, A.P. Oliver, Effects of putative neurotransmitters on neuronal activity in monkey auditory cortex. Brain Res. 86, 229–242 (1975). https://doi.org/10.1016/0006-8993(75)90699-X
- C. Zhao, Y. Wang, G. Tang, J. Ru, Z. Zhu et al., Ionic flexible sensors: mechanisms, materials, structures, and applications. Adv. Funct. Mater. 32, 2110417 (2022). https://doi.org/10.1002/adfm.202110417
- V. Amoli, J.S. Kim, S.Y. Kim, J. Koo, Y.S. Chung et al., Ionic tactile sensors for emerging human-interactive technologies: a review of recent progress. Adv. Funct. Mater. 30, 1904532 (2020). https://doi.org/10.1002/adfm.201904532
- T. Sarkar, K. Lieberth, A. Pavlou, T. Frank, V. Mailaender et al., An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat. Electron. 5, 774–783 (2022). https://doi.org/10.1038/s41928-022-00859-y
- A. Classen, T. Heumueller, I. Wabra, J. Gerner, Y. He et al., Revealing hidden UV instabilities in organic solar cells by correlating device and material stability. Adv. Energy Mater. 9, 1902124 (2019). https://doi.org/10.1002/aenm.201902124
- H. Aziz, Z.D. Popovic, Degradation phenomena in small-molecule organic light-emitting devices. Chem. Mater. 16, 4522–4532 (2004). https://doi.org/10.1021/cm040081o
- S.W. Cho, C. Jo, Y.H. Kim, S.K. Park, Progress of materials and devices for neuromorphic vision sensors. Nano-Micro Lett. 14, 203 (2022). https://doi.org/10.1007/s40820-022-00945-y
- S.S. Radhakrishnan, A. Sebastian, A. Oberoi, S. Das, S. Das, A biomimetic neural encoder for spiking neural network. Nat. Commun. 12, 2143 (2021). https://doi.org/10.1038/s41467-021-22332-8
- H. Lee, S.W. Cho, S.J. Kim, J. Lee, K.S. Kim et al., Three-terminal ovonic threshold switch (3T-OTS) with tunable threshold voltage for versatile artificial sensory neurons. Nano Lett. 22, 733–739 (2022). https://doi.org/10.1021/acs.nanolett.1c04125
- F. Wang, F. Hu, M. Dai, S. Zhu, F. Sun et al., Author Correction: a two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding. Nat. Commun. 14, 7877 (2023). https://doi.org/10.1038/s41467-023-37623-5
- J. Zhao, Y. Ran, Y. Pei, Y. Wei, J. Sun et al., Memristors based on NdNiO3 nanocrystals film as sensory neurons for neuromorphic computing. Mater. Horiz. 10, 4521–4531 (2023). https://doi.org/10.1039/d3mh00835e
- J.K. Han, J. Sim, D.M. Geum, S.K. Kim, J.M. Yu et al., 3D stackable broadband photoresponsive InGaAs biristor neuron for a neuromorphic visual system with near 1 V operation. In: 2021 IEEE international electron devices meeting (IEDM). December 11-16, 2021, San Francisco, CA, USA. IEEE, (2021), pp. 1–4.
- D. Regan, H. Spekreijse, Electrophysiological correlate of binocular depth perception in man. Nature 225, 92–94 (1970). https://doi.org/10.1038/225092a0
- N. Qian, Binocular disparity and the perception of depth. Neuron 18, 359–368 (1997). https://doi.org/10.1016/S0896-6273(00)81238-6
- C. Chen, Y. He, H. Mao, L. Zhu, X. Wang et al., A photoelectric spiking neuron for visual depth perception. Adv. Mater. 34, e2201895 (2022). https://doi.org/10.1002/adma.202201895
- X. Wang, C. Chen, L. Zhu, K. Shi, B. Peng et al., Vertically integrated spiking cone photoreceptor arrays for color perception. Nat. Commun. 14, 3444 (2023). https://doi.org/10.1038/s41467-023-39143-8
- D.I. Perrett, E.T. Rolls, W. Caan, Visual neurones responsive to faces in the monkey temporal cortex. Exp. Brain Res. 47, 329–342 (1982). https://doi.org/10.1007/BF00239352
- R.S. Johansson, I. Birznieks, First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat. Neurosci. 7, 170–177 (2004). https://doi.org/10.1038/nn1177
- J. Huxter, N. Burgess, J. O’Keefe, Independent rate and temporal coding in hippocampal pyramidal cells. Nature 425, 828–832 (2003). https://doi.org/10.1038/nature02058
- S.S. Radhakrishnan, S. Chakrabarti, D. Sen, M. Das, T.F. Schranghamer et al., A sparse and spike-timing-based adaptive photoencoder for augmenting machine vision for spiking neural networks. Adv. Mater. 34, e2202535 (2022). https://doi.org/10.1002/adma.202202535
- C. Spence, S. Squire, Multisensory integration: maintaining the perception of synchrony. Curr. Biol. 13, R519–R521 (2003). https://doi.org/10.1016/S0960-9822(03)00445-7
- T. Wang, P. Zheng, S. Li, L. Wang, Multimodal human–robot interaction for human-centric smart manufacturing: a survey. Adv. Intell. Syst. 6, 2300359 (2024). https://doi.org/10.1002/aisy.202300359
- R. Yang, W. Zhang, N. Tiwari, H. Yan, T. Li et al., Multimodal sensors with decoupled sensing mechanisms. Adv. Sci. 9, 2202470 (2022). https://doi.org/10.1002/advs.202202470
- Z. Yuan, G. Shen, Materials and device architecture towards a multimodal electronic skin. Mater. Today 64, 165–179 (2023). https://doi.org/10.1016/j.mattod.2023.02.023
- Q. Duan, T. Zhang, C. Liu, R. Yuan, G. Li et al., Artificial multisensory neurons with fused haptic and temperature perception for multimodal in-sensor computing. Adv. Intell. Syst. 4, 2270039 (2022). https://doi.org/10.1002/aisy.202270039
- J. Zhu, X. Zhang, R. Wang, M. Wang, P. Chen et al., A heterogeneously integrated spiking neuron array for multimode-fused perception and object classification. Adv. Mater. 34, 2200481 (2022). https://doi.org/10.1002/adma.202200481
- J.-K. Han, S.-Y. Yun, J.-M. Yu, S.-B. Jeon, Y.-K. Choi, Artificial multisensory neuron with a single transistor for multimodal perception through hybrid visual and thermal sensing. ACS Appl. Mater. Interfaces 15, 5449–5455 (2023). https://doi.org/10.1021/acsami.2c19208
- A.C. Abad, A. Ranasinghe, Visuotactile sensors with emphasis on GelSight sensor: a review. IEEE Sens. J. 20, 7628–7638 (2020). https://doi.org/10.1109/JSEN.2020.2979662
- P. Cornelio, C. Velasco, M. Obrist, Multisensory integration as per technological advances: a review. Front. Neurosci. 15, 652611 (2021). https://doi.org/10.3389/fnins.2021.652611
- J.T. Lee, D. Bollegala, S. Luo, “Touching to see” and “seeing to feel”: robotic cross-modal sensory data generation for visual-tactile perception. In: 2019 International conference on robotics and automation (ICRA). May 20–24, 2019, Montreal, QC, Canada. IEEE, (2019), pp. 4276–4282.
- P. Falco, S. Lu, A. Cirillo, C. Natale, S. Pirozzi et al., Cross-modal visuo-tactile object recognition using robotic active exploration. In: 2017 IEEE International conference on robotics and automation (ICRA). May 29–June 3, 2017, Singapore. IEEE, (2017), pp. 5273–5280.
- M.U.K. Sadaf, N.U. Sakib, A. Pannone, H. Ravichandran, S. Das, A bio-inspired visuotactile neuron for multisensory integration. Nat. Commun. 14, 5729 (2023). https://doi.org/10.1038/s41467-023-40686-z
- Y. Yuan, R. Gao, Q. Wu, S. Fang, X. Bu et al., Artificial leaky integrate-and-fire sensory neuron for in-sensor computing neuromorphic perception at the edge. ACS Sens. 8, 2646–2655 (2023). https://doi.org/10.1021/acssensors.3c00487
- Y. Ding, P. Yuan, J. Yu, Y. Chen, P. Jiang et al., Forming-free NbOx-based memristor enabling low-energy-consumption artificial spiking afferent nerves. IEEE Trans. Electron Devices 69, 5391–5394 (2022). https://doi.org/10.1109/TED.2022.3191988
- S. Zhong, Y. Zhang, H. Zheng, F. Yu, R. Zhao, Spike-based spatiotemporal processing enabled by oscillation neuron for energy-efficient artificial sensory systems. Adv. Intell. Syst. 4, 2200076 (2022). https://doi.org/10.1002/aisy.202200076
- B. Grisafe, M. Jerry, J.A. Smith, S. Datta, Performance enhancement of Ag/HfO2 metal ion threshold switch cross-point selectors. IEEE Electron Device Lett. 40, 1602–1605 (2019). https://doi.org/10.1109/led.2019.2936104
- J. Park, T. Hadamek, A.B. Posadas, E. Cha, A.A. Demkov et al., Multi-layered NiOy/NbOx/NiOy fast drift-free threshold switch with high Ion/Ioff ratio for selector application. Sci. Rep. 7, 4068 (2017). https://doi.org/10.1038/s41598-017-04529-4
- L. Wang, W. Cai, D. He, Q. Lin, D. Wan et al., Performance improvement of GeTex-based ovonic threshold switching selector by C doping. IEEE Electron Device Lett. 42, 688–691 (2021). https://doi.org/10.1109/LED.2021.3064857
- N. Luo, W. Dai, C. Li, Z. Zhou, L. Lu et al., Flexible piezoresistive sensor patch enabling ultralow power cuffless blood pressure measurement. Adv. Funct. Mater. 26, 1178–1187 (2016). https://doi.org/10.1002/adfm.201504560
- K. Kim, M. Jung, B. Kim, J. Kim, K. Shin et al., Low-voltage, high-sensitivity and high-reliability bimodal sensor array with fully inkjet-printed flexible conducting electrode for low power consumption electronic skin. Nano Energy 41, 301–307 (2017). https://doi.org/10.1016/j.nanoen.2017.09.024
- Y. Guo, M. Zhong, Z. Fang, P. Wan, G. Yu, A wearable transient pressure sensor made with MXene nanosheets for sensitive broad-range human-machine interfacing. Nano Lett. 19, 1143–1150 (2019). https://doi.org/10.1021/acs.nanolett.8b04514
- M. Swierczewska, G. Liu, S. Lee, X. Chen, High-sensitivity nanosensors for biomarker detection. Chem. Soc. Rev. 41, 2641–2655 (2012). https://doi.org/10.1039/c1cs15238f
- C. Wang, L. Yin, L. Zhang, D. Xiang, R. Gao, Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10, 2088–2106 (2010). https://doi.org/10.3390/s100302088
- X. Wang, J. Yu, Y. Cui, W. Li, Research progress of flexible wearable pressure sensors. Sens. Actuat. A Phys. 330, 112838 (2021). https://doi.org/10.1016/j.sna.2021.112838
- S. Pyo, J. Lee, K. Bae, S. Sim, J. Kim, Recent progress in flexible tactile sensors for human-interactive systems: from sensors to advanced applications. Adv. Mater. 33, e2005902 (2021). https://doi.org/10.1002/adma.202005902
- Y. Lee, J. Park, S. Cho, Y.-E. Shin, H. Lee et al., Flexible ferroelectric sensors with ultrahigh pressure sensitivity and linear response over exceptionally broad pressure range. ACS Nano 12, 4045–4054 (2018). https://doi.org/10.1021/acsnano.8b01805
- M. Stevens, G. Yun, T. Hasan, Porous conductive hybrid composite with superior pressure sensitivity and dynamic range. Adv. Funct. Mater. 34, 2309347 (2024). https://doi.org/10.1002/adfm.202309347
- H. Niu, N. Li, E.-S. Kim, Y.K. Shin, N.-Y. Kim et al., Advances in advanced solution-synthesis-based structural materials for tactile sensors and their intelligent applications. InfoMat 6, e12500 (2024). https://doi.org/10.1002/inf2.12500
- S. Zhong, J. Zhou, F. Yu, M. Xu, Y. Zhang et al., An optical neuromorphic sensor with high uniformity and high linearity for indoor visible light localization. Adv. Sens. Res. 3, 2300197 (2024). https://doi.org/10.1002/adsr.202300197
- B. Zhu, Z. Xu, X. Liu, Z. Wang, Y. Zhang et al., High-linearity flexible pressure sensor based on the Gaussian-curve-shaped microstructure for human physiological signal monitoring. ACS Sens. 8, 3127–3135 (2023). https://doi.org/10.1021/acssensors.3c00818
- X. Huang, D. Zhang, A high sensitivity and high linearity pressure sensor based on a peninsula-structured diaphragm for low-pressure ranges. Sens. Actuat. A Phys. 216, 176–189 (2014). https://doi.org/10.1016/j.sna.2014.05.031
- N. Bai, L. Wang, Y. Xue, Y. Wang, X. Hou et al., Graded interlocks for iontronic pressure sensors with high sensitivity and high linearity over a broad range. ACS Nano 16, 4338–4347 (2022). https://doi.org/10.1021/acsnano.1c10535
- S.R.A. Ruth, V.R. Feig, H. Tran, Z. Bao, Microengineering pressure sensor active layers for improved performance. Adv. Funct. Mater. 30, 2003491 (2020). https://doi.org/10.1002/adfm.202003491
- Z. Luo, J. Chen, Z. Zhu, L. Li, Y. Su et al., High-resolution and high-sensitivity flexible capacitive pressure sensors enhanced by a transferable electrode array and a micropillar-PVDF film. ACS Appl. Mater. Interfaces 13, 7635–7649 (2021). https://doi.org/10.1021/acsami.0c23042
- Y. Li, J. Long, Y. Chen, Y. Huang, N. Zhao, Crosstalk-free, high-resolution pressure sensor arrays enabled by high-throughput laser manufacturing. Adv. Mater. 34, e2200517 (2022). https://doi.org/10.1002/adma.202200517
- J. Lee, S. Kim, S. Park, J. Lee, W. Hwang et al., An artificial tactile neuron enabling spiking representation of stiffness and disease diagnosis. Adv. Mater. 34, e2201608 (2022). https://doi.org/10.1002/adma.202201608
- J. Zhu, X. Zhang, M. Wang, R. Wang, P. Chen et al., An artificial spiking nociceptor integrating pressure sensors and memristors. IEEE Electron Device Lett. 43, 962–965 (2022). https://doi.org/10.1109/led.2022.3167421
- Y. Wang, Y. Gong, S. Huang, X. Xing, Z. Lv et al., Memristor-based biomimetic compound eye for real-time collision detection. Nat. Commun. 12, 5979 (2021). https://doi.org/10.1038/s41467-021-26314-8
- Y. Wang, M.L. Adam, Y. Zhao, W. Zheng, L. Gao et al., Machine learning-enhanced flexible mechanical sensing. Nano-Micro Lett. 15, 55 (2023). https://doi.org/10.1007/s40820-023-01013-9
- Z. Shi, L. Meng, X. Shi, H. Li, J. Zhang et al., Morphological engineering of sensing materials for flexible pressure sensors and artificial intelligence applications. Nano-Micro Lett. 14, 141 (2022). https://doi.org/10.1007/s40820-022-00874-w
- R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14, 139 (2022). https://doi.org/10.1007/s40820-022-00887-5
- I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3–31 (2000). https://doi.org/10.1016/s0167-7012(00)00201-3
- K. Yamazaki, V.-K. Vo-Ho, D. Bulsara, N. Le, Spiking neural networks and their applications: a review. Brain Sci. 12, 863 (2022). https://doi.org/10.3390/brainsci12070863
- A. Taherkhani, A. Belatreche, Y. Li, G. Cosma, L.P. Maguire et al., A review of learning in biologically plausible spiking neural networks. Neural Netw. 122, 253–272 (2020). https://doi.org/10.1016/j.neunet.2019.09.036
- D. Auge, J. Hille, E. Mueller, A. Knoll, A survey of encoding techniques for signal processing in spiking neural networks. Neural. Process. Lett. 53, 4693–4710 (2021). https://doi.org/10.1007/s11063-021-10562-2
- R. Yuan, Q. Duan, P.J. Tiw, G. Li, Z. Xiao et al., A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system. Nat. Commun. 13, 3973 (2022). https://doi.org/10.1038/s41467-022-31747-w
- G. Tanaka, T. Yamane, J.B. Héroux, R. Nakane, N. Kanazawa et al., Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). https://doi.org/10.1016/j.neunet.2019.03.005
- M. Lukoševičius, H. Jaeger, Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009). https://doi.org/10.1016/j.cosrev.2009.03.005
- Z. Qi, L. Mi, H. Qian, W. Zheng, Y. Guo et al., Physical Reservoir computing based on nanoscale materials and devices. Adv. Funct. Mater. 33, 2306149 (2023). https://doi.org/10.1002/adfm.202306149
- J. Cao, X. Zhang, H. Cheng, J. Qiu, X. Liu et al., Emerging dynamic memristors for neuromorphic reservoir computing. Nanoscale 14, 289–298 (2022). https://doi.org/10.1039/d1nr06680c
- Z. Wang, Y. Ma, F. Cheng, L. Yang, Review of pulse-coupled neural networks. Image Vis. Comput. 28, 5–13 (2010). https://doi.org/10.1016/j.imavis.2009.06.007
- M.M. Subashini, S.K. Sahoo, Pulse coupled neural networks and its applications. Expert Syst. Appl. 41, 3965–3974 (2014). https://doi.org/10.1016/j.eswa.2013.12.027
- Q. Wu, B. Dang, C. Lu, G. Xu, G. Yang et al., Spike encoding with optic sensory neurons enable a pulse coupled neural network for ultraviolet image segmentation. Nano Lett. 20, 8015–8023 (2020). https://doi.org/10.1021/acs.nanolett.0c02892
- M.N. Baliki, A.V. Apkarian, Nociception, pain, negative moods, and behavior selection. Neuron 87, 474–491 (2015). https://doi.org/10.1016/j.neuron.2015.06.005
- B. Frias, A. Merighi, Capsaicin, nociception and pain. Molecules 21, 797 (2016). https://doi.org/10.3390/molecules21060797
- D. Julius, A.I. Basbaum, Molecular mechanisms of nociception. Nature 413, 203–210 (2001). https://doi.org/10.1038/35093019
- E. St. John Smith, Advances in understanding nociception and neuropathic pain. J. Neurol. 265, 231–238 (2018). https://doi.org/10.1007/s00415-017-8641-6
- J.H. Yoon, Z. Wang, K.M. Kim, H. Wu, V. Ravichandran et al., An artificial nociceptor based on a diffusive memristor. Nat. Commun. 9, 417 (2018). https://doi.org/10.1038/s41467-017-02572-3
- Y. Kim, Y.J. Kwon, D.E. Kwon, K.J. Yoon, J.H. Yoon et al., Nociceptive memristor. Adv. Mater. 30, 1704320 (2018). https://doi.org/10.1002/adma.201704320
- F.-D. Wang, M.-X. Yu, X.-D. Chen, J. Li, Z.-C. Zhang et al., Optically modulated dual-mode memristor arrays based on core-shell CsPbBr3@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware. SmartMat 4, e1135 (2023). https://doi.org/10.1002/smm2.1135
- A. Vahidi, A. Eskandarian, Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans. Intell. Transp. Syst. 4, 143–153 (2003). https://doi.org/10.1109/TITS.2003.821292
- J. Dahl, G.R. de Campos, C. Olsson, J. Fredriksson, Collision avoidance: a literature review on threat-assessment techniques. IEEE Trans. Intell. Veh. 4, 101–113 (2019). https://doi.org/10.1109/TIV.2018.2886682
- D. Jayachandran, A. Oberoi, A. Sebastian, T.H. Choudhury, B. Shankar et al., A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector. Nat. Electron. 3, 646–655 (2020). https://doi.org/10.1038/s41928-020-00466-9
- F. Gabbiani, H.G. Krapp, C. Koch, G. Laurent, Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320–324 (2002). https://doi.org/10.1038/nature01190
- F. Gabbiani, H.G. Krapp, G. Laurent, Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999). https://doi.org/10.1523/JNEUROSCI.19-03-01122.1999
- A. Mukhtar, L. Xia, T.B. Tang, Vehicle detection techniques for collision avoidance systems: a review. IEEE Trans. Intell. Transp. Syst. 16, 2318–2338 (2015). https://doi.org/10.1109/TITS.2015.2409109
- J.N. Yasin, S.A.S. Mohamed, M.-H. Haghbayan, J. Heikkonen, H. Tenhunen et al., Unmanned aerial vehicles (UAVs): collision avoidance systems and approaches. IEEE Access 8, 105139–105155 (2020). https://doi.org/10.1109/ACCESS.2020.3000064
- Y. Pei, L. Yan, Z. Wu, J. Lu, J. Zhao et al., Artificial visual perception nervous system based on low-dimensional material photoelectric memristors. ACS Nano 15, 17319–17326 (2021). https://doi.org/10.1021/acsnano.1c04676
- Z. Li, Z. Li, W. Tang, J. Yao, Z. Dou et al., Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system. Nat. Commun. 15, 7275 (2024). https://doi.org/10.1038/s41467-024-51609-x
- Y.K. Choi, N. Lindert, P. Xuan, S. Tang, D. Ha et al., Sub-20 nm CMOS FinFET technologies. In: International Electron Devices Meeting. Technical Digest. December 2–5, 2001, Washington, DC, USA. IEEE, (2002), 19.1.1–19.1.4.
- H. Lee, L.E. Yu, S.W. Ryu, J.-W. Han, K. Jeon et al., Sub-5nm all-around gate FinFET for ultimate scaling. In: 2006 Symposium on VLSI Technology, 2006. Digest of Technical Papers. June 13-15, 2006, Honolulu, HI, USA. IEEE, (2006), pp. 58–59.
- R. Wang, F. Li, D. Li, C. Wang, Y. Tang et al., 1-phototransistor-1-threshold switching optoelectronic neuron for in-sensor compression via spiking neuron network. In: 2023 International electron devices meeting (IEDM). December 9–13, 2023, San Francisco, CA, USA. IEEE, (2023), pp. 1–4.
- Y. Zhang, P. Qu, Y. Ji, W. Zhang, G. Gao et al., A system hierarchy for brain-inspired computing. Nature 586, 378–384 (2020). https://doi.org/10.1038/s41586-020-2782-y
- Y. Cao, B. Xu, B. Li, H. Fu, Advanced design of soft robots with artificial intelligence. Nano-Micro Lett. 16, 214 (2024). https://doi.org/10.1007/s40820-024-01423-3
References
H.A. Elmarakeby, J. Hwang, R. Arafeh, J. Crowdis, S. Gang et al., Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021). https://doi.org/10.1038/s41586-021-03922-4
S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou et al., Dense reinforcement learning for safety validation of autonomous vehicles. Nature 615, 620–627 (2023). https://doi.org/10.1038/s41586-023-05732-2
Y.-G. Ham, J.-H. Kim, S.-K. Min, D. Kim, T. Li et al., Anthropogenic fingerprints in daily precipitation revealed by deep learning. Nature 622, 301–307 (2023). https://doi.org/10.1038/s41586-023-06474-x
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
N. Ratledge, G. Cadamuro, B. de la Cuesta, M. Stigler, M. Burke, Using machine learning to assess the livelihood impact of electricity access. Nature 611, 491–495 (2022). https://doi.org/10.1038/s41586-022-05322-8
R. Nair, Evolution of memory architecture. Proc. IEEE 103, 1331–1345 (2015). https://doi.org/10.1109/JPROC.2015.2435018
S. Yu, Neuro-inspired computing with emerging nonvolatile memorys. Proc. IEEE 106, 260–285 (2018). https://doi.org/10.1109/JPROC.2018.2790840
X. Zou, S. Xu, X. Chen, L. Yan, Y. Han, Breaking the von Neumann bottleneck: architecture-level processing-in-memory technology. Sci. China Inf. Sci. 64, 160404 (2021). https://doi.org/10.1007/s11432-020-3227-1
C.D. Wright, P. Hosseini, J.A.V. Diosdado, Beyond von-Neumann computing with nanoscale phase-change memory devices. Adv. Funct. Mater. 23, 2248–2254 (2013). https://doi.org/10.1002/adfm.201202383
A. Sebastian, M. Le Gallo, R. Khaddam-Aljameh, E. Eleftheriou, Memory devices and applications for in-memory computing. Nat. Nanotechnol. 15, 529–544 (2020). https://doi.org/10.1038/s41565-020-0655-z
W. Wan, R. Kubendran, C. Schaefer, S.B. Eryilmaz, W. Zhang et al., A compute-in-memory chip based on resistive random-access memory. Nature 608, 504–512 (2022). https://doi.org/10.1038/s41586-022-04992-8
J.-Q. Yang, R. Wang, Y. Ren, J.-Y. Mao, Z.-P. Wang et al., Neuromorphic engineering: from biological to spike-based hardware nervous systems. Adv. Mater. 32, 2003610 (2020). https://doi.org/10.1002/adma.202003610
J. Tang, F. Yuan, X. Shen, Z. Wang, M. Rao et al., Bridging biological and artificial neural networks with emerging neuromorphic devices: fundamentals, progress, and challenges. Adv. Mater. 31, e1902761 (2019). https://doi.org/10.1002/adma.201902761
K. Roy, A. Jaiswal, P. Panda, Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019). https://doi.org/10.1038/s41586-019-1677-2
D. Kumar, H. Li, D.D. Kumbhar, M.K. Rajbhar, U.K. Das et al., Highly efficient back-end-of-line compatible flexible Si-based optical memristive crossbar array for edge neuromorphic physiological signal processing and bionic machine vision. Nano-Micro Lett. 16, 238 (2024). https://doi.org/10.1007/s40820-024-01456-8
Y. Sun, H. Wang, D. Xie, Recent advance in synaptic plasticity modulation techniques for neuromorphic applications. Nano-Micro Lett. 16, 211 (2024). https://doi.org/10.1007/s40820-024-01445-x
K.C. Kwon, J.H. Baek, K. Hong, S.Y. Kim, H.W. Jang, Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano-Micro Lett. 14, 58 (2022). https://doi.org/10.1007/s40820-021-00784-3
C. Tan, M. Šarlija, N. Kasabov, Spiking neural networks: background, recent development and the NeuCube architecture. Neural. Process. Lett. 52, 1675–1701 (2020). https://doi.org/10.1007/s11063-020-10322-8
J.L. Lobo, J. Del Ser, A. Bifet, N. Kasabov, Spiking neural networks and online learning: an overview and perspectives. Neural Netw. 121, 88–100 (2020). https://doi.org/10.1016/j.neunet.2019.09.004
K. He, C. Wang, Y. He, J. Su, X. Chen, Artificial neuron devices. Chem. Rev. 123, 13796–13865 (2023). https://doi.org/10.1021/acs.chemrev.3c00527
P.A. Merolla, J.V. Arthur, R. Alvarez-Icaza, A.S. Cassidy, J. Sawada et al., A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014). https://doi.org/10.1126/science.1254642
M. Davies, N. Srinivasa, T.-H. Lin, G. Chinya, Y. Cao et al., Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018). https://doi.org/10.1109/MM.2018.112130359
S. Choi, J. Yang, G. Wang, Emerging memristive artificial synapses and neurons for energy-efficient neuromorphic computing. Adv. Mater. 32, e2004659 (2020). https://doi.org/10.1002/adma.202004659
G. Lee, J.-H. Baek, F. Ren, S.J. Pearton, G.-H. Lee et al., Artificial neuron and synapse devices based on 2D materials. Small 17, 2100640 (2021). https://doi.org/10.1002/smll.202100640
N.K. Upadhyay, H. Jiang, Z. Wang, S. Asapu, Q. Xia et al., Emerging memory devices for neuromorphic computing. Adv. Mater. Technol. 4, 1800589 (2019). https://doi.org/10.1002/admt.201800589
J. Zhu, T. Zhang, Y. Yang, R. Huang, A comprehensive review on emerging artificial neuromorphic devices. Appl. Phys. Rev. 7, 011312 (2020). https://doi.org/10.1063/1.5118217
Q. Duan, Z. Jing, X. Zou, Y. Wang, K. Yang et al., Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks. Nat. Commun. 11, 3399 (2020). https://doi.org/10.1038/s41467-020-17215-3
Z. Wang, S. Joshi, S. Savel’ev, W. Song, R. Midya et al., Fully memristive neural networks for pattern classification with unsupervised learning. Nat. Electron. 1, 137–145 (2018). https://doi.org/10.1038/s41928-018-0023-2
R.H. Walden, Analog-to-digital converter survey and analysis. IEEE J. Sel. Areas Commun. 17, 539–550 (1999). https://doi.org/10.1109/49.761034
Y. Kim, A. Chortos, W. Xu, Y. Liu, J.Y. Oh et al., A bioinspired flexible organic artificial afferent nerve. Science 360, 998–1003 (2018). https://doi.org/10.1126/science.aao0098
S. Qu, L. Sun, S. Zhang, J. Liu, Y. Li et al., An artificially-intelligent Cornea with tactile sensation enables sensory expansion and interaction. Nat. Commun. 14, 7181 (2023). https://doi.org/10.1038/s41467-023-42240-3
C. Jiang, H. Xu, L. Yang, J. Liu, Y. Li et al., Neuromorphic antennal sensory system. Nat. Commun. 15, 2109 (2024). https://doi.org/10.1038/s41467-024-46393-7
L. Chen, C. Wen, S.-L. Zhang, Z.L. Wang, Z.-B. Zhang, Artificial tactile peripheral nervous system supported by self-powered transducers. Nano Energy 82, 105680 (2021). https://doi.org/10.1016/j.nanoen.2020.105680
Y. Lee, T.-W. Lee, Organic synapses for neuromorphic electronics: from brain-inspired computing to sensorimotor nervetronics. Acc. Chem. Res. 52, 964–974 (2019). https://doi.org/10.1021/acs.accounts.8b00553
D. Ielmini, S. Ambrogio, Emerging neuromorphic devices. Nanotechnology 31, 092001 (2020). https://doi.org/10.1088/1361-6528/ab554b
Q. Wan, Y. Yang, X. Huang, P. Zhou, L. Chen et al., 2022 roadmap on neuromorphic devices and applications research in China. Neuromorph. Comput. Eng. 2, 042501 (2022). https://doi.org/10.1088/2634-4386/ac7a5a
H. Liu, Y. Qin, H.-Y. Chen, J. Wu, J. Ma et al., Artificial neuronal devices based on emerging materials: neuronal dynamics and applications. Adv. Mater. 35, 2205047 (2023). https://doi.org/10.1002/adma.202205047
J.-K. Han, S.-Y. Yun, S.-W. Lee, J.-M. Yu, Y.-K. Choi, A review of artificial spiking neuron devices for neural processing and sensing. Adv. Funct. Mater. 32, 2204102 (2022). https://doi.org/10.1002/adfm.202204102
J. Bian, Z. Liu, Y. Tao, Z. Wang, X. Zhao et al., Advances in memristor based artificial neuron fabrication-materials, models, and applications. Int. J. Extrem. Manuf. 6, 012002 (2024). https://doi.org/10.1088/2631-7990/acfcf1
Y. Wang, S. Liu, H. Wang, Y. Zhao, X.-D. Zhang, Neuron devices: emerging prospects in neural interfaces and recognition. Microsyst. Nanoeng. 8, 128 (2022). https://doi.org/10.1038/s41378-022-00453-4
Z. Li, W. Tang, B. Zhang, R. Yang, X. Miao, Emerging memristive neurons for neuromorphic computing and sensing. Sci. Technol. Adv. Mater. 24, 2188878 (2023). https://doi.org/10.1080/14686996.2023.2188878
C. Wan, G. Chen, Y. Fu, M. Wang, N. Matsuhisa et al., An artificial sensory neuron with tactile perceptual learning. Adv. Mater. 30, 1801291 (2018). https://doi.org/10.1002/adma.201801291
B. Mu, L. Guo, J. Liao, P. Xie, G. Ding et al., Near-infrared artificial synapses for artificial sensory neuron system. Small 17, e2103837 (2021). https://doi.org/10.1002/smll.202103837
C. Wan, P. Cai, X. Guo, M. Wang, N. Matsuhisa et al., An artificial sensory neuron with visual-haptic fusion. Nat. Commun. 11, 4602 (2020). https://doi.org/10.1038/s41467-020-18375-y
H. Long, X. Lin, Y. Wang, H. Mao, Y. Zhu et al., Multifunctional ultraviolet laser induced graphene for flexible artificial sensory neuron. Adv. Mater. Technol. 8, 2201761 (2023). https://doi.org/10.1002/admt.202201761
H. Ye, Z. Liu, H. Han, T. Shi, G. Liao, Lead-free AgBiI4 perovskite artificial synapses for a tactile sensory neuron system with information preprocessing function. Mater. Adv. 3, 7248–7256 (2022). https://doi.org/10.1039/D2MA00675H
C.R. Donnelly, C. Ouyang, R.-R. Ji, How do sensory neurons sense danger signals? Trends Neurosci. 43, 822–838 (2020). https://doi.org/10.1016/j.tins.2020.07.008
V.E. Abraira, D.D. Ginty, The sensory neurons of touch. Neuron 79, 618–639 (2013). https://doi.org/10.1016/j.neuron.2013.07.051
D. Usoskin, A. Furlan, S. Islam, H. Abdo, P. Lönnerberg et al., Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015). https://doi.org/10.1038/nn.3881
F.A. Pinho-Ribeiro, W.A. Verri Jr., I.M. Chiu, Nociceptor sensory neuron-immune interactions in pain and inflammation. Trends Immunol. 38, 5–19 (2017). https://doi.org/10.1016/j.it.2016.10.001
N.M. Dalesio, S.F. Barreto Ortiz, J.L. Pluznick, D.E. Berkowitz, Olfactory, taste, and photo sensory receptors in non-sensory organs: it just makes sense. Front. Physiol. 9, 1673 (2018). https://doi.org/10.3389/fphys.2018.01673
X. Ren, T. Léveillard, Modulating antioxidant systems as a therapeutic approach to retinal degeneration. Redox Biol. 57, 102510 (2022). https://doi.org/10.1016/j.redox.2022.102510
H.E. Grossniklaus, E.E. Geisert, J.M. Nickerson, Introduction to the Retina, in Molecular biology of eye disease. ed. by J.F. Hejtmancik, J.M. Nickerson (Academic Press, Cambridge, 2015), pp.383–396
K.-W. Yau, R.C. Hardie, Phototransduction motifs and variations. Cell 139, 246–264 (2009). https://doi.org/10.1016/j.cell.2009.09.029
G.L. Fain, R. Hardie, S.B. Laughlin, Phototransduction and the evolution of photoreceptors. Curr. Biol. 20, R114–R124 (2010). https://doi.org/10.1016/j.cub.2009.12.006
V.P. Pandiyan, A. Maloney-Bertelli, J.A. Kuchenbecker, K.C. Boyle, T. Ling et al., The optoretinogram reveals the primary steps of phototransduction in the living human eye. Sci. Adv. 6, eabc124 (2020). https://doi.org/10.1126/sciadv.abc1124
M.T.H. Do, K.-W. Yau, Intrinsically photosensitive retinal ganglion cells. Physiol. Rev. 90, 1547–1581 (2010). https://doi.org/10.1152/physrev.00013.2010
T.D. Lamb, Evolution of phototransduction, vertebrate photoreceptors and retina. Prog. Retin. Eye Res. 36, 52–119 (2013). https://doi.org/10.1016/j.preteyeres.2013.06.001
S.-H. Woo, V. Lukacs, J.C. de Nooij, D. Zaytseva, C.R. Criddle et al., Piezo2 is the principal mechanotransduction channel for proprioception. Nat. Neurosci. 18, 1756–1762 (2015). https://doi.org/10.1038/nn.4162
S.S. Ranade, S.-H. Woo, A.E. Dubin, R.A. Moshourab, C. Wetzel et al., Piezo2 is the major transducer of mechanical forces for touch sensation in mice. Nature 516, 121–125 (2014). https://doi.org/10.1038/nature13980
J. Wu, A.H. Lewis, J. Grandl, Touch, tension, and transduction—the function and regulation of piezo ion channels. Trends Biochem. Sci. 42, 57–71 (2017). https://doi.org/10.1016/j.tibs.2016.09.004
S.-H. Woo, S. Ranade, A.D. Weyer, A.E. Dubin, Y. Baba et al., Piezo2 is required for merkel-cell mechanotransduction. Nature 509, 622–626 (2014). https://doi.org/10.1038/nature13251
S.-H. Woo, E.A. Lumpkin, A. Patapoutian, Merkel cells and neurons keep in touch. Trends Cell Biol. 25, 74–81 (2015). https://doi.org/10.1016/j.tcb.2014.10.003
S. Maksimovic, M. Nakatani, Y. Baba, A.M. Nelson, K.L. Marshall et al., Epidermal Merkel cells are mechanosensory cells that tune mammalian touch receptors. Nature 509, 617–621 (2014). https://doi.org/10.1038/nature13250
R. Ikeda, M. Cha, J. Ling, Z. Jia, D. Coyle et al., Merkel cells transduce and encode tactile stimuli to drive Aβ-afferent impulses. Cell 157, 664–675 (2014). https://doi.org/10.1016/j.cell.2014.02.026
D. Deflorio, M. Di Luca, A.M. Wing, Skin and mechanoreceptor contribution to tactile input for perception: a review of simulation models. Front. Hum. Neurosci. 16, 862344 (2022). https://doi.org/10.3389/fnhum.2022.862344
A. Handler, D.D. Ginty, The mechanosensory neurons of touch and their mechanisms of activation. Nat. Rev. Neurosci. 22, 521–537 (2021). https://doi.org/10.1038/s41583-021-00489-x
M.J. Caterina, How do you feel? A warm and touching 2021 Nobel tribute. J. Clin. Invest. 131, e156587 (2021). https://doi.org/10.1172/JCI156587
R.J. Schepers, M. Ringkamp, Thermoreceptors and thermosensitive afferents. Neurosci. Biobehav. Rev. 34, 177–184 (2010). https://doi.org/10.1016/j.neubiorev.2009.10.003
R. Latorre, S. Brauchi, R. Madrid, P. Orio, A cool channel in cold transduction. Physiology 26, 273–285 (2011). https://doi.org/10.1152/physiol.00004.2011
K. Lezama-García, D. Mota-Rojas, A.M.F. Pereira, J. Martínez-Burnes, M. Ghezzi et al., Transient receptor potential (TRP) and thermoregulation in animals: structural biology and neurophysiological aspects. Animals 12, 106 (2022). https://doi.org/10.3390/ani12010106
D.M. Bautista, J. Siemens, J.M. Glazer, P.R. Tsuruda, A.I. Basbaum et al., The menthol receptor TRPM8 is the principal detector of environmental cold. Nature 448, 204–208 (2007). https://doi.org/10.1038/nature05910
E. Spekker, T. Körtési, L. Vécsei, TRP channels: recent development in translational research and potential therapeutic targets in migraine. Int. J. Mol. Sci. 24, 700 (2022). https://doi.org/10.3390/ijms24010700
S.A. Gravina, G.L. Yep, M. Khan, Human biology of taste. Ann. Saudi Med. 33, 217–222 (2013). https://doi.org/10.5144/0256-4947.2013.217
J. Chandrashekar, M.A. Hoon, N.J.P. Ryba, C.S. Zuker, The receptors and cells for mammalian taste. Nature 444, 288–294 (2006). https://doi.org/10.1038/nature05401
S.D. Roper, N. Chaudhari, Taste buds: cells, signals and synapses. Nat. Rev. Neurosci. 18, 485–497 (2017). https://doi.org/10.1038/nrn.2017.68
D.A. Yarmolinsky, C.S. Zuker, N.J.P. Ryba, Common sense about taste: from mammals to insects. Cell 139, 234–244 (2009). https://doi.org/10.1016/j.cell.2009.10.001
N. Chaudhari, S.D. Roper, The cell biology of taste. J. Cell Biol. 190, 285–296 (2010). https://doi.org/10.1083/jcb.201003144
A. Taruno, K. Nomura, T. Kusakizako, Z. Ma, O. Nureki et al., Taste transduction and channel synapses in taste buds. Pflugers Arch. 473, 3–13 (2021). https://doi.org/10.1007/s00424-020-02464-4
T.A. Gilbertson, S. Damak, R.F. Margolskee, The molecular physiology of taste transduction. Curr. Opin. Neurobiol. 10, 519–527 (2000). https://doi.org/10.1016/S0959-4388(00)00118-5
B. Lindemann, Receptors and transduction in taste. Nature 413, 219–225 (2001). https://doi.org/10.1038/35093032
Y. Ishimaru, H. Inada, M. Kubota, H. Zhuang, M. Tominaga et al., Transient receptor potential family members PKD1L3 and PKD2L1 form a candidate sour taste receptor. Proc. Natl. Acad. Sci. U.S.A. 103, 12569–12574 (2006). https://doi.org/10.1073/pnas.0602702103
N. Horio, R. Yoshida, K. Yasumatsu, Y. Yanagawa, Y. Ishimaru et al., Sour taste responses in mice lacking PKD channels. PLoS ONE 6, e20007 (2011). https://doi.org/10.1371/journal.pone.0020007
T.M. Nelson, N.D. Lopezjimenez, L. Tessarollo, M. Inoue, A.A. Bachmanov et al., Taste function in mice with a targeted mutation of the pkd1l3 gene. Chem. Senses 35, 565–577 (2010). https://doi.org/10.1093/chemse/bjq070
Y.-H. Tu, A.J. Cooper, B. Teng, R.B. Chang, D.J. Artiga et al., An evolutionarily conserved gene family encodes proton-selective ion channels. Science 359, 1047–1050 (2018). https://doi.org/10.1126/science.aao3264
J. Zhang, H. Jin, W. Zhang, C. Ding, S. O’Keeffe et al., Sour sensing from the tongue to the brain. Cell 179, 392-402.e15 (2019). https://doi.org/10.1016/j.cell.2019.08.031
B. Teng, C.E. Wilson, Y.-H. Tu, N.R. Joshi, S.C. Kinnamon et al., Cellular and neural responses to sour stimuli require the proton channel Otop1. Curr. Biol. 29, 3647-3656.e5 (2019). https://doi.org/10.1016/j.cub.2019.08.077
S. Boesveldt, V. Parma, The importance of the olfactory system in human well-being, through nutrition and social behavior. Cell Tissue Res. 383, 559–567 (2021). https://doi.org/10.1007/s00441-020-03367-7
M. Melis, I.T. Barbarossa, G. Sollai, The implications of taste and olfaction in nutrition and health. Nutrients 15, 3412 (2023). https://doi.org/10.3390/nu15153412
K. Touhara, L.B. Vosshall, Sensing odorants and pheromones with chemosensory receptors. Annu. Rev. Physiol. 71, 307–332 (2009). https://doi.org/10.1146/annurev.physiol.010908.163209
G.V. Ronnett, C. Moon, G proteins and olfactory signal transduction. Annu. Rev. Physiol. 64, 189–222 (2002). https://doi.org/10.1146/annurev.physiol.64.082701.102219
U.B. Kaupp, Olfactory signalling in vertebrates and insects: differences and commonalities. Nat. Rev. Neurosci. 11, 188–200 (2010). https://doi.org/10.1038/nrn2789
N. Kang, J. Koo, Olfactory receptors in non-chemosensory tissues. BMB Rep. 45, 612–622 (2012). https://doi.org/10.5483/bmbrep.2012.45.11.232
F. Abbas, F. Vinberg, Transduction and adaptation mechanisms in the Cilium or microvilli of photoreceptors and olfactory receptors from insects to humans. Front. Cell. Neurosci. 15, 662453 (2021). https://doi.org/10.3389/fncel.2021.662453
M. Schwander, B. Kachar, U. Müller, The cell biology of hearing. J. Cell Biol. 190, 9–20 (2010). https://doi.org/10.1083/jcb.201001138
S. Jia, P. Dallos, D.Z.Z. He, Mechanoelectric transduction of adult inner hair cells. J. Neurosci. 27, 1006–1014 (2007). https://doi.org/10.1523/JNEUROSCI.5452-06.2007
W. Zheng, J.R. Holt, The mechanosensory transduction machinery in inner ear hair cells. Annu. Rev. Biophys. 50, 31–51 (2021). https://doi.org/10.1146/annurev-biophys-062420-081842
P. Kazmierczak, U. Müller, Sensing sound: molecules that orchestrate mechanotransduction by hair cells. Trends Neurosci. 35, 220–229 (2012). https://doi.org/10.1016/j.tins.2011.10.007
P.G. Gillespie, U. Müller, Mechanotransduction by hair cells: models, molecules, and mechanisms. Cell 139, 33–44 (2009). https://doi.org/10.1016/j.cell.2009.09.010
Y.C. Wu, A.J. Ricci, R. Fettiplace, Two components of transducer adaptation in auditory hair cells. J. Neurophysiol. 82, 2171–2181 (1999). https://doi.org/10.1152/jn.1999.82.5.2171
J.A. Assad, D.P. Corey, An active motor model for adaptation by vertebrate hair cells. J. Neurosci. 12, 3291–3309 (1992). https://doi.org/10.1523/JNEUROSCI.12-09-03291.1992
J. Howard, A.J. Hudspeth, Mechanical relaxation of the hair bundle mediates adaptation in mechanoelectrical transduction by the bullfrog’s saccular hair cell. Proc. Natl. Acad. Sci. U.S.A. 84, 3064–3068 (1987). https://doi.org/10.1073/pnas.84.9.3064
M. Pascal, D. Bozovic, Y. Choe, A.J. Hudspeth, Spontaneous oscillation by hair bundles of the bullfrog’s sacculus. J. Neurosci. 23, 4533 (2003). https://doi.org/10.1523/JNEUROSCI.23-11-04533.2003
V. Torre, J.F. Ashmore, T.D. Lamb, A. Menini, Transduction and adaptation in sensory receptor cells. J. Neurosci. 15, 7757–7768 (1995). https://doi.org/10.1523/JNEUROSCI.15-12-07757.1995
X. Zhang, W. Wang, Q. Liu, X. Zhao, J. Wei et al., An artificial neuron based on a threshold switching memristor. IEEE Electron Device Lett. 39, 308–311 (2018). https://doi.org/10.1109/LED.2017.2782752
R. Cao, X. Zhang, S. Liu, J. Lu, Y. Wang et al., Compact artificial neuron based on anti-ferroelectric transistor. Nat. Commun. 13, 7018 (2022). https://doi.org/10.1038/s41467-022-34774-9
L. Gao, P.-Y. Chen, S. Yu, NbOx based oscillation neuron for neuromorphic computing. Appl. Phys. Lett. 111, 103503 (2017). https://doi.org/10.1063/1.4991917
D. Lee, M. Kwak, K. Moon, W. Choi, J. Park et al., Various threshold switching devices for integrate and fire neuron applications. Adv. Electron. Mater. 5, 1800866 (2019). https://doi.org/10.1002/aelm.201800866
T. Tuma, A. Pantazi, M. Le Gallo, A. Sebastian, E. Eleftheriou, Stochastic phase-change neurons. Nat. Nanotechnol. 11, 693–699 (2016). https://doi.org/10.1038/nnano.2016.70
A. Sengupta, P. Panda, P. Wijesinghe, Y. Kim, K. Roy, Magnetic tunnel junction mimics stochastic cortical spiking neurons. Sci. Rep. 6, 30039 (2016). https://doi.org/10.1038/srep30039
H. Kalita, A. Krishnaprasad, N. Choudhary, S. Das, D. Dev et al., Artificial neuron using vertical MoS2/graphene threshold switching memristors. Sci. Rep. 9, 53 (2019). https://doi.org/10.1038/s41598-018-35828-z
F. Qian, R.-S. Chen, R. Wang, J. Wang, P. Xie et al., A leaky integrate-and-fire neuron based on hexagonal boron nitride (h-BN) monocrystalline memristor. IEEE Trans. Electron Devices 69, 6049–6056 (2022). https://doi.org/10.1109/TED.2022.3206170
Y. Zhang, L. Chu, W. Li, A fully-integrated memristor chip for edge learning. Nano-Micro Lett. 16, 166 (2024). https://doi.org/10.1007/s40820-024-01368-7
H. Zhou, S. Li, K.-W. Ang, Y.-W. Zhang, Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett. 16, 121 (2024). https://doi.org/10.1007/s40820-024-01335-2
Y. Sun, C. Song, S. Yin, L. Qiao, Q. Wan et al., Design of a controllable redox-diffusive threshold switching memristor. Adv. Electron. Mater. 6, 2000695 (2020). https://doi.org/10.1002/aelm.202000695
J.-H. Cha, S.Y. Yang, J. Oh, S. Choi, S. Park et al., Conductive-bridging random-access memories for emerging neuromorphic computing. Nanoscale 12, 14339–14368 (2020). https://doi.org/10.1039/D0NR01671C
Z. Wang, M. Rao, R. Midya, S. Joshi, H. Jiang et al., Threshold switching: threshold switching of Ag or Cu in dielectrics: materials, mechanism, and applications. Adv. Funct. Mater. 28, 1870036 (2018). https://doi.org/10.1002/adfm.201870036
R. Wang, J.-Q. Yang, J.-Y. Mao, Z.-P. Wang, S. Wu et al., Recent advances of volatile memristors: devices, mechanisms, and applications. Adv. Intell. Syst. 2, 2000055 (2020). https://doi.org/10.1002/aisy.202000055
W. Sun, B. Gao, M. Chi, Q. Xia, J.J. Yang et al., Understanding memristive switching via in situ characterization and device modeling. Nat. Commun. 10, 3453 (2019). https://doi.org/10.1038/s41467-019-11411-6
F. Zahoor, T.Z. Azni Zulkifli, F.A. Khanday, Resistive random access memory (RRAM): an overview of materials, switching mechanism, performance, multilevel cell (mlc) storage, modeling, and applications. Nanoscale Res. Lett. 15, 90 (2020). https://doi.org/10.1186/s11671-020-03299-9
Z. Shen, C. Zhao, Y. Qi, W. Xu, Y. Liu et al., Advances of RRAM devices: resistive switching mechanisms, materials and bionic synaptic application. Nanomaterials 10, 1437 (2020). https://doi.org/10.3390/nano10081437
F. Pan, S. Gao, C. Chen, C. Song, F. Zeng, Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater. Sci. Eng. R. Rep. 83, 1–59 (2014). https://doi.org/10.1016/j.mser.2014.06.002
Y. Zhou, S. Ramanathan, Mott memory and neuromorphic devices. Proc. IEEE 103, 1289–1310 (2015). https://doi.org/10.1109/JPROC.2015.2431914
M.D. Pickett, G. Medeiros-Ribeiro, R.S. Williams, A scalable neuristor built with Mott memristors. Nat. Mater. 12, 114–117 (2013). https://doi.org/10.1038/nmat3510
S. Kumar, J.P. Strachan, R.S. Williams, Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing. Nature 548, 318–321 (2017). https://doi.org/10.1038/nature23307
I. Messaris, T.D. Brown, A.S. Demirkol, A. Ascoli, M.M. Al Chawa et al., NbO2-Mott memristor: a circuit- theoretic investigation. IEEE Trans. Circuits Syst. I Regul. Pap. 68, 4979–4992 (2021). https://doi.org/10.1109/TCSI.2021.3126657
G. Stefanovich, A. Pergament, D. Stefanovich, Electrical switching and Mott transition in VO2. J. Phys. Condens. Matter 12, 8837–8845 (2000). https://doi.org/10.1088/0953-8984/12/41/310
M.M. Qazilbash, M. Brehm, B.G. Chae, P.C. Ho, G.O. Andreev et al., Mott transition in VO2 revealed by infrared spectroscopy and nano-imaging. Science 318, 1750–1753 (2007). https://doi.org/10.1126/science.1150124
H.-T. Kim, B.-G. Chae, D.-H. Youn, S.-L. Maeng, G. Kim et al., Mechanism and observation of Mott transition in VO2-based two- and three-terminal devices. New J. Phys. 6, 52 (2004). https://doi.org/10.1088/1367-2630/6/1/052
P. Wang, A.I. Khan, S. Yu, Cryogenic behavior of NbO2 based threshold switching devices as oscillation neurons. Appl. Phys. Lett. 116, 162108 (2020). https://doi.org/10.1063/5.0006467
J. Woo, P. Wang, S. Yu, Integrated crossbar array with resistive synapses and oscillation neurons. IEEE Electron Device Lett. 40, 1313–1316 (2019). https://doi.org/10.1109/LED.2019.2921656
G. Zhou, Z. Wang, B. Sun, F. Zhou, L. Sun et al., Volatile and nonvolatile memristive devices for neuromorphic computing. Adv. Electron. Mater. 8, 2101127 (2022). https://doi.org/10.1002/aelm.202101127
S. Chen, T. Zhang, S. Tappertzhofen, Y. Yang, I. Valov, Electrochemical-memristor-based artificial neurons and synapses-fundamentals, applications, and challenges. Adv. Mater. 35, e2301924 (2023). https://doi.org/10.1002/adma.202301924
W. Yi, K.K. Tsang, S.K. Lam, X. Bai, J.A. Crowell et al., Biological plausibility and stochasticity in scalable VO2 active memristor neurons. Nat. Commun. 9, 4661 (2018). https://doi.org/10.1038/s41467-018-07052-w
W. Park, G. Kim, J.H. In, H. Rhee, H. Song et al., High amplitude spike generator in Au nanodot-incorporated NbOx Mott memristor. Nano Lett. 23, 5399–5407 (2023). https://doi.org/10.1021/acs.nanolett.2c04599
S.K. Nath, X. Sun, S.K. Nandi, X. Chen, Z. Wang et al., Harnessing metal/oxide interlayer to engineer the memristive response and oscillation dynamics of two-terminal memristors. Adv. Funct. Mater. 33, 2306428 (2023). https://doi.org/10.1002/adfm.202306428
C.-D. Chen, M. Matloubian, R. Sundaresan, B.-Y. Mao, C.C. Wei et al., Single-transistor latch in SOI MOSFETs. IEEE Electron Device Lett. 9, 636–638 (1988). https://doi.org/10.1109/55.20420
R.J.T. Bunyan, M.J. Uren, N.J. Thomas, J.R. Davis, Degradation in thin-film SOI MOSFET’s caused by single-transistor latch. IEEE Electron Device Lett. 11, 359–361 (1990). https://doi.org/10.1109/55.62955
J.-W. Jung, J.-K. Han, J.-M. Yu, M.-W. Lee, M.-S. Kim et al., Concealable oscillation-based physical unclonable function with a single-transistor latch. IEEE Electron Device Lett. 43, 1359–1362 (2022). https://doi.org/10.1109/led.2022.3182754
J.-K. Han, M.-W. Lee, J.-M. Yu, Y.-K. Choi, A single transistor-based threshold switch for a bio-inspired reconfigurable threshold logic. Adv. Electron. Mater. 7, 2100117 (2021). https://doi.org/10.1002/aelm.202100117
J.K. Han, J.W. Lee, Y. Kim, Y.B. Kim, S.Y. Yun et al., 3D neuromorphic hardware with single thin-film transistor synapses over single thin-body transistor neurons by monolithic vertical integration. Adv. Sci. 10, e2302380 (2023). https://doi.org/10.1002/advs.202302380
J.K. Han, D.M. Geum, M.W. Lee, J.M. Yu, S.K. Kim et al., Bioinspired photoresponsive single transistor neuron for a neuromorphic visual system. Nano Lett. 20, 8781–8788 (2020). https://doi.org/10.1021/acs.nanolett.0c03652
V.K. Sangwan, H.-S. Lee, H. Bergeron, I. Balla, M.E. Beck et al., Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide. Nature 554, 500–504 (2018). https://doi.org/10.1038/nature25747
Y. Zheng, H. Ravichandran, T.F. Schranghamer, N. Trainor, J.M. Redwing et al., Hardware implementation of Bayesian network based on two-dimensional memtransistors. Nat. Commun. 13, 5578 (2022). https://doi.org/10.1038/s41467-022-33053-x
A. Wali, S. Das, Two-dimensional memtransistors for non-von Neumann computing: progress and challenges. Adv. Funct. Mater. 34, 2308129 (2024). https://doi.org/10.1002/adfm.202308129
H.-S. Lee, V.K. Sangwan, W.A.G. Rojas, H. Bergeron, H.Y. Jeong et al., Dual-gated MoS2 memtransistor crossbar array. Adv. Funct. Mater. 30, 2003683 (2020). https://doi.org/10.1002/adfm.202003683
X. Yan, J.H. Qian, V.K. Sangwan, M.C. Hersam, Progress and challenges for memtransistors in neuromorphic circuits and systems. Adv. Mater. 34, e2108025 (2022). https://doi.org/10.1002/adma.202108025
A. Dodda, N. Trainor, J.M. Redwing, S. Das, All-in-one, bio-inspired, and low-power crypto engines for near-sensor security based on two-dimensional memtransistors. Nat. Commun. 13, 3587 (2022). https://doi.org/10.1038/s41467-022-31148-z
L. Wang, W. Liao, S.L. Wong, Z.G. Yu, S. Li et al., Artificial synapses based on multiterminal memtransistors for neuromorphic application. Adv. Funct. Mater. 29, 1901106 (2019). https://doi.org/10.1002/adfm.201901106
H. Li, J. Hu, Y. Zhang, A. Chen, J. Zhou et al., Single-transistor optoelectronic spiking neuron with optogenetics-inspired spatiotemporal dynamics. Adv. Funct. Mater. 34, 2314456 (2024). https://doi.org/10.1002/adfm.202314456
J. Jadwiszczak, D. Keane, P. Maguire, C.P. Cullen, Y. Zhou et al., MoS2 memtransistors fabricated by localized helium ion beam irradiation. ACS Nano 13, 14262–14273 (2019). https://doi.org/10.1021/acsnano.9b07421
H. Li, J. Hu, A. Chen, C. Wang, L. Chen et al., Single-transistor neuron with excitatory-inhibitory spatiotemporal dynamics applied for neuronal oscillations. Adv. Mater. 34, e2207371 (2022). https://doi.org/10.1002/adma.202207371
Y. Chen, D. Li, H. Ren, Y. Tang, K. Liang et al., Highly linear and symmetric synaptic memtransistors based on polarization switching in two-dimensional ferroelectric semiconductors. Small 18, e2203611 (2022). https://doi.org/10.1002/smll.202203611
K. Liu, T. Zhang, B. Dang, L. Bao, L. Xu et al., An optoelectronic synapse based on α-In2Se3 with controllable temporal dynamics for multimode and multiscale reservoir computing. Nat. Electron. 5, 761–773 (2022). https://doi.org/10.1038/s41928-022-00847-2
S. Chun, J.-S. Kim, Y. Yoo, Y. Choi, S.J. Jung et al., An artificial neural tactile sensing system. Nat. Electron. 4, 429–438 (2021). https://doi.org/10.1038/s41928-021-00585-x
V. Amoli, S.Y. Kim, J.S. Kim, H. Choi, J. Koo et al., Biomimetics for high-performance flexible tactile sensors and advanced artificial sensory systems. J. Mater. Chem. C 7, 14816–14844 (2019). https://doi.org/10.1039/c9tc05392a
L. Shan, H. Zeng, Y. Liu, X. Zhang, E. Li et al., Artificial tactile sensing system with photoelectric output for high accuracy haptic texture recognition and parallel information processing. Nano Lett. 22, 7275–7283 (2022). https://doi.org/10.1021/acs.nanolett.2c02995
K. Lee, S. Jang, K.L. Kim, M. Koo, C. Park et al., Artificially intelligent tactile ferroelectric skin. Adv. Sci. 7, 2001662 (2020). https://doi.org/10.1002/advs.202001662
X. Zhang, Y. Zhuo, Q. Luo, Z. Wu, R. Midya et al., An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11, 51 (2020). https://doi.org/10.1038/s41467-019-13827-6
F. Li, R. Wang, C. Song, M. Zhao, H. Ren et al., A skin-inspired artificial mechanoreceptor for tactile enhancement and integration. ACS Nano 15, 16422–16431 (2021). https://doi.org/10.1021/acsnano.1c05836
J. Wen, L. Zhang, Y.-Z. Wang, X. Guo, Artificial tactile perception system based on spiking tactile neurons and spiking neural networks. ACS Appl. Mater. Interfaces 16, 998–1004 (2024). https://doi.org/10.1021/acsami.3c12244
J. Li, R. Bao, J. Tao, Y. Peng, C. Pan, Recent progress in flexible pressure sensor arrays: from design to applications. J. Mater. Chem. C 6, 11878–11892 (2018). https://doi.org/10.1039/C8TC02946F
Y. Duan, S. He, J. Wu, B. Su, Y. Wang, Recent progress in flexible pressure sensor arrays. Nanomaterials 12, 2495 (2022). https://doi.org/10.3390/nano12142495
S.L. Fang, C.Y. Han, Z.R. Han, B. Ma, Y.L. Cui et al., An artificial spiking afferent neuron system achieved by 1M1S for neuromorphic computing. IEEE Trans. Electron Devices 69, 2346–2352 (2022). https://doi.org/10.1109/TED.2022.3159270
W. Ye, J. Lin, X. Zhang, Q. Lian, Y. Liu et al., Self-powered perception system based on triboelectric nanogenerator and artificial neuron for fast-speed multilevel feature recognition. Nano Energy 100, 107525 (2022). https://doi.org/10.1016/j.nanoen.2022.107525
A. Delorme, L. Perrinet, S.J. Thorpe, Networks of integrate-and-fire neurons using rank order coding B: spike timing dependent plasticity and emergence of orientation selectivity. Neurocomputing 38, 539–545 (2001). https://doi.org/10.1016/S0925-2312(01)00403-9
H. Tang, D. Cho, D. Lew, T. Kim, J. Park, Rank order coding based spiking convolutional neural network architecture with energy-efficient membrane voltage updates. Neurocomputing 407, 300–312 (2020). https://doi.org/10.1016/j.neucom.2020.05.031
S. Wang, J.Y. Oh, J. Xu, H. Tran, Z. Bao, Skin-inspired electronics: an emerging paradigm. Acc. Chem. Res. 51, 1033–1045 (2018). https://doi.org/10.1021/acs.accounts.8b00015
Y. Liu, M. Pharr, G.A. Salvatore, Lab-on-skin: a review of flexible and stretchable electronics for wearable health monitoring. ACS Nano 11, 9614–9635 (2017). https://doi.org/10.1021/acsnano.7b04898
J.C. Yang, J. Mun, S.Y. Kwon, S. Park, Z. Bao et al., Electronic skin: recent progress and future prospects for skin-attachable devices for health monitoring, robotics, and prosthetics. Adv. Mater. 31, e1904765 (2019). https://doi.org/10.1002/adma.201904765
S.L. Fang, C.Y. Han, W. Liu, Z.R. Han, B. Ma et al., A bioinspired flexible artificial mechanoreceptor based on VO2 insulator-metal transition memristor. J. Alloys Compd. 911, 165096 (2022). https://doi.org/10.1016/j.jallcom.2022.165096
J.-K. Han, I.-W. Tcho, S.-B. Jeon, J.-M. Yu, W.-G. Kim et al., Self-powered artificial mechanoreceptor based on triboelectrification for a neuromorphic tactile system. Adv. Sci. 9, e2201831 (2022). https://doi.org/10.1002/advs.202201831
J. Benda, Neural adaptation. Curr. Biol. 31, R110–R116 (2021). https://doi.org/10.1016/j.cub.2020.11.054
P.V. Watkins, D.L. Barbour, Specialized neuronal adaptation for preserving input sensitivity. Nat. Neurosci. 11, 1259–1261 (2008). https://doi.org/10.1038/nn.2201
J. Tuthill, R. Wilson, Mechanosensation and adaptive motor control in insects. Curr. Biol. 26, R1022–R1038 (2016). https://doi.org/10.1016/j.cub.2016.06.070
P. Delmas, J. Hao, L. Rodat-Despoix, Molecular mechanisms of mechanotransduction in mammalian sensory neurons. Nat. Rev. Neurosci. 12, 139–153 (2011). https://doi.org/10.1038/nrn2993
Z. Xie, X. Zhu, W. Wang, Z. Guo, Y. Zhang et al., Temporal pattern coding in ionic memristor-based spiking neurons for adaptive tactile perception. Adv. Electron. Mater. 8, 2200334 (2022). https://doi.org/10.1002/aelm.202200334
M.-W. Lee, J.-K. Han, G.-J. Yun, J.-M. Yu, G.-B. Lee et al., A temperature sensor with a thermillator. IEEE Electron Device Lett. 42, 1654–1657 (2021). https://doi.org/10.1109/led.2021.3111622
J. Wu, W. Ye, J. Lin, X. Zhang, B. Zeng et al., Temperature regulated artificial neuron based on memristor. IEEE Electron Device Lett. 43, 2001–2004 (2022). https://doi.org/10.1109/led.2022.3206796
K. Shi, S. Heng, X. Wang, S. Liu, H. Cui et al., An oxide based spiking thermoreceptor for low-power thermography edge detection. IEEE Electron Device Lett. 43, 2196–2199 (2022). https://doi.org/10.1109/LED.2022.3215693
C.Y. Han, Z.R. Han, S.L. Fang, S.Q. Fan, J.Q. Yin et al., Characterization and modelling of flexible VO2 Mott memristor for the artificial spiking warm receptor. Adv. Mater. Interfaces 9, 2200394 (2022). https://doi.org/10.1002/admi.202200394
J. Zhao, L. Tong, J. Niu, Z. Fang, Y. Pei et al., A bidirectional thermal sensory leaky integrate-and-fire (LIF) neuron model based on bipolar NbOx volatile threshold devices with ultra-low operating current. Nanoscale 15, 17599–17608 (2023). https://doi.org/10.1039/D3NR03034B
Y. Luo, L. Pu, M. Zuba, Z. Peng, J.-H. Cui, Challenges and opportunities of underwater cognitive acoustic networks. IEEE Trans. Emerg. Top. Comput. 2, 198–211 (2014). https://doi.org/10.1109/TETC.2014.2310457
B. Mishachandar, S. Vairamuthu, An underwater cognitive acoustic network strategy for efficient spectrum utilization. Appl. Acoust. 175, 107861 (2021). https://doi.org/10.1016/j.apacoust.2020.107861
K.M.H. Badami, S. Lauwereins, W. Meert, M. Verhelst, A 90 nm CMOS, 6 μW power-proportional acoustic sensing frontend for voice activity detection. IEEE J. Solid State Circuits 51, 291–302 (2016). https://doi.org/10.1109/JSSC.2015.2487276
C.-T. Chiang, C.-Y. Wu, A CMOS digitized silicon condenser microphone for acoustic applications. IEEE Sens. J. 11, 296–304 (2011). https://doi.org/10.1109/JSEN.2010.2071380
S.-Y. Yun, J.-K. Han, S.-W. Lee, J.-M. Yu, S.-B. Jeon et al., Self-aware artificial auditory neuron with a triboelectric sensor for spike-based neuromorphic hardware. Nano Energy 109, 108322 (2023). https://doi.org/10.1016/j.nanoen.2023.108322
Y. Tahara, K. Toko, Electronic tongues—a review. IEEE Sens. J. 13, 3001–3011 (2013). https://doi.org/10.1109/JSEN.2013.2263125
A. Riul Jr., C.A.R. Dantas, C.M. Miyazaki, O.N. Oliveira Jr., Recent advances in electronic tongues. Anal. Bioanal. Chem. 135, 2481 (2010). https://doi.org/10.1039/c0an00292e
Y. Vlasov, A. Legin, A. Rudnitskaya, Electronic tongues and their analytical application. Anal. Bioanal. Chem. 373, 136–146 (2002). https://doi.org/10.1007/s00216-002-1310-2
J.-K. Han, S.-C. Park, J.-M. Yu, J.-H. Ahn, Y.-K. Choi, A bioinspired artificial gustatory neuron for a neuromorphic based electronic tongue. Nano Lett. 22, 5244–5251 (2022). https://doi.org/10.1021/acs.nanolett.2c01107
S. Ampuero, J.O. Bosset, The electronic nose applied to dairy products: a review. Sens. Actuat. B Chem. 94, 1–12 (2003). https://doi.org/10.1016/S0925-4005(03)00321-6
L. Cheng, Q.-H. Meng, A.J. Lilienthal, P.-F. Qi, Development of compact electronic noses: a review. Meas. Sci. Technol. 32, 062002 (2021). https://doi.org/10.1088/1361-6501/abef3b
F. Röck, N. Barsan, U. Weimar, Electronic nose: current status and future trends. Chem. Rev. 108, 705–725 (2008). https://doi.org/10.1021/cr068121q
A.D. Wilson, M. Baietto, Applications and advances in electronic-nose technologies. Sensors 9, 5099–5148 (2009). https://doi.org/10.3390/s90705099
X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
T. Wang, X.-X. Wang, J. Wen, Z.-Y. Shao, H.-M. Huang et al., A bio-inspired neuromorphic sensory system. Adv. Intell. Syst. 4, 2200047 (2022). https://doi.org/10.1002/aisy.202200047
J.-K. Han, M. Kang, J. Jeong, I. Cho, J.-M. Yu et al., Artificial olfactory neuron for an in-sensor neuromorphic nose. Adv. Sci. 9, e2106017 (2022). https://doi.org/10.1002/advs.202106017
L. Wollmuth, Structure and gating of the glutamate receptor ion channel. Trends Neurosci. 27, 321–328 (2004). https://doi.org/10.1016/j.tins.2004.04.005
M.L. Mayer, Glutamate receptor ion channels. Curr. Opin. Neurobiol. 15, 282–288 (2005). https://doi.org/10.1016/j.conb.2005.05.004
N.C. Spitzer, Activity-dependent neurotransmitter respecification. Nat. Rev. Neurosci. 13, 94–106 (2012). https://doi.org/10.1038/nrn3154
S.L. Foote, R. Freedman, A.P. Oliver, Effects of putative neurotransmitters on neuronal activity in monkey auditory cortex. Brain Res. 86, 229–242 (1975). https://doi.org/10.1016/0006-8993(75)90699-X
C. Zhao, Y. Wang, G. Tang, J. Ru, Z. Zhu et al., Ionic flexible sensors: mechanisms, materials, structures, and applications. Adv. Funct. Mater. 32, 2110417 (2022). https://doi.org/10.1002/adfm.202110417
V. Amoli, J.S. Kim, S.Y. Kim, J. Koo, Y.S. Chung et al., Ionic tactile sensors for emerging human-interactive technologies: a review of recent progress. Adv. Funct. Mater. 30, 1904532 (2020). https://doi.org/10.1002/adfm.201904532
T. Sarkar, K. Lieberth, A. Pavlou, T. Frank, V. Mailaender et al., An organic artificial spiking neuron for in situ neuromorphic sensing and biointerfacing. Nat. Electron. 5, 774–783 (2022). https://doi.org/10.1038/s41928-022-00859-y
A. Classen, T. Heumueller, I. Wabra, J. Gerner, Y. He et al., Revealing hidden UV instabilities in organic solar cells by correlating device and material stability. Adv. Energy Mater. 9, 1902124 (2019). https://doi.org/10.1002/aenm.201902124
H. Aziz, Z.D. Popovic, Degradation phenomena in small-molecule organic light-emitting devices. Chem. Mater. 16, 4522–4532 (2004). https://doi.org/10.1021/cm040081o
S.W. Cho, C. Jo, Y.H. Kim, S.K. Park, Progress of materials and devices for neuromorphic vision sensors. Nano-Micro Lett. 14, 203 (2022). https://doi.org/10.1007/s40820-022-00945-y
S.S. Radhakrishnan, A. Sebastian, A. Oberoi, S. Das, S. Das, A biomimetic neural encoder for spiking neural network. Nat. Commun. 12, 2143 (2021). https://doi.org/10.1038/s41467-021-22332-8
H. Lee, S.W. Cho, S.J. Kim, J. Lee, K.S. Kim et al., Three-terminal ovonic threshold switch (3T-OTS) with tunable threshold voltage for versatile artificial sensory neurons. Nano Lett. 22, 733–739 (2022). https://doi.org/10.1021/acs.nanolett.1c04125
F. Wang, F. Hu, M. Dai, S. Zhu, F. Sun et al., Author Correction: a two-dimensional mid-infrared optoelectronic retina enabling simultaneous perception and encoding. Nat. Commun. 14, 7877 (2023). https://doi.org/10.1038/s41467-023-37623-5
J. Zhao, Y. Ran, Y. Pei, Y. Wei, J. Sun et al., Memristors based on NdNiO3 nanocrystals film as sensory neurons for neuromorphic computing. Mater. Horiz. 10, 4521–4531 (2023). https://doi.org/10.1039/d3mh00835e
J.K. Han, J. Sim, D.M. Geum, S.K. Kim, J.M. Yu et al., 3D stackable broadband photoresponsive InGaAs biristor neuron for a neuromorphic visual system with near 1 V operation. In: 2021 IEEE international electron devices meeting (IEDM). December 11-16, 2021, San Francisco, CA, USA. IEEE, (2021), pp. 1–4.
D. Regan, H. Spekreijse, Electrophysiological correlate of binocular depth perception in man. Nature 225, 92–94 (1970). https://doi.org/10.1038/225092a0
N. Qian, Binocular disparity and the perception of depth. Neuron 18, 359–368 (1997). https://doi.org/10.1016/S0896-6273(00)81238-6
C. Chen, Y. He, H. Mao, L. Zhu, X. Wang et al., A photoelectric spiking neuron for visual depth perception. Adv. Mater. 34, e2201895 (2022). https://doi.org/10.1002/adma.202201895
X. Wang, C. Chen, L. Zhu, K. Shi, B. Peng et al., Vertically integrated spiking cone photoreceptor arrays for color perception. Nat. Commun. 14, 3444 (2023). https://doi.org/10.1038/s41467-023-39143-8
D.I. Perrett, E.T. Rolls, W. Caan, Visual neurones responsive to faces in the monkey temporal cortex. Exp. Brain Res. 47, 329–342 (1982). https://doi.org/10.1007/BF00239352
R.S. Johansson, I. Birznieks, First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat. Neurosci. 7, 170–177 (2004). https://doi.org/10.1038/nn1177
J. Huxter, N. Burgess, J. O’Keefe, Independent rate and temporal coding in hippocampal pyramidal cells. Nature 425, 828–832 (2003). https://doi.org/10.1038/nature02058
S.S. Radhakrishnan, S. Chakrabarti, D. Sen, M. Das, T.F. Schranghamer et al., A sparse and spike-timing-based adaptive photoencoder for augmenting machine vision for spiking neural networks. Adv. Mater. 34, e2202535 (2022). https://doi.org/10.1002/adma.202202535
C. Spence, S. Squire, Multisensory integration: maintaining the perception of synchrony. Curr. Biol. 13, R519–R521 (2003). https://doi.org/10.1016/S0960-9822(03)00445-7
T. Wang, P. Zheng, S. Li, L. Wang, Multimodal human–robot interaction for human-centric smart manufacturing: a survey. Adv. Intell. Syst. 6, 2300359 (2024). https://doi.org/10.1002/aisy.202300359
R. Yang, W. Zhang, N. Tiwari, H. Yan, T. Li et al., Multimodal sensors with decoupled sensing mechanisms. Adv. Sci. 9, 2202470 (2022). https://doi.org/10.1002/advs.202202470
Z. Yuan, G. Shen, Materials and device architecture towards a multimodal electronic skin. Mater. Today 64, 165–179 (2023). https://doi.org/10.1016/j.mattod.2023.02.023
Q. Duan, T. Zhang, C. Liu, R. Yuan, G. Li et al., Artificial multisensory neurons with fused haptic and temperature perception for multimodal in-sensor computing. Adv. Intell. Syst. 4, 2270039 (2022). https://doi.org/10.1002/aisy.202270039
J. Zhu, X. Zhang, R. Wang, M. Wang, P. Chen et al., A heterogeneously integrated spiking neuron array for multimode-fused perception and object classification. Adv. Mater. 34, 2200481 (2022). https://doi.org/10.1002/adma.202200481
J.-K. Han, S.-Y. Yun, J.-M. Yu, S.-B. Jeon, Y.-K. Choi, Artificial multisensory neuron with a single transistor for multimodal perception through hybrid visual and thermal sensing. ACS Appl. Mater. Interfaces 15, 5449–5455 (2023). https://doi.org/10.1021/acsami.2c19208
A.C. Abad, A. Ranasinghe, Visuotactile sensors with emphasis on GelSight sensor: a review. IEEE Sens. J. 20, 7628–7638 (2020). https://doi.org/10.1109/JSEN.2020.2979662
P. Cornelio, C. Velasco, M. Obrist, Multisensory integration as per technological advances: a review. Front. Neurosci. 15, 652611 (2021). https://doi.org/10.3389/fnins.2021.652611
J.T. Lee, D. Bollegala, S. Luo, “Touching to see” and “seeing to feel”: robotic cross-modal sensory data generation for visual-tactile perception. In: 2019 International conference on robotics and automation (ICRA). May 20–24, 2019, Montreal, QC, Canada. IEEE, (2019), pp. 4276–4282.
P. Falco, S. Lu, A. Cirillo, C. Natale, S. Pirozzi et al., Cross-modal visuo-tactile object recognition using robotic active exploration. In: 2017 IEEE International conference on robotics and automation (ICRA). May 29–June 3, 2017, Singapore. IEEE, (2017), pp. 5273–5280.
M.U.K. Sadaf, N.U. Sakib, A. Pannone, H. Ravichandran, S. Das, A bio-inspired visuotactile neuron for multisensory integration. Nat. Commun. 14, 5729 (2023). https://doi.org/10.1038/s41467-023-40686-z
Y. Yuan, R. Gao, Q. Wu, S. Fang, X. Bu et al., Artificial leaky integrate-and-fire sensory neuron for in-sensor computing neuromorphic perception at the edge. ACS Sens. 8, 2646–2655 (2023). https://doi.org/10.1021/acssensors.3c00487
Y. Ding, P. Yuan, J. Yu, Y. Chen, P. Jiang et al., Forming-free NbOx-based memristor enabling low-energy-consumption artificial spiking afferent nerves. IEEE Trans. Electron Devices 69, 5391–5394 (2022). https://doi.org/10.1109/TED.2022.3191988
S. Zhong, Y. Zhang, H. Zheng, F. Yu, R. Zhao, Spike-based spatiotemporal processing enabled by oscillation neuron for energy-efficient artificial sensory systems. Adv. Intell. Syst. 4, 2200076 (2022). https://doi.org/10.1002/aisy.202200076
B. Grisafe, M. Jerry, J.A. Smith, S. Datta, Performance enhancement of Ag/HfO2 metal ion threshold switch cross-point selectors. IEEE Electron Device Lett. 40, 1602–1605 (2019). https://doi.org/10.1109/led.2019.2936104
J. Park, T. Hadamek, A.B. Posadas, E. Cha, A.A. Demkov et al., Multi-layered NiOy/NbOx/NiOy fast drift-free threshold switch with high Ion/Ioff ratio for selector application. Sci. Rep. 7, 4068 (2017). https://doi.org/10.1038/s41598-017-04529-4
L. Wang, W. Cai, D. He, Q. Lin, D. Wan et al., Performance improvement of GeTex-based ovonic threshold switching selector by C doping. IEEE Electron Device Lett. 42, 688–691 (2021). https://doi.org/10.1109/LED.2021.3064857
N. Luo, W. Dai, C. Li, Z. Zhou, L. Lu et al., Flexible piezoresistive sensor patch enabling ultralow power cuffless blood pressure measurement. Adv. Funct. Mater. 26, 1178–1187 (2016). https://doi.org/10.1002/adfm.201504560
K. Kim, M. Jung, B. Kim, J. Kim, K. Shin et al., Low-voltage, high-sensitivity and high-reliability bimodal sensor array with fully inkjet-printed flexible conducting electrode for low power consumption electronic skin. Nano Energy 41, 301–307 (2017). https://doi.org/10.1016/j.nanoen.2017.09.024
Y. Guo, M. Zhong, Z. Fang, P. Wan, G. Yu, A wearable transient pressure sensor made with MXene nanosheets for sensitive broad-range human-machine interfacing. Nano Lett. 19, 1143–1150 (2019). https://doi.org/10.1021/acs.nanolett.8b04514
M. Swierczewska, G. Liu, S. Lee, X. Chen, High-sensitivity nanosensors for biomarker detection. Chem. Soc. Rev. 41, 2641–2655 (2012). https://doi.org/10.1039/c1cs15238f
C. Wang, L. Yin, L. Zhang, D. Xiang, R. Gao, Metal oxide gas sensors: sensitivity and influencing factors. Sensors 10, 2088–2106 (2010). https://doi.org/10.3390/s100302088
X. Wang, J. Yu, Y. Cui, W. Li, Research progress of flexible wearable pressure sensors. Sens. Actuat. A Phys. 330, 112838 (2021). https://doi.org/10.1016/j.sna.2021.112838
S. Pyo, J. Lee, K. Bae, S. Sim, J. Kim, Recent progress in flexible tactile sensors for human-interactive systems: from sensors to advanced applications. Adv. Mater. 33, e2005902 (2021). https://doi.org/10.1002/adma.202005902
Y. Lee, J. Park, S. Cho, Y.-E. Shin, H. Lee et al., Flexible ferroelectric sensors with ultrahigh pressure sensitivity and linear response over exceptionally broad pressure range. ACS Nano 12, 4045–4054 (2018). https://doi.org/10.1021/acsnano.8b01805
M. Stevens, G. Yun, T. Hasan, Porous conductive hybrid composite with superior pressure sensitivity and dynamic range. Adv. Funct. Mater. 34, 2309347 (2024). https://doi.org/10.1002/adfm.202309347
H. Niu, N. Li, E.-S. Kim, Y.K. Shin, N.-Y. Kim et al., Advances in advanced solution-synthesis-based structural materials for tactile sensors and their intelligent applications. InfoMat 6, e12500 (2024). https://doi.org/10.1002/inf2.12500
S. Zhong, J. Zhou, F. Yu, M. Xu, Y. Zhang et al., An optical neuromorphic sensor with high uniformity and high linearity for indoor visible light localization. Adv. Sens. Res. 3, 2300197 (2024). https://doi.org/10.1002/adsr.202300197
B. Zhu, Z. Xu, X. Liu, Z. Wang, Y. Zhang et al., High-linearity flexible pressure sensor based on the Gaussian-curve-shaped microstructure for human physiological signal monitoring. ACS Sens. 8, 3127–3135 (2023). https://doi.org/10.1021/acssensors.3c00818
X. Huang, D. Zhang, A high sensitivity and high linearity pressure sensor based on a peninsula-structured diaphragm for low-pressure ranges. Sens. Actuat. A Phys. 216, 176–189 (2014). https://doi.org/10.1016/j.sna.2014.05.031
N. Bai, L. Wang, Y. Xue, Y. Wang, X. Hou et al., Graded interlocks for iontronic pressure sensors with high sensitivity and high linearity over a broad range. ACS Nano 16, 4338–4347 (2022). https://doi.org/10.1021/acsnano.1c10535
S.R.A. Ruth, V.R. Feig, H. Tran, Z. Bao, Microengineering pressure sensor active layers for improved performance. Adv. Funct. Mater. 30, 2003491 (2020). https://doi.org/10.1002/adfm.202003491
Z. Luo, J. Chen, Z. Zhu, L. Li, Y. Su et al., High-resolution and high-sensitivity flexible capacitive pressure sensors enhanced by a transferable electrode array and a micropillar-PVDF film. ACS Appl. Mater. Interfaces 13, 7635–7649 (2021). https://doi.org/10.1021/acsami.0c23042
Y. Li, J. Long, Y. Chen, Y. Huang, N. Zhao, Crosstalk-free, high-resolution pressure sensor arrays enabled by high-throughput laser manufacturing. Adv. Mater. 34, e2200517 (2022). https://doi.org/10.1002/adma.202200517
J. Lee, S. Kim, S. Park, J. Lee, W. Hwang et al., An artificial tactile neuron enabling spiking representation of stiffness and disease diagnosis. Adv. Mater. 34, e2201608 (2022). https://doi.org/10.1002/adma.202201608
J. Zhu, X. Zhang, M. Wang, R. Wang, P. Chen et al., An artificial spiking nociceptor integrating pressure sensors and memristors. IEEE Electron Device Lett. 43, 962–965 (2022). https://doi.org/10.1109/led.2022.3167421
Y. Wang, Y. Gong, S. Huang, X. Xing, Z. Lv et al., Memristor-based biomimetic compound eye for real-time collision detection. Nat. Commun. 12, 5979 (2021). https://doi.org/10.1038/s41467-021-26314-8
Y. Wang, M.L. Adam, Y. Zhao, W. Zheng, L. Gao et al., Machine learning-enhanced flexible mechanical sensing. Nano-Micro Lett. 15, 55 (2023). https://doi.org/10.1007/s40820-023-01013-9
Z. Shi, L. Meng, X. Shi, H. Li, J. Zhang et al., Morphological engineering of sensing materials for flexible pressure sensors and artificial intelligence applications. Nano-Micro Lett. 14, 141 (2022). https://doi.org/10.1007/s40820-022-00874-w
R. Wu, S. Seo, L. Ma, J. Bae, T. Kim, Full-fiber auxetic-interlaced yarn sensor for sign-language translation glove assisted by artificial neural network. Nano-Micro Lett. 14, 139 (2022). https://doi.org/10.1007/s40820-022-00887-5
I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43, 3–31 (2000). https://doi.org/10.1016/s0167-7012(00)00201-3
K. Yamazaki, V.-K. Vo-Ho, D. Bulsara, N. Le, Spiking neural networks and their applications: a review. Brain Sci. 12, 863 (2022). https://doi.org/10.3390/brainsci12070863
A. Taherkhani, A. Belatreche, Y. Li, G. Cosma, L.P. Maguire et al., A review of learning in biologically plausible spiking neural networks. Neural Netw. 122, 253–272 (2020). https://doi.org/10.1016/j.neunet.2019.09.036
D. Auge, J. Hille, E. Mueller, A. Knoll, A survey of encoding techniques for signal processing in spiking neural networks. Neural. Process. Lett. 53, 4693–4710 (2021). https://doi.org/10.1007/s11063-021-10562-2
R. Yuan, Q. Duan, P.J. Tiw, G. Li, Z. Xiao et al., A calibratable sensory neuron based on epitaxial VO2 for spike-based neuromorphic multisensory system. Nat. Commun. 13, 3973 (2022). https://doi.org/10.1038/s41467-022-31747-w
G. Tanaka, T. Yamane, J.B. Héroux, R. Nakane, N. Kanazawa et al., Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019). https://doi.org/10.1016/j.neunet.2019.03.005
M. Lukoševičius, H. Jaeger, Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009). https://doi.org/10.1016/j.cosrev.2009.03.005
Z. Qi, L. Mi, H. Qian, W. Zheng, Y. Guo et al., Physical Reservoir computing based on nanoscale materials and devices. Adv. Funct. Mater. 33, 2306149 (2023). https://doi.org/10.1002/adfm.202306149
J. Cao, X. Zhang, H. Cheng, J. Qiu, X. Liu et al., Emerging dynamic memristors for neuromorphic reservoir computing. Nanoscale 14, 289–298 (2022). https://doi.org/10.1039/d1nr06680c
Z. Wang, Y. Ma, F. Cheng, L. Yang, Review of pulse-coupled neural networks. Image Vis. Comput. 28, 5–13 (2010). https://doi.org/10.1016/j.imavis.2009.06.007
M.M. Subashini, S.K. Sahoo, Pulse coupled neural networks and its applications. Expert Syst. Appl. 41, 3965–3974 (2014). https://doi.org/10.1016/j.eswa.2013.12.027
Q. Wu, B. Dang, C. Lu, G. Xu, G. Yang et al., Spike encoding with optic sensory neurons enable a pulse coupled neural network for ultraviolet image segmentation. Nano Lett. 20, 8015–8023 (2020). https://doi.org/10.1021/acs.nanolett.0c02892
M.N. Baliki, A.V. Apkarian, Nociception, pain, negative moods, and behavior selection. Neuron 87, 474–491 (2015). https://doi.org/10.1016/j.neuron.2015.06.005
B. Frias, A. Merighi, Capsaicin, nociception and pain. Molecules 21, 797 (2016). https://doi.org/10.3390/molecules21060797
D. Julius, A.I. Basbaum, Molecular mechanisms of nociception. Nature 413, 203–210 (2001). https://doi.org/10.1038/35093019
E. St. John Smith, Advances in understanding nociception and neuropathic pain. J. Neurol. 265, 231–238 (2018). https://doi.org/10.1007/s00415-017-8641-6
J.H. Yoon, Z. Wang, K.M. Kim, H. Wu, V. Ravichandran et al., An artificial nociceptor based on a diffusive memristor. Nat. Commun. 9, 417 (2018). https://doi.org/10.1038/s41467-017-02572-3
Y. Kim, Y.J. Kwon, D.E. Kwon, K.J. Yoon, J.H. Yoon et al., Nociceptive memristor. Adv. Mater. 30, 1704320 (2018). https://doi.org/10.1002/adma.201704320
F.-D. Wang, M.-X. Yu, X.-D. Chen, J. Li, Z.-C. Zhang et al., Optically modulated dual-mode memristor arrays based on core-shell CsPbBr3@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware. SmartMat 4, e1135 (2023). https://doi.org/10.1002/smm2.1135
A. Vahidi, A. Eskandarian, Research advances in intelligent collision avoidance and adaptive cruise control. IEEE Trans. Intell. Transp. Syst. 4, 143–153 (2003). https://doi.org/10.1109/TITS.2003.821292
J. Dahl, G.R. de Campos, C. Olsson, J. Fredriksson, Collision avoidance: a literature review on threat-assessment techniques. IEEE Trans. Intell. Veh. 4, 101–113 (2019). https://doi.org/10.1109/TIV.2018.2886682
D. Jayachandran, A. Oberoi, A. Sebastian, T.H. Choudhury, B. Shankar et al., A low-power biomimetic collision detector based on an in-memory molybdenum disulfide photodetector. Nat. Electron. 3, 646–655 (2020). https://doi.org/10.1038/s41928-020-00466-9
F. Gabbiani, H.G. Krapp, C. Koch, G. Laurent, Multiplicative computation in a visual neuron sensitive to looming. Nature 420, 320–324 (2002). https://doi.org/10.1038/nature01190
F. Gabbiani, H.G. Krapp, G. Laurent, Computation of object approach by a wide-field, motion-sensitive neuron. J. Neurosci. 19, 1122–1141 (1999). https://doi.org/10.1523/JNEUROSCI.19-03-01122.1999
A. Mukhtar, L. Xia, T.B. Tang, Vehicle detection techniques for collision avoidance systems: a review. IEEE Trans. Intell. Transp. Syst. 16, 2318–2338 (2015). https://doi.org/10.1109/TITS.2015.2409109
J.N. Yasin, S.A.S. Mohamed, M.-H. Haghbayan, J. Heikkonen, H. Tenhunen et al., Unmanned aerial vehicles (UAVs): collision avoidance systems and approaches. IEEE Access 8, 105139–105155 (2020). https://doi.org/10.1109/ACCESS.2020.3000064
Y. Pei, L. Yan, Z. Wu, J. Lu, J. Zhao et al., Artificial visual perception nervous system based on low-dimensional material photoelectric memristors. ACS Nano 15, 17319–17326 (2021). https://doi.org/10.1021/acsnano.1c04676
Z. Li, Z. Li, W. Tang, J. Yao, Z. Dou et al., Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system. Nat. Commun. 15, 7275 (2024). https://doi.org/10.1038/s41467-024-51609-x
Y.K. Choi, N. Lindert, P. Xuan, S. Tang, D. Ha et al., Sub-20 nm CMOS FinFET technologies. In: International Electron Devices Meeting. Technical Digest. December 2–5, 2001, Washington, DC, USA. IEEE, (2002), 19.1.1–19.1.4.
H. Lee, L.E. Yu, S.W. Ryu, J.-W. Han, K. Jeon et al., Sub-5nm all-around gate FinFET for ultimate scaling. In: 2006 Symposium on VLSI Technology, 2006. Digest of Technical Papers. June 13-15, 2006, Honolulu, HI, USA. IEEE, (2006), pp. 58–59.
R. Wang, F. Li, D. Li, C. Wang, Y. Tang et al., 1-phototransistor-1-threshold switching optoelectronic neuron for in-sensor compression via spiking neuron network. In: 2023 International electron devices meeting (IEDM). December 9–13, 2023, San Francisco, CA, USA. IEEE, (2023), pp. 1–4.
Y. Zhang, P. Qu, Y. Ji, W. Zhang, G. Gao et al., A system hierarchy for brain-inspired computing. Nature 586, 378–384 (2020). https://doi.org/10.1038/s41586-020-2782-y
Y. Cao, B. Xu, B. Li, H. Fu, Advanced design of soft robots with artificial intelligence. Nano-Micro Lett. 16, 214 (2024). https://doi.org/10.1007/s40820-024-01423-3