{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:28:12Z","timestamp":1770917292506,"version":"3.50.1"},"reference-count":110,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20230807120800001"],"award-info":[{"award-number":["JCYJ20230807120800001"]}]},{"name":"2023 Shenzhen sustainable supporting funds for colleges and universities","award":["20231121165240001"],"award-info":[{"award-number":["20231121165240001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cogn. Dev. Syst."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tcds.2024.3500018","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T13:47:46Z","timestamp":1732024066000},"page":"644-658","source":"Crossref","is-referenced-by-count":4,"title":["Spatial\u2013Temporal Spiking Feature Pruning in Spiking Transformer"],"prefix":"10.1109","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4454-6630","authenticated-orcid":false,"given":"Zhaokun","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-1905","authenticated-orcid":false,"given":"Kaiwei","family":"Che","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4776-7397","authenticated-orcid":false,"given":"Jun","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0904-8524","authenticated-orcid":false,"given":"Man","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8994-431X","authenticated-orcid":false,"given":"Guoqi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2120-5588","authenticated-orcid":false,"given":"Li","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-1207","authenticated-orcid":false,"given":"Guibo","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-6800","authenticated-orcid":false,"given":"Yuesheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1254642"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1424-8"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01245"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3114196"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3241201"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.07.008"},{"key":"ref9","first-page":"21056","article-title":"Deep residual learning in spiking neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Fang","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3119238"},{"key":"ref11","article-title":"Spikformer: When spiking neural network meets transformer","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Zhou","year":"2023"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00266"},{"key":"ref13","first-page":"32160","article-title":"GLIF: A unified gated leaky integrate-and-fire neuron for spiking neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"35","author":"Yao","year":"2022"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"ref15","article-title":"Optimal ANN-SNN conversion for high-accuracy and ultra-low-latency spiking neural networks","author":"Bu","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_3"},{"key":"ref17","first-page":"16253","article-title":"AutoSNN: Towards energy-efficient spiking neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Na","year":"2022"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2024.1372257"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3477145.3477157"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534111"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/236"},{"key":"ref22","article-title":"Spikingformer: Spike-driven residual learning for transformer-based spiking neural network","author":"Zhou","year":"2023"},{"key":"ref23","article-title":"Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Yao","year":"2023"},{"key":"ref24","article-title":"Enhancing the performance of transformer-based spiking neural networks by improved downsampling with precise gradient backpropagation","author":"Zhou","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00169"},{"key":"ref26","article-title":"Spikebert: A language spikformer trained with two-stage knowledge distillation from bert","author":"Lv","year":"2023"},{"key":"ref27","article-title":"Spikformer v2 Join the high accuracy club on ImageNet with an SNN ticket","author":"Zhou","year":"2024"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00550"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adi1480"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/tetci.2024.3393367"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19775-8_7"},{"key":"ref32","first-page":"3701","article-title":"State transition of dendritic spines improves learning of sparse spiking neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Chen","year":"2022"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3263826"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01082"},{"key":"ref35","article-title":"Token merging: Your vit but faster","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Bolya","year":"2023"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00010"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01760"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00508"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011311"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2018.00331"},{"key":"ref41","article-title":"A unified framework for soft threshold pruning","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Chen","year":"2022"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0788-3"},{"key":"ref43","article-title":"Spiking deep networks with LIF neurons","author":"Hunsberger","year":"2015"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00682"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01212"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/347"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3372613"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01357"},{"key":"ref49","article-title":"SpikeGPT: Generative pre-trained language model with spiking neural networks","author":"Zhu","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.106092"},{"key":"ref51","article-title":"SLAYER: Spike layer error reassignment in time","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Shrestha","year":"2018"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00119"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2931595"},{"key":"ref54","first-page":"14516","article-title":"Training feedback spiking neural networks by implicit differentiation on the equilibrium state","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Xiao","year":"2021"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref59","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","author":"Zhu","year":"2020"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2022.3206108"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01212"},{"key":"ref64","first-page":"3690","article-title":"Power-bert: Accelerating bert inference via progressive word-vector elimination","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Goyal","year":"2020"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539260"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.01390"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_24"},{"key":"ref68","first-page":"24898","article-title":"IA-RED2: Interpretability-aware redundancy reduction for vision transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Pan","year":"2021"},{"key":"ref69","article-title":"SAIT: Sparse vision transformers through adaptive token pruning","author":"Li","year":"2022"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01185"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_26"},{"key":"ref72","article-title":"Centroid transformers: Learning to abstract with attention","author":"Wu","year":"2021"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00996"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01054"},{"key":"ref75","first-page":"5156","article-title":"Transformers are RNNs: Fast autoregressive transformers with linear attention","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Katharopoulos","year":"2020"},{"key":"ref76","first-page":"3531","article-title":"Efficient attention: Attention with linear complexities","volume-title":"Proc. IEEE\/CVF Winter Conf. Appl. Comput. Vis. (WACV)","author":"Shen","year":"2021"},{"key":"ref77","article-title":"Q-VIT: Fully differentiable quantization for vision transformer","author":"Li","year":"2022"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/164"},{"key":"ref79","article-title":"Learning to merge tokens in vision transformers","author":"Renggli","year":"2022"},{"key":"ref80","article-title":"Tokenlearner: Adaptive space-time tokenization for videos","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","author":"Ryoo","year":"2021"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104614"},{"key":"ref82","article-title":"Spike-driven transformer","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"36","author":"Yao","year":"2024"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00400"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004485"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/221"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3109064"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611838"},{"key":"ref88","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Zagoruyko","year":"2016"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01554"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2017.00309"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01552"},{"key":"ref92","first-page":"13937","article-title":"DynamicViT: Efficient vision transformers with dynamic token sparsification","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"34","author":"Rao","year":"2021"},{"key":"ref93","article-title":"Spike-driven transformer v2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Yao","year":"2023"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.781"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2015.00437"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_24"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00516"},{"issue":"241","key":"ref99","first-page":"1","article-title":"Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks","volume":"22","author":"Hoefler","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref100","article-title":"Occam\u2019s razor","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"13","author":"Rasmussen","year":"2000"},{"key":"ref101","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw50498.2020.00359"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref106","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"139","author":"Touvron","year":"2021"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247810"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73411-3_15"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/tcds.2024.3422873"}],"container-title":["IEEE Transactions on Cognitive and Developmental Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7274989\/11023974\/10758407.pdf?arnumber=10758407","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T18:32:59Z","timestamp":1765909979000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10758407\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":110,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tcds.2024.3500018","relation":{},"ISSN":["2379-8920","2379-8939"],"issn-type":[{"value":"2379-8920","type":"print"},{"value":"2379-8939","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}