{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:34:51Z","timestamp":1772120091891,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Science and Technology Department of Guizhou Province, China","award":["Qiankehe Foundation-ZK[2022]130."],"award-info":[{"award-number":["Qiankehe Foundation-ZK[2022]130."]}]},{"name":"Science and Technology Department of Guizhou Province, China","award":["Qiankehe Foundation-ZK[2022]130."],"award-info":[{"award-number":["Qiankehe Foundation-ZK[2022]130."]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13042-025-02788-6","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T08:18:10Z","timestamp":1756887490000},"page":"9733-9752","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TL-SNN: event-driven visual-tactile learning with temporal and location spiking neurons"],"prefix":"10.1007","volume":"16","author":[{"given":"Jing","family":"Yang","sequence":"first","affiliation":[]},{"given":"Baofan","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhidong","family":"Su","sequence":"additional","affiliation":[]},{"given":"Zhaohu","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"2788_CR1","first-page":"1","volume":"804","author":"M Hassaballah","year":"2019","unstructured":"Hassaballah M, Hosny KM (2019) Recent advances in computer vision. Stud Comput Intell 804:1\u201384","journal-title":"Studies in computational intelligence"},{"key":"2788_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.mechatronics.2017.11.002","volume":"48","author":"S Luo","year":"2017","unstructured":"Luo S, Bimbo J, Dahiya R, Liu H (2017) Robotic tactile perception of object properties: a review. Mechatronics 48:54\u201367. https:\/\/doi.org\/10.1016\/j.mechatronics.2017.11.002","journal-title":"Mechatronics"},{"key":"2788_CR3","doi-asserted-by":"publisher","unstructured":"Wang C, Liu C, Shang F, Niu S, Ke L, Zhang N, Ma B, Li R, Sun X, Zhang S (2022) Tactile sensing technology in bionic skin: areview. Biosens Bioelectr 114882 https:\/\/doi.org\/10.1016\/j.bios.2022.114882","DOI":"10.1016\/j.bios.2022.114882"},{"issue":"2","key":"2788_CR4","doi-asserted-by":"publisher","first-page":"2100074","DOI":"10.1002\/aisy.202100074","volume":"4","author":"S Gao","year":"2022","unstructured":"Gao S, Dai Y, Nathan A (2022) Tactile and vision perception for intelligent humanoids. Adv Intell Syst 4(2):2100074. https:\/\/doi.org\/10.1002\/aisy.202100074","journal-title":"Advanced Intelligent Systems"},{"issue":"22","key":"2788_CR5","doi-asserted-by":"publisher","first-page":"25973","DOI":"10.1109\/JSEN.2021.3119060","volume":"21","author":"R Sui","year":"2021","unstructured":"Sui R, Zhang L, Li T, Jiang Y (2021) Incipient slip detection method with vision-based tactile sensor based on distribution force and deformation. IEEE Sens J 21(22):25973\u201325985. https:\/\/doi.org\/10.1109\/JSEN.2021.3119060","journal-title":"IEEE Sens J"},{"key":"2788_CR6","doi-asserted-by":"publisher","unstructured":"Strubell E, Ganesh A, McCallum A (2019) Energy and policy considerations for deep learning in nlp. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/1906.02243. https:\/\/doi.org\/10.48550\/arXiv.1906.02243","DOI":"10.48550\/arXiv.1906.02243"},{"issue":"6","key":"2788_CR7","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MSP.2019.2928127","volume":"36","author":"SC Liu","year":"2019","unstructured":"Liu SC, Rueckauer B, Ceolini E, Huber A (2019) Delbruck T (2019) Event-driven sensing for efficient perception: vision and audition algorithms. IEEE Signal Process Mag 36(6):29\u201337. https:\/\/doi.org\/10.1109\/MSP.2019.2928127","journal-title":"IEEE Signal Process Mag"},{"key":"2788_CR8","doi-asserted-by":"crossref","unstructured":"Barranco F, Fermuller C, Aloimonos Y (2015) Bio-inspired motion estimation with event-driven sensors. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence. In: 13th international work-conference on artificial neural networks, IWANN 2015. pp 309\u2013321. Springer, Palma de Mallorca, Spain","DOI":"10.1007\/978-3-319-19258-1_27"},{"key":"2788_CR9","doi-asserted-by":"publisher","unstructured":"Rebecq H, Ranftl R, Koltun V, Scaramuzza D (2019) Events-to-video: Bringing modern computer vision to event cameras. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). pp 3857\u20133866, Long Beach, CA. https:\/\/doi.org\/10.1109\/CVPR.2019.00398","DOI":"10.1109\/CVPR.2019.00398"},{"issue":"9","key":"2788_CR10","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1038\/nmat4731","volume":"15","author":"C Bartolozzi","year":"2016","unstructured":"Bartolozzi C, Natale L, Nori F, Metta G (2016) Robots with a sense of touch. Nat Mater 15(9):921\u2013925. https:\/\/doi.org\/10.1038\/nmat4731","journal-title":"Nat Mater"},{"key":"2788_CR11","doi-asserted-by":"publisher","unstructured":"Messikommer N, Gehrig D, Loquercio A, Scaramuzza D (2020) Event-based asynchronous sparse convolutional networks. In: Computer vision ECCV 2020: 16th European conference. Computer vision ECCV 2020, pp 415\u2013431. Springer, Glasgow, UK. https:\/\/doi.org\/10.48550\/arXiv.2003.09148","DOI":"10.48550\/arXiv.2003.09148"},{"issue":"6","key":"2788_CR12","doi-asserted-by":"publisher","first-page":"4693","DOI":"10.1007\/s11063-021-10562-2","volume":"53","author":"D Auge","year":"2021","unstructured":"Auge D, Hille J, Mueller E, Knoll A (2021) A survey of encoding techniques for signal processing in spiking neural networks. Neural Process Lett 53(6):4693\u20134710. https:\/\/doi.org\/10.1007\/s11063-021-10562-2","journal-title":"Neural Process Lett"},{"issue":"7","key":"2788_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/brainsci12070863","volume":"12","author":"K Yamazaki","year":"2022","unstructured":"Yamazaki K, Vo-Ho V-K, Bulsara D, Le N (2022) Spiking neural networks and their applications: a review. Brain Sci 12(7):863. https:\/\/doi.org\/10.3390\/brainsci12070863","journal-title":"Brain Sci"},{"key":"2788_CR14","doi-asserted-by":"publisher","unstructured":"Taunyazov T, Sng W, See HH, Lim B, Kuan J, Ansari AF, Tee BC, Soh H (2020) Event-driven visual-tactile sensing and learning for robots. In: Robotics: Science and Systems 2020. Corvalis, Oregon, USA. https:\/\/doi.org\/10.48550\/arXiv.2009.07083","DOI":"10.48550\/arXiv.2009.07083"},{"key":"2788_CR15","doi-asserted-by":"publisher","unstructured":"Kang P, Banerjee S, Chopp H, Katsaggelos A, Cossairt O (2022) Event-driven tactile learning with location spiking neurons. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139 IEEE, Padua, Italy. https:\/\/doi.org\/10.3389\/fnins.2023.1127537","DOI":"10.3389\/fnins.2023.1127537"},{"issue":"1","key":"2788_CR16","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/TCDS.2016.2612721","volume":"10","author":"AJ Glover","year":"2018","unstructured":"Glover AJ (2018) Wyeth GF (2018) Toward lifelong affordance learning using a distributed markov model. IEEE Trans Cogn Dev Syst 10(1):44\u201355. https:\/\/doi.org\/10.1109\/TCDS.2016.2612721","journal-title":"IEEE Transactions on Cognitive and Developmental Systems"},{"issue":"1","key":"2788_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1109\/TNNLS.2020.2977497","volume":"32","author":"G Sun","year":"2021","unstructured":"Sun G, Cong Y, Zhang Y, Zhao G, Fu Y (2021) Continual multiview task learning via deep matrix factorization. IEEE Trans Neural Netw Learn Syst 32(1):139\u2013150. https:\/\/doi.org\/10.1109\/TNNLS.2020.2977497","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"2788_CR18","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1109\/TAFFC.2017.2771234","volume":"8","author":"R Xia","year":"2017","unstructured":"Xia R, Jiang J, He H (2017) Distantly supervised lifelong learning for large-scale social media sentiment analysis. IEEE Trans Affect Comput 8(4):480\u2013491. https:\/\/doi.org\/10.1109\/TAFFC.2017.2771234","journal-title":"IEEE Trans Affect Comput"},{"key":"2788_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108176","volume":"121","author":"J Dong","year":"2022","unstructured":"Dong J, Cong Y, Sun G, Zhang T (2022) Lifelong robotic visual-tactile perception learning. Patt Recogn 121:108176. https:\/\/doi.org\/10.1016\/j.patcog.2021.108176","journal-title":"Pattern Recogn"},{"key":"2788_CR20","doi-asserted-by":"publisher","unstructured":"Li J,Dong S, Adelson E (2018) Slip detection with combined tactile and visual information. In: 2018 IEEE international conference on robotics and automation (ICRA), pp 7772\u20137777. IEEE, Brisbane, QLD, Australia. https:\/\/doi.org\/10.1109\/ICRA.2018.8460495","DOI":"10.1109\/ICRA.2018.8460495"},{"key":"2788_CR21","doi-asserted-by":"publisher","unstructured":"Lee MA, Zhu Y, Srinivasan K, Shah P, Savarese S, Fei-Fei L, Garg A, Bohg J (2019) Making sense of vision and touch: self-supervised learning of multimodal representations for contact-rich tasks. In: 2019 international conference on robotics and automation (ICRA), pp 8943\u20138950. https:\/\/doi.org\/10.1109\/ICRA.2019.8793485","DOI":"10.1109\/ICRA.2019.8793485"},{"key":"2788_CR22","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.inffus.2022.11.032","volume":"92","author":"RP Babadian","year":"2023","unstructured":"Babadian RP, Faez K, Amiri M, Falotico E (2023) Fusion of tactile and visual information in deep learning models for object recognition. Inf Fus 92:313\u2013325. https:\/\/doi.org\/10.1016\/j.inffus.2022.11.032","journal-title":"Information Fusion"},{"issue":"1","key":"2788_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0957-4158(98)00045-2","volume":"9","author":"MH Lee","year":"1999","unstructured":"Lee MH, Nicholls HR (1999) Review article tactile sensing for mechatronics a state of the art survey. Mechatronics 9(1):1\u201331. https:\/\/doi.org\/10.1016\/S0957-4158(98)00045-2","journal-title":"Mechatronics"},{"issue":"6","key":"2788_CR24","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TRO.2020.3003230","volume":"36","author":"Q Li","year":"2020","unstructured":"Li Q, Kroemer O, Su Z, Veiga FF, Kaboli M, Ritter HJ (2020) A review of tactile information: perception and action through touch. IEEE Trans Rob 36(6):1619\u20131634. https:\/\/doi.org\/10.1109\/TRO.2020.3003230","journal-title":"IEEE Trans Rob"},{"issue":"19","key":"2788_CR25","doi-asserted-by":"publisher","first-page":"21131","DOI":"10.1109\/JSEN.2021.3100645","volume":"21","author":"NF Lepora","year":"2021","unstructured":"Lepora NF (2021) Soft biomimetic optical tactile sensing with the tactip: a review. IEEE Sens J 21(19):21131\u201321143. https:\/\/doi.org\/10.1109\/JSEN.2021.3100645","journal-title":"IEEE Sens J"},{"key":"2788_CR26","doi-asserted-by":"publisher","unstructured":"Ma D, Donlon E, Dong S, Rodriguez A (2018) Dense tactile force distribution estimation using gelslim and inverse fem. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/1810.04621. https:\/\/doi.org\/10.1109\/ICRA.2019.8794113","DOI":"10.1109\/ICRA.2019.8794113"},{"key":"2788_CR27","doi-asserted-by":"publisher","unstructured":"Yuan W, Li R, Srinivasan MA, Adelson EH (2015) Measurement of shear and slip with a gelsight tactile sensor. In: 2015 IEEE international conference on robotics and automation (ICRA), pp 304\u2013311. IEEE, Seattle, WA, USA. https:\/\/doi.org\/10.1109\/ICRA.2015.7139016","DOI":"10.1109\/ICRA.2015.7139016"},{"issue":"4","key":"2788_CR28","doi-asserted-by":"publisher","first-page":"3340","DOI":"10.1109\/ICRA.2018.8460495","volume":"3","author":"JW James","year":"2018","unstructured":"James JW, Pestell N, Lepora NF (2018) Slip detection with a biomimetic tactile sensor. IEEE Robot Autom Lett 3(4):3340\u20133346. https:\/\/doi.org\/10.1109\/ICRA.2018.8460495","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"2","key":"2788_CR29","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1109\/TRO.2020.3031245","volume":"37","author":"JW James","year":"2020","unstructured":"James JW, Lepora NF (2020) Slip detection for grasp stabilization with a multifingered tactile robot hand. IEEE Trans Rob 37(2):506\u2013519. https:\/\/doi.org\/10.1109\/TRO.2020.3031245","journal-title":"IEEE Trans Rob"},{"key":"2788_CR30","doi-asserted-by":"publisher","unstructured":"Taunyazov T, Koh HF, Wu Y, Cai C, Soh H (2019) Towards effective tactile identification of textures using a hybrid touch approach. In: 2019 international conference on robotics and automation (ICRA), pp 4269\u20134275. IEEE, Montreal, QC, Canada. https:\/\/doi.org\/10.1109\/ICRA.2019.8793967","DOI":"10.1109\/ICRA.2019.8793967"},{"key":"2788_CR31","doi-asserted-by":"publisher","unstructured":"Kerzel M, Strahl E, Gaede C, Gasanov E, Wermter S (2019) Neuro-robotic haptic object classification by active exploration on a novel dataset. In: 2019 international joint conference on neural networks (IJCNN), pp 1\u20138. IEEE, Budapest, Hungary. https:\/\/doi.org\/10.1109\/IJCNN.2019.8852359","DOI":"10.1109\/IJCNN.2019.8852359"},{"issue":"24","key":"2788_CR32","doi-asserted-by":"publisher","first-page":"30805","DOI":"10.1109\/JSEN.2023.3329559","volume":"23","author":"J Yang","year":"2023","unstructured":"Yang J, Liu T, Ren Y, Hou Q, Li S, Hu J (2023) Am-sgcn: Tactile object recognition for adaptive multichannel spiking graph convolutional neural networks. IEEE Sens J 23(24):30805\u201330820. https:\/\/doi.org\/10.1109\/JSEN.2023.3329559","journal-title":"IEEE Sens J"},{"key":"2788_CR33","doi-asserted-by":"publisher","unstructured":"Johnson MK, Adelson EH (2009) Retrographic sensing for the measurement of surface texture and shape. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 1070\u20131077. https:\/\/doi.org\/10.1109\/CVPR.2009.5206534","DOI":"10.1109\/CVPR.2009.5206534"},{"issue":"8","key":"2788_CR34","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1163\/156855308X314533","volume":"22","author":"N Wettels","year":"2008","unstructured":"Wettels N, Santos VJ, Johansson RS, Loeb GE (2008) Biomimetic tactile sensor array. Adv Robot 22(8):829\u2013849. https:\/\/doi.org\/10.1163\/156855308X314533","journal-title":"Advanced robotics"},{"key":"2788_CR35","doi-asserted-by":"publisher","unstructured":"Taunyazov T, Song LS, Lim E, See HH, Lee D, Tee BC, Soh H (2021) Extended tactile perception: vibration sensing through tools and grasped objects. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 1755\u20131762. IEEE, Prague, Czech Republic. https:\/\/doi.org\/10.1109\/IROS51168.2021.9636677","DOI":"10.1109\/IROS51168.2021.9636677"},{"key":"2788_CR36","doi-asserted-by":"publisher","unstructured":"Wang S, Wu J, Sun X, Yuan W, Freeman WT, Tenenbaum JB, Adelson EH (2018) 3d shape perception from monocular vision, touch, and shape priors. In: 2018 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 1606\u20131613. IEEE, Madrid, Spain. https:\/\/doi.org\/10.1109\/IROS.2018.8593430","DOI":"10.1109\/IROS.2018.8593430"},{"key":"2788_CR37","doi-asserted-by":"publisher","unstructured":"Gao Y, Hendricks LA, Kuchenbecker KJ, Darrell T (2016) Deep learning for tactile understanding from visual and haptic data. In: 2016 IEEE international conference on robotics and automation (ICRA), pp. 536\u2013543. IEEE, Stockholm, Sweden. https:\/\/doi.org\/10.1109\/ICRA.2016.7487176","DOI":"10.1109\/ICRA.2016.7487176"},{"issue":"4","key":"2788_CR38","doi-asserted-by":"publisher","first-page":"3300","DOI":"10.1109\/LRA.2018.2852779","volume":"3","author":"R Calandra","year":"2018","unstructured":"Calandra R, Owens A, Jayaraman D, Lin J, Yuan W, Malik J, Adelson EH, Levine S (2018) More than a feeling: learning to grasp and regrasp using vision and touch. IEEE Robot Autom Lett 3(4):3300\u20133307. https:\/\/doi.org\/10.1109\/LRA.2018.2852779","journal-title":"IEEE Robotics and Automation Letters"},{"key":"2788_CR39","doi-asserted-by":"publisher","unstructured":"Luo S, Yuan W, Adelson E, Cohn AG, Fuentes R (2018) Vitac: feature sharing between vision and tactile sensing for cloth texture recognition. In: 2018 IEEE international conference on robotics and automation (ICRA), pp 2722\u20132727. IEEE, Brisbane, QLD, Australia. https:\/\/doi.org\/10.1109\/ICRA.2018.8460494","DOI":"10.1109\/ICRA.2018.8460494"},{"key":"2788_CR40","doi-asserted-by":"publisher","first-page":"774","DOI":"10.3389\/fnins.2018.00774","volume":"12","author":"M Pfeiffer","year":"2018","unstructured":"Pfeiffer M, Pfeil T (2018) Deep learning with spiking neurons: opportunities and challenges. Front Neurosci 12:774. https:\/\/doi.org\/10.3389\/fnins.2018.00774","journal-title":"Front Neurosci"},{"key":"2788_CR41","doi-asserted-by":"publisher","unstructured":"Taunyazov T, Chua Y, Gao R, Soh H, Wu Y (2020) Fast texture classification using tactile neural coding and spiking neural network. In: 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS). pp 9890\u20139895. IEEE, Las Vegas, NV, USA. https:\/\/doi.org\/10.1109\/IROS45743.2020.9340693","DOI":"10.1109\/IROS45743.2020.9340693"},{"key":"2788_CR42","doi-asserted-by":"publisher","unstructured":"Gu F, Sng W, Taunyazov T, Soh H (2020) Tactilesgnet: a spiking graph neural network for event-based tactile object recognition. In: 2020 IEEE\/RSJ international conference on intelligent robots and systems (IROS), pp 9876\u20139882. IEEE, Las Vegas, NV, USA. https:\/\/doi.org\/10.1109\/IROS45743.2020.9341421","DOI":"10.1109\/IROS45743.2020.9341421"},{"issue":"220711","key":"2788_CR43","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.11999\/JEIT220711","volume":"45","author":"JXLS Yang Jing","year":"2023","unstructured":"Yang Jing JXLS (2023) Spiking neural network robot tactile object recognition method with regularization constraints. J Electr Inform Technol 45(220711):2595. https:\/\/doi.org\/10.11999\/JEIT220711","journal-title":"Journal of Electronics & Information Technology"},{"key":"2788_CR44","doi-asserted-by":"publisher","unstructured":"Shrestha SB, Orchard G (2018) Slayer: Spike layer error reassignment in time. Adv Neural Inform Process Syst 31 https:\/\/doi.org\/10.48550\/arXiv.1810.08646","DOI":"10.48550\/arXiv.1810.08646"},{"key":"2788_CR45","unstructured":"Gr\u00fcning A, Bohte S (2014) Spiking neural networks: Principles and challenges. In: 22nd European symposium on artificial neural networks, computational intelligence and machine learning ESANN. Bruges Belgium"},{"key":"2788_CR46","doi-asserted-by":"publisher","first-page":"331","DOI":"10.3389\/fnins.2018.00331","volume":"12","author":"Y Wu","year":"2018","unstructured":"Wu Y, Deng L, Li G, Zhu J, Shi L (2018) Spatio-temporal backpropagation for training high-performance spiking neural networks. Front Neurosci 12:331. https:\/\/doi.org\/10.3389\/fnins.2018.00331","journal-title":"Front Neurosci"},{"issue":"5","key":"2788_CR47","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/JPROC.2021.3067593","volume":"109","author":"M Davies","year":"2021","unstructured":"Davies M, Wild A, Orchard G, Sandamirskaya Y, Guerra GAF, Joshi P, Plank P, Risbud SR (2021) Advancing neuromorphic computing with loihi: a survey of results and outlook. Proc IEEE 109(5):911\u2013934. https:\/\/doi.org\/10.1109\/JPROC.2021.3067593","journal-title":"Proc IEEE"},{"key":"2788_CR48","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neunet.2018.12.002","volume":"111","author":"A Tavanaei","year":"2019","unstructured":"Tavanaei A, Ghodrati M, Kheradpisheh SR, Masquelier T, Maida A (2019) Deep learning in spiking neural networks. Neural Netw 111:47\u201363. https:\/\/doi.org\/10.1016\/j.neunet.2018.12.002","journal-title":"Neural Netw"},{"key":"2788_CR49","first-page":"6316","volume":"139","author":"Y Li","year":"2021","unstructured":"Li Y, Deng S, Dong X, Gong R, Gu S (2021) A free lunch from ann: Towards efficient, accurate spiking neural networks calibration. The 38th international conference on machine learning 139:6316\u20136325","journal-title":"The 38th International Conference on Machine Learning"},{"key":"2788_CR50","doi-asserted-by":"publisher","unstructured":"Ding J, Yu Z, Tian Y, Huang T (2021) Optimal ann-snn conversion for fast and accurate inference in deep spiking neural networks. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/2105.11654. https:\/\/doi.org\/10.48550\/arXiv.2105.11654","DOI":"10.48550\/arXiv.2105.11654"},{"issue":"6","key":"2788_CR51","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/MSP.2019.2931595","volume":"36","author":"EO Neftci","year":"2019","unstructured":"Neftci EO, Mostafa H, Zenke F (2019) Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process Mag 36(6):51\u201363. https:\/\/doi.org\/10.1109\/MSP.2019.2931595","journal-title":"IEEE Signal Process Mag"},{"issue":"6197","key":"2788_CR52","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1126\/science.1254642","volume":"345","author":"PA Merolla","year":"2014","unstructured":"Merolla PA, Arthur JV, Alvarez-Icaza R, Cassidy AS, Sawada J, Akopyan F, Jackson BL, Imam N, Guo C, Nakamura Y, Brezzo B, Vo I, Esser SK, Appuswamy R, Taba B, Amir A, Flickner MD, Risk WP, Manohar R, Modha DS (2014) A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197):668\u2013673. https:\/\/doi.org\/10.1126\/science.1254642","journal-title":"Science"},{"issue":"1","key":"2788_CR53","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies M, Srinivasa N, Lin T-H, Chinya G, Cao Y, Choday SH, Dimou G, Joshi P, Imam N, Jain S (2018) Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1):82\u201399. https:\/\/doi.org\/10.1109\/MM.2018.112130359","journal-title":"IEEE Micro"},{"issue":"1","key":"2788_CR54","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/TETCI.2018.2872014","volume":"5","author":"N Rathi","year":"2018","unstructured":"Rathi N, Roy K (2018) Stdp based unsupervised multimodal learning with cross-modal processing in spiking neural networks. IEEE Trans Emerg Top Comput Intell 5(1):143\u2013153. https:\/\/doi.org\/10.1109\/TETCI.2018.2872014","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"issue":"24","key":"2788_CR55","doi-asserted-by":"publisher","first-page":"2200481","DOI":"10.1002\/adma.202200481","volume":"34","author":"J Zhu","year":"2022","unstructured":"Zhu J, Zhang X, Wang R, Wang M, Chen P, Cheng L, Wu Z, Wang Y, Liu Q, Liu M (2022) A heterogeneously integrated spiking neuron array for multimode-fused perception and object classification. Adv Mater 34(24):2200481. https:\/\/doi.org\/10.1002\/adma.202200481","journal-title":"Adv Mater"},{"key":"2788_CR56","doi-asserted-by":"publisher","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:http:\/\/arxiv.org\/abs\/1406.1078. https:\/\/doi.org\/10.48550\/arXiv.1406.1078","DOI":"10.48550\/arXiv.1406.1078"},{"key":"2788_CR57","doi-asserted-by":"publisher","unstructured":"Gandarias JM, Pastor F, Garcia-Cerezo AJ, G\u00f3mez-de-Gabriel JM (2019) Active tactile recognition of deformable objects with 3d convolutional neural networks. In: 2019 IEEE World Haptics Conference (WHC), pp 551\u2013555. IEEE, Tokyo, Japan. https:\/\/doi.org\/10.1109\/WHC.2019.8816162","DOI":"10.1109\/WHC.2019.8816162"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02788-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02788-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02788-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T09:40:27Z","timestamp":1765618827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02788-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,3]]},"references-count":57,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["2788"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02788-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4605061\/v1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,3]]},"assertion":[{"value":"19 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 September 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"Not applicable. The data used in this study are derived from a publicly available dataset. No individual person\u2019s data were used or disclosed in any form.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}]}}