{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T05:54:08Z","timestamp":1778910848106,"version":"3.51.4"},"reference-count":93,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T00:00:00Z","timestamp":1639612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s11432-021-3336-8","type":"journal-article","created":{"date-parts":[[2021,12,19]],"date-time":"2021-12-19T17:02:21Z","timestamp":1639933341000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Neuromorphic sensory computing"],"prefix":"10.1007","volume":"65","author":[{"given":"Tianqing","family":"Wan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sijie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuyou","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingwei","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"key":"3336_CR1","doi-asserted-by":"publisher","first-page":"1900118","DOI":"10.1002\/aisy.201900118","volume":"2","author":"X L Ji","year":"2020","unstructured":"Ji X L, Zhao X Y, Tan M C, et al. Artificial perception built on memristive system: visual, auditory, and tactile sensations. Adv Intell Syst, 2020, 2: 1900118","journal-title":"Adv Intell Syst"},{"key":"3336_CR2","doi-asserted-by":"publisher","first-page":"1902434","DOI":"10.1002\/adma.201902434","volume":"32","author":"C L Wan","year":"2020","unstructured":"Wan C L, Cai P Q, Wang M, et al. Artificial sensory memory. Adv Mater, 2020, 32: 1902434","journal-title":"Adv Mater"},{"key":"3336_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/d41586-020-00592-6","volume":"579","author":"Y Chai","year":"2020","unstructured":"Chai Y. In-sensor computing for machine vision. Nature, 2020, 579: 32\u201333","journal-title":"Nature"},{"key":"3336_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1038\/s41928-020-00501-9","volume":"3","author":"F C Zhou","year":"2020","unstructured":"Zhou F C, Chai Y. Near-sensor and in-sensor computing. Nat Electron, 2020, 3: 664\u2013671","journal-title":"Nat Electron"},{"key":"3336_CR5","doi-asserted-by":"publisher","first-page":"013105","DOI":"10.1088\/1674-4926\/42\/1\/013105","volume":"42","author":"F Y Liao","year":"2021","unstructured":"Liao F Y, Zhou F C, Chai Y. Neuromorphic vision sensors: principle, progress and perspectives. J Semicond, 2021, 42: 013105","journal-title":"J Semicond"},{"key":"3336_CR6","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/JSSC.2012.2235031","volume":"48","author":"N Cottini","year":"2013","unstructured":"Cottini N, Gottardi M, Massari N, et al. A 33 \u00b5W 64\u00d764 pixel vision sensor embedding robust dynamic background subtraction for event detection and scene interpretation. IEEE J Solid-State Circ, 2013, 48: 850\u2013863","journal-title":"IEEE J Solid-State Circ"},{"key":"3336_CR7","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1109\/JSSC.2007.914337","volume":"43","author":"P Lichtsteiner","year":"2008","unstructured":"Lichtsteiner P, Posch C, Delbruck T. A 128\u00d7128 120 dB 15 \u00b5s latency asynchronous temporal contrast vision sensor. IEEE J Solid-State Circ, 2008, 43: 566\u2013576","journal-title":"IEEE J Solid-State Circ"},{"key":"3336_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5267-3","volume-title":"Vision Chips2000","author":"A Moini","year":"2000","unstructured":"Moini A. Vision Chips2000. Berlin: Springer, 2000"},{"key":"3336_CR9","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/JSSC.2003.819169","volume":"38","author":"P F Ruedi","year":"2003","unstructured":"Ruedi P F, Heim P, Kaess F, et al. A 128\u00d7128 pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction. IEEE J Solid-State Circ, 2003, 38: 2325\u20132333","journal-title":"IEEE J Solid-State Circ"},{"key":"3336_CR10","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1523\/JNEUROSCI.20-04-01374.2000","volume":"20","author":"J S Dittman","year":"2000","unstructured":"Dittman J S, Kreitzer A C, Regehr W G. Interplay between facilitation, depression, and residual calcium at three presynaptic terminals. J Neurosci, 2000, 20: 1374\u20131385","journal-title":"J Neurosci"},{"key":"3336_CR11","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1126\/science.275.5297.221","volume":"275","author":"L F Abbott","year":"1997","unstructured":"Abbott L F, Varela J A, Sen K, et al. Synaptic depression and cortical gain control. Science, 1997, 275: 221\u2013224","journal-title":"Science"},{"key":"3336_CR12","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1038\/nature07604","volume":"457","author":"J S Rothman","year":"2009","unstructured":"Rothman J S, Cathala L, Steuber V, et al. Synaptic depression enables neuronal gain control. Nature, 2009, 457: 1015\u20131018","journal-title":"Nature"},{"key":"3336_CR13","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1038\/s41566-020-00754-y","volume":"15","author":"B J Shastri","year":"2021","unstructured":"Shastri B J, Tait A N, de Lima T F, et al. Photonics for artificial intelligence and neuromorphic computing. Nat Photonics, 2021, 15: 102\u2013114","journal-title":"Nat Photonics"},{"key":"3336_CR14","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/TBCAS.2010.2075929","volume":"5","author":"D G Chen","year":"2011","unstructured":"Chen D G, Matolin D, Bermak A, et al. Pulse-modulation imaging-review and performance analysis. IEEE Trans Biomed Circ Syst, 2011, 5: 64\u201382","journal-title":"IEEE Trans Biomed Circ Syst"},{"key":"3336_CR15","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1109\/JPROC.2014.2346153","volume":"102","author":"C Posch","year":"2014","unstructured":"Posch C, Serrano-Gotarredona T, Linares-Barranco B, et al. Retinomorphic event-based vision sensors: bioinspired cameras with spiking output. Proc IEEE, 2014, 102: 1470\u20131484","journal-title":"Proc IEEE"},{"key":"3336_CR16","doi-asserted-by":"crossref","unstructured":"Finateu T, Niwa A, Matolin D, et al. A 1280\u00d7720 back-illuminated stacked temporal contrast event-based vision sensor with 4.86 \u00b5m pixels, 1.066GEPS readout, programmable event-rate controller and compressive data-formatting pipeline. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 2020","DOI":"10.1109\/ISSCC19947.2020.9063149"},{"key":"3336_CR17","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/JSEN.2005.854488","volume":"5","author":"P Hasler","year":"2005","unstructured":"Hasler P, Smith P D, Graham D, et al. Analog floating-gate, on-chip auditory sensing system interfaces. IEEE Sens J, 2005, 5: 1027\u20131034","journal-title":"IEEE Sens J"},{"key":"3336_CR18","doi-asserted-by":"crossref","unstructured":"Hsu T H, Chen Y K, Wu J S, et al. A 0.8 V multimode vision sensor for motion and saliency detection with ping-pong PWM pixel. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 2020","DOI":"10.1109\/ISSCC19947.2020.9062926"},{"key":"3336_CR19","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/TNNLS.2016.2583223","volume":"28","author":"A Jimenez-Fernandez","year":"2017","unstructured":"Jimenez-Fernandez A, Cerezuela-Escudero E, Miro-Amarante L, et al. A binaural neuromorphic auditory sensor for FPGA: a spike signal processing approach. IEEE Trans Neural Netw Learn Syst, 2017, 28: 804\u2013818","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"3336_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/372197a0","volume":"372","author":"K Kyuma","year":"1994","unstructured":"Kyuma K, Lange E, Ohta J, et al. Artificial retinas \u2014 fast, versatile image processors. Nature, 1994, 372: 197\u2013198","journal-title":"Nature"},{"key":"3336_CR21","doi-asserted-by":"crossref","unstructured":"Lichtsteiner P, Posch C, Delbruck T. A 128\u00d7128 120 dB 30 mW asynchronous vision sensor that responds to relative intensity change. In: Proceedings of IEEE International Solid-State Circuits Conference, San Francisco, 2006","DOI":"10.1109\/ISSCC.2006.1696265"},{"key":"3336_CR22","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/29.1639","volume":"36","author":"R F Lyon","year":"1988","unstructured":"Lyon R F, Mead C. An analog electronic cochlea. IEEE Trans Acoust Speech Signal Process, 1988, 36: 1119\u20131134","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"3336_CR23","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/nature12083","volume":"497","author":"Y M Song","year":"2013","unstructured":"Song Y M, Xie Y, Malyarchuk V, et al. Digital cameras with designs inspired by the arthropod eye. Nature, 2013, 497: 95\u201399","journal-title":"Nature"},{"key":"3336_CR24","doi-asserted-by":"crossref","unstructured":"Wen B, Boahen K. A 360-channel speech preprocessor that emulates the cochlear amplifier. In: Proceedings of IEEE International Solid State Circuits Conference, San Francisco, 2006","DOI":"10.1109\/ISSCC.2006.1696289"},{"key":"3336_CR25","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1038\/nmat4756","volume":"16","author":"Z R Wang","year":"2017","unstructured":"Wang Z R, Joshi S, Savel\u2019ev S E, et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat Mater, 2017, 16: 101\u2013108","journal-title":"Nat Mater"},{"key":"3336_CR26","doi-asserted-by":"publisher","first-page":"011307","DOI":"10.1063\/1.5122249","volume":"7","author":"H F Ling","year":"2020","unstructured":"Ling H F, Koutsouras D A, Kazemzadeh S, et al. Electrolyte-gated transistors for synaptic electronics, neuromorphic computing, and adaptable biointerfacing. Appl Phys Rev, 2020, 7: 011307","journal-title":"Appl Phys Rev"},{"key":"3336_CR27","doi-asserted-by":"publisher","first-page":"1606088","DOI":"10.1002\/adma.201606088","volume":"29","author":"Y P Zang","year":"2017","unstructured":"Zang Y P, Shen H G, Huang D Z, et al. A dual-organic-transistor-based tactile-perception system with signal-processing functionality. Adv Mater, 2017, 29: 1606088","journal-title":"Adv Mater"},{"key":"3336_CR28","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1038\/s41586-020-1942-4","volume":"577","author":"P Yao","year":"2020","unstructured":"Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577: 641\u2013646","journal-title":"Nature"},{"key":"3336_CR29","doi-asserted-by":"publisher","first-page":"4602","DOI":"10.1038\/s41467-020-18375-y","volume":"11","author":"C J Wan","year":"2020","unstructured":"Wan C J, Cai P Q, Guo X T, et al. An artificial sensory neuron with visual-haptic fusion. Nat Commun, 2020, 11: 4602","journal-title":"Nat Commun"},{"key":"3336_CR30","doi-asserted-by":"publisher","first-page":"1900903","DOI":"10.1002\/adma.201900903","volume":"31","author":"Y L He","year":"2019","unstructured":"He Y L, Nie S, Liu R, et al. Spatiotemporal information processing emulated by multiterminal neuro-transistor networks. Adv Mater, 2019, 31: 1900903","journal-title":"Adv Mater"},{"key":"3336_CR31","doi-asserted-by":"publisher","first-page":"3450","DOI":"10.1038\/s41467-019-11381-9","volume":"10","author":"S Das","year":"2019","unstructured":"Das S, Dodda A, Das S. A biomimetic 2D transistor for audiomorphic computing. Nat Commun, 2019, 10: 3450","journal-title":"Nat Commun"},{"key":"3336_CR32","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1038\/nnano.2017.83","volume":"12","author":"P M Sheridan","year":"2017","unstructured":"Sheridan P M, Cai F X, Du C, et al. Sparse coding with memristor networks. Nat Nanotech, 2017, 12: 784\u2013789","journal-title":"Nat Nanotech"},{"key":"3336_CR33","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1038\/s41467-018-04484-2","volume":"9","author":"C Li","year":"2018","unstructured":"Li C, Belkin D, Li Y N, et al. Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nat Commun, 2018, 9: 2385","journal-title":"Nat Commun"},{"key":"3336_CR34","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1038\/nature14441","volume":"521","author":"M Prezioso","year":"2015","unstructured":"Prezioso M, Merrikh-Bayat F, Hoskins B D, et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors. Nature, 2015, 521: 61\u201364","journal-title":"Nature"},{"key":"3336_CR35","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41928-018-0023-2","volume":"1","author":"Z R Wang","year":"2018","unstructured":"Wang Z R, Joshi S, Savel\u2019ev S, et al. Fully memristive neural networks for pattern classification with unsupervised learning. Nat Electron, 2018, 1: 137\u2013145","journal-title":"Nat Electron"},{"key":"3336_CR36","doi-asserted-by":"publisher","first-page":"12611","DOI":"10.1038\/ncomms12611","volume":"7","author":"A Serb","year":"2016","unstructured":"Serb A, Bill J, Khiat A, et al. Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses. Nat Commun, 2016, 7: 12611","journal-title":"Nat Commun"},{"key":"3336_CR37","doi-asserted-by":"publisher","first-page":"205","DOI":"10.3389\/fnins.2014.00205","volume":"8","author":"S B Eryilmaz","year":"2014","unstructured":"Eryilmaz S B, Kuzum D, Jeyasingh R, et al. Brain-like associative learning using a nanoscale non-volatile phase change synaptic device array. Front Neurosci, 2014, 8: 205","journal-title":"Front Neurosci"},{"key":"3336_CR38","doi-asserted-by":"publisher","first-page":"7522","DOI":"10.1038\/ncomms8522","volume":"6","author":"S G Hu","year":"2015","unstructured":"Hu S G, Liu Y, Liu Z, et al. Associative memory realized by a reconfigurable memristive Hopfield neural network. Nat Commun, 2015, 6: 7522","journal-title":"Nat Commun"},{"key":"3336_CR39","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.neunet.2010.05.001","volume":"23","author":"Y V Pershin","year":"2010","unstructured":"Pershin Y V, Di Ventra M. Experimental demonstration of associative memory with memristive neural networks. Neural Netw, 2010, 23: 881\u2013886","journal-title":"Neural Netw"},{"key":"3336_CR40","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s41928-019-0313-3","volume":"2","author":"J Moon","year":"2019","unstructured":"Moon J, Ma W, Shin J H, et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat Electron, 2019, 2: 480\u2013487","journal-title":"Nat Electron"},{"key":"3336_CR41","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1038\/s41928-019-0318-y","volume":"2","author":"M J Marinella","year":"2019","unstructured":"Marinella M J, Agarwal S. Efficient reservoir computing with memristors. Nat Electron, 2019, 2: 437\u2013438","journal-title":"Nat Electron"},{"key":"3336_CR42","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1038\/s41467-020-20692-1","volume":"12","author":"Y N Zhong","year":"2021","unstructured":"Zhong Y N, Tang J S, Li X Y, et al. Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing. Nat Commun, 2021, 12: 408","journal-title":"Nat Commun"},{"key":"3336_CR43","doi-asserted-by":"publisher","first-page":"106787","DOI":"10.1016\/j.optlastec.2020.106787","volume":"136","author":"R Q Xu","year":"2021","unstructured":"Xu R Q, Lv P, Xu F J, et al. A survey of approaches for implementing optical neural networks. Opt Laser Tech, 2021, 136: 106787","journal-title":"Opt Laser Tech"},{"key":"3336_CR44","doi-asserted-by":"crossref","unstructured":"Chen H G, Jayasuriya S, Yang J Y, et al. ASP vision: optically computing the first layer of convolutional neural networks using angle sensitive pixels. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 27\u201330","DOI":"10.1109\/CVPR.2016.104"},{"key":"3336_CR45","doi-asserted-by":"crossref","unstructured":"Hirsch M, Sivaramakrishnan S, Jayasuriya S, et al. A switchable light field camera architecture with angle sensitive pixels and dictionary-based sparse coding. In: Proceedings of IEEE International Conference on Computational Photography (ICCP), 2014","DOI":"10.1109\/ICCPHOT.2014.6831813"},{"key":"3336_CR46","doi-asserted-by":"crossref","unstructured":"Wang A, Sivaramakrishnan S, Molnar A. A 180 nm CMOS image sensor with on-chip optoelectronic image compression. In: Proceedings of IEEE Custom Integrated Circuits Conference, 2012","DOI":"10.1109\/CICC.2012.6330604"},{"key":"3336_CR47","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.1364\/AO.5.001248","volume":"5","author":"C S Weaver","year":"1966","unstructured":"Weaver C S, Goodman J W. A technique for optically convolving two functions. Appl Opt, 1966, 5: 1248","journal-title":"Appl Opt"},{"key":"3336_CR48","doi-asserted-by":"publisher","first-page":"12324","DOI":"10.1038\/s41598-018-30619-y","volume":"8","author":"J L Chang","year":"2018","unstructured":"Chang J L, Sitzmann V, Dun X, et al. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci Rep, 2018, 8: 12324","journal-title":"Sci Rep"},{"key":"3336_CR49","volume-title":"Handbook of Brain Theory and Neural Networks","author":"Y LeCun","year":"1995","unstructured":"LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. In: Handbook of Brain Theory and Neural Networks. Cambridge: MIT Press, 1995"},{"key":"3336_CR50","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1038\/nphoton.2017.93","volume":"11","author":"Y C Shen","year":"2017","unstructured":"Shen Y C, Harris N C, Skirlo S, et al. Deep learning with coherent nanophotonic circuits. Nat Photon, 2017, 11: 441\u2013446","journal-title":"Nat Photon"},{"key":"3336_CR51","doi-asserted-by":"publisher","first-page":"5221","DOI":"10.1016\/j.eswa.2008.06.072","volume":"36","author":"S Bueno","year":"2009","unstructured":"Bueno S, Salmeron J L. Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst Appl, 2009, 36: 5221\u20135229","journal-title":"Expert Syst Appl"},{"key":"3336_CR52","doi-asserted-by":"publisher","first-page":"1489","DOI":"10.1080\/713821684","volume":"32","author":"J W Goodman","year":"1985","unstructured":"Goodman J W. Fan-in and fan-out with optical interconnections. Opt Acta-Int J Opt, 1985, 32: 1489\u20131496","journal-title":"Opt Acta-Int J Opt"},{"key":"3336_CR53","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.1109\/TNN.2002.804222","volume":"13","author":"M T Hill","year":"2002","unstructured":"Hill M T, Frietman E E E, de Waardt H, et al. All fiber-optic neural network using coupled SOA based ring lasers. IEEE Trans Neural Netw, 2002, 13: 1504\u20131513","journal-title":"IEEE Trans Neural Netw"},{"key":"3336_CR54","doi-asserted-by":"publisher","first-page":"11182","DOI":"10.1364\/OE.16.011182","volume":"16","author":"K Vandoorne","year":"2008","unstructured":"Vandoorne K, Dierckx W, Schrauwen B, et al. Toward optical signal processing using photonic reservoir computing. Opt Express, 2008, 16: 11182","journal-title":"Opt Express"},{"key":"3336_CR55","doi-asserted-by":"publisher","first-page":"3048","DOI":"10.1364\/JOSAB.30.003048","volume":"30","author":"C Mesaritakis","year":"2013","unstructured":"Mesaritakis C, Papataxiarhis V, Syvridis D. Micro ring resonators as building blocks for an all-optical high-speed reservoircomputing bit-pattern-recognition system. J Opt Soc Am B, 2013, 30: 3048","journal-title":"J Opt Soc Am B"},{"key":"3336_CR56","doi-asserted-by":"publisher","first-page":"22767","DOI":"10.1364\/OE.17.022767","volume":"17","author":"D Rosenbluth","year":"2009","unstructured":"Rosenbluth D, Kravtsov K, Fok M P, et al. A high performance photonic pulse processing device. Opt Express, 2009, 17: 22767","journal-title":"Opt Express"},{"key":"3336_CR57","doi-asserted-by":"publisher","first-page":"023901","DOI":"10.1103\/PhysRevLett.123.023901","volume":"123","author":"T Yan","year":"2019","unstructured":"Yan T, Wu J M, Zhou T K, et al. Fourier-space diffractive deep neural network. Phys Rev Lett, 2019, 123: 023901","journal-title":"Phys Rev Lett"},{"key":"3336_CR58","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1364\/OPTICA.6.001132","volume":"6","author":"Y Zuo","year":"2019","unstructured":"Zuo Y, Li B H, Zhao Y J, et al. All-optical neural network with nonlinear activation functions. Optica, 2019, 6: 1132","journal-title":"Optica"},{"key":"3336_CR59","doi-asserted-by":"publisher","first-page":"12980","DOI":"10.1038\/s41598-018-31365-x","volume":"8","author":"I Chakraborty","year":"2018","unstructured":"Chakraborty I, Saha G, Sengupta A, et al. Toward fast neural computing using all-photonic phase change spiking neurons. Sci Rep, 2018, 8: 12980","journal-title":"Sci Rep"},{"key":"3336_CR60","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1038\/s41586-019-1157-8","volume":"569","author":"J Feldmann","year":"2019","unstructured":"Feldmann J, Youngblood N, Wright C D, et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature, 2019, 569: 208\u2013214","journal-title":"Nature"},{"key":"3336_CR61","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1364\/PRJ.7.000823","volume":"7","author":"E Khoram","year":"2019","unstructured":"Khoram E, Chen A, Liu D J, et al. Nanophotonic media for artificial neural inference. Photon Res, 2019, 7: 823","journal-title":"Photon Res"},{"key":"3336_CR62","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1038\/s41565-019-0501-3","volume":"14","author":"F C Zhou","year":"2019","unstructured":"Zhou F C, Zhou Z, Chen J W, et al. Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat Nanotechnol, 2019, 14: 776\u2013782","journal-title":"Nat Nanotechnol"},{"key":"3336_CR63","doi-asserted-by":"publisher","first-page":"078502","DOI":"10.1088\/1674-1056\/ab99b6","volume":"29","author":"Y B Guo","year":"2020","unstructured":"Guo Y B, Zhu L Q. Recent progress in optoelectronic neuromorphic devices. Chin Phys B, 2020, 29: 078502","journal-title":"Chin Phys B"},{"key":"3336_CR64","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1038\/s41467-021-22047-w","volume":"12","author":"Q B Zhu","year":"2021","unstructured":"Zhu Q B, Li B, Yang D D, et al. A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems. Nat Commun, 2021, 12: 1798","journal-title":"Nat Commun"},{"key":"3336_CR65","doi-asserted-by":"publisher","first-page":"21870","DOI":"10.1038\/s41598-020-78767-4","volume":"10","author":"M M Islam","year":"2020","unstructured":"Islam M M, Dev D, Krishnaprasad A, et al. Optoelectronic synapse using monolayer MoS2 field effect transistors. Sci Rep, 2020, 10: 21870","journal-title":"Sci Rep"},{"key":"3336_CR66","doi-asserted-by":"publisher","first-page":"5934","DOI":"10.1038\/s41467-020-19806-6","volume":"11","author":"C Choi","year":"2020","unstructured":"Choi C, Leem J, Kim M S, et al. Curved neuromorphic image sensor array using a MoS2-organic heterostructure inspired by the human visual recognition system. Nat Commun, 2020, 11: 5934","journal-title":"Nat Commun"},{"key":"3336_CR67","doi-asserted-by":"publisher","first-page":"5106","DOI":"10.1038\/s41467-018-07572-5","volume":"9","author":"S Seo","year":"2018","unstructured":"Seo S, Jo S H, Kim S, et al. Artificial optic-neural synapse for colored and color-mixed pattern recognition. Nat Commun, 2018, 9: 5106","journal-title":"Nat Commun"},{"key":"3336_CR68","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1038\/nrn2331","volume":"9","author":"B E Stein","year":"2008","unstructured":"Stein B E, Stanford T R. Multisensory integration: current issues from the perspective of the single neuron. Nat Rev Neurosci, 2008, 9: 255\u2013266","journal-title":"Nat Rev Neurosci"},{"key":"3336_CR69","doi-asserted-by":"publisher","first-page":"3340","DOI":"10.1016\/j.neuropsychologia.2007.05.025","volume":"45","author":"N P Holmes","year":"2007","unstructured":"Holmes N P. The law of inverse effectiveness in neurons and behaviour: multisensory integration versus normal variability. Neuropsychologia, 2007, 45: 3340\u20133345","journal-title":"Neuropsychologia"},{"key":"3336_CR70","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1162\/0898929041502733","volume":"16","author":"H Colonius","year":"2004","unstructured":"Colonius H, Diederich A. Multisensory interaction in saccadic reaction time: a time-window-of-integration model. J Cogn Neurosci, 2004, 16: 1000\u20131009","journal-title":"J Cogn Neurosci"},{"key":"3336_CR71","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/nn.2815","volume":"14","author":"T Ohshiro","year":"2011","unstructured":"Ohshiro T, Angelaki D E, DeAngelis G C. A normalization model of multisensory integration. Nat Neurosci, 2011, 14: 775\u2013782","journal-title":"Nat Neurosci"},{"key":"3336_CR72","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1038\/nrn3503","volume":"14","author":"C R Fetsch","year":"2013","unstructured":"Fetsch C R, DeAngelis G C, Angelaki D E. Bridging the gap between theories of sensory cue integration and the physiology of multisensory neurons. Nat Rev Neurosci, 2013, 14: 429\u2013442","journal-title":"Nat Rev Neurosci"},{"key":"3336_CR73","doi-asserted-by":"publisher","first-page":"128822","DOI":"10.1016\/j.snb.2020.128822","volume":"326","author":"J Y Zhang","year":"2021","unstructured":"Zhang J Y, Xue Y Y, Sun Q Y, et al. A miniaturized electronic nose with artificial neural network for anti-interference detection of mixed indoor hazardous gases. Sens Actuat B-Chem, 2021, 326: 128822","journal-title":"Sens Actuat B-Chem"},{"key":"3336_CR74","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/s41467-017-02685-9","volume":"9","author":"Q L Hua","year":"2018","unstructured":"Hua Q L, Sun J L, Liu H T, et al. Skin-inspired highly stretchable and conformable matrix networks for multifunctional sensing. Nat Commun, 2018, 9: 244","journal-title":"Nat Commun"},{"key":"3336_CR75","doi-asserted-by":"publisher","first-page":"10966","DOI":"10.1021\/acsnano.0c03757","volume":"14","author":"Y Y Lu","year":"2020","unstructured":"Lu Y Y, Xu K C, Zhang L S, et al. Multimodal plant healthcare flexible sensor system. ACS Nano, 2020, 14: 10966\u201310975","journal-title":"ACS Nano"},{"key":"3336_CR76","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1126\/science.aba5132","volume":"370","author":"I You","year":"2020","unstructured":"You I, Mackanic D G, Matsuhisa N, et al. Artificial multimodal receptors based on ion relaxation dynamics. Science, 2020, 370: 961\u2013965","journal-title":"Science"},{"key":"3336_CR77","doi-asserted-by":"publisher","first-page":"9117","DOI":"10.1126\/sciadv.abd9117","volume":"7","author":"J R Yu","year":"2021","unstructured":"Yu J R, Yang X X, Gao G Y, et al. Bioinspired mechano-photonic artificial synapse based on graphene\/MoS2 heterostructure. Sci Adv, 2021, 7: 9117","journal-title":"Sci Adv"},{"key":"3336_CR78","doi-asserted-by":"publisher","first-page":"106000","DOI":"10.1016\/j.nanoen.2021.106000","volume":"85","author":"X M Wu","year":"2021","unstructured":"Wu X M, Li E L, Liu Y Q, et al. Artificial multisensory integration nervous system with haptic and iconic perception behaviors. Nano Energy, 2021, 85: 106000","journal-title":"Nano Energy"},{"key":"3336_CR79","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1038\/s42256-020-0159-4","volume":"2","author":"N Imam","year":"2020","unstructured":"Imam N, Cleland T A. Rapid online learning and robust recall in a neuromorphic olfactory circuit. Nat Mach Intell, 2020, 2: 181\u2013191","journal-title":"Nat Mach Intell"},{"key":"3336_CR80","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41586-019-1424-8","volume":"572","author":"J Pei","year":"2019","unstructured":"Pei J, Deng L, Song S, et al. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 2019, 572: 106\u2013111","journal-title":"Nature"},{"key":"3336_CR81","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1063\/1.100933","volume":"54","author":"W C Koscielniak","year":"1989","unstructured":"Koscielniak W C, Pelouard J L, Littlejohn M A. Dynamic behavior of photocarriers in a GaAs metal-semiconductor-metal photodetector with sub-half-micron electrode pattern. Appl Phys Lett, 1989, 54: 567\u2013569","journal-title":"Appl Phys Lett"},{"key":"3336_CR82","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1109\/JLT.2008.2008399","volume":"27","author":"A Beling","year":"2009","unstructured":"Beling A, Campbell J C. InP-based high-speed photodetectors. J Lightwave Technol, 2009, 27: 343\u2013355","journal-title":"J Lightwave Technol"},{"key":"3336_CR83","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1038\/nphoton.2010.40","volume":"4","author":"T Mueller","year":"2010","unstructured":"Mueller T, Xia F N, Avouris P. Graphene photodetectors for high-speed optical communications. Nat Photon, 2010, 4: 297\u2013301","journal-title":"Nat Photon"},{"key":"3336_CR84","doi-asserted-by":"publisher","first-page":"2804","DOI":"10.1021\/nl2011388","volume":"11","author":"A Urich","year":"2011","unstructured":"Urich A, Unterrainer K, Mueller T. Intrinsic response time of graphene photodetectors. Nano Lett, 2011, 11: 2804\u20132808","journal-title":"Nano Lett"},{"key":"3336_CR85","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1038\/nnano.2009.292","volume":"4","author":"F N Xia","year":"2009","unstructured":"Xia F N, Mueller T, Lin Y M, et al. Ultrafast graphene photodetector. Nat Nanotech, 2009, 4: 839\u2013843","journal-title":"Nat Nanotech"},{"key":"3336_CR86","doi-asserted-by":"publisher","first-page":"051102","DOI":"10.1063\/1.4975360","volume":"110","author":"N Youngblood","year":"2017","unstructured":"Youngblood N, Li M. Ultrafast photocurrent measurements of a black phosphorus photodetector. Appl Phys Lett, 2017, 110: 051102","journal-title":"Appl Phys Lett"},{"key":"3336_CR87","doi-asserted-by":"publisher","first-page":"1906899","DOI":"10.1002\/adma.201906899","volume":"32","author":"H L Park","year":"2020","unstructured":"Park H L, Kim H, Lim D, et al. Retina-inspired carbon nitride-based photonic synapses for selective detection of UV light. Adv Mater, 2020, 32: 1906899","journal-title":"Adv Mater"},{"key":"3336_CR88","doi-asserted-by":"publisher","first-page":"10294","DOI":"10.1021\/acsnano.9b03683","volume":"13","author":"S G Kim","year":"2019","unstructured":"Kim S G, Kim S H, Park J, et al. Infrared detectable MoS2 phototransistor and its application to artificial multilevel optic-neural synapse. ACS Nano, 2019, 13: 10294\u201310300","journal-title":"ACS Nano"},{"key":"3336_CR89","doi-asserted-by":"publisher","first-page":"2002653","DOI":"10.1002\/adma.202002653","volume":"32","author":"C Qian","year":"2020","unstructured":"Qian C, Choi Y, Choi Y J, et al. Oxygen-detecting synaptic device for realization of artificial autonomic nervous system for maintaining oxygen homeostasis. Adv Mater, 2020, 32: 2002653","journal-title":"Adv Mater"},{"key":"3336_CR90","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1002\/adma.201404794","volume":"27","author":"J Yang","year":"2015","unstructured":"Yang J, Chen J, Su Y J, et al. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition. Adv Mater, 2015, 27: 1316\u20131326","journal-title":"Adv Mater"},{"key":"3336_CR91","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1002\/adma.201504754","volume":"28","author":"B W Zhu","year":"2016","unstructured":"Zhu B W, Wang H, Liu Y Q, et al. Skin-inspired haptic memory arrays with an electrically reconfigurable architecture. Adv Mater, 2016, 28: 1559\u20131566","journal-title":"Adv Mater"},{"key":"3336_CR92","doi-asserted-by":"crossref","unstructured":"Ledda P, Santos L P, Chalmers A. A local model of eye adaptation for high dynamic range images. In: Proceedings of the 3rd International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa, 2004. 151\u2013160","DOI":"10.1145\/1029949.1029978"},{"key":"3336_CR93","doi-asserted-by":"crossref","unstructured":"Pattanaik S N, Ferwerda J A, Fairchild M D, et al. A multiscale model of adaptation and spatial vision for realistic image display. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, 1998. 287\u2013298","DOI":"10.1145\/280814.280922"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3336-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-021-3336-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3336-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T21:02:29Z","timestamp":1683234149000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-021-3336-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":93,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["3336"],"URL":"https:\/\/doi.org\/10.1007\/s11432-021-3336-8","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]},"assertion":[{"value":"31 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"141401"}}