{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T23:48:35Z","timestamp":1769212115722,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031301070","type":"print"},{"value":"9783031301087","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-30108-7_24","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:03:04Z","timestamp":1681272184000},"page":"279-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["High-Accuracy and\u00a0Energy-Efficient Action Recognition with\u00a0Deep Spiking Neural Network"],"prefix":"10.1007","author":[{"given":"Jingren","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jingjing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xie","family":"Di","sequence":"additional","affiliation":[]},{"given":"Shiliang","family":"Pu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"issue":"9","key":"24_CR1","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10(9), 1659\u20131671 (1997)","journal-title":"Neural Netw."},{"issue":"6197","key":"24_CR2","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1126\/science.1254642","volume":"345","author":"PA Merolla","year":"2014","unstructured":"Merolla, P.A., Arthur, J.V., Icaza, R.A., et al.: A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345(6197), 668\u2013673 (2014)","journal-title":"Science"},{"key":"24_CR3","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 572, 106\u2013124 (2019)","journal-title":"Nature"},{"key":"24_CR4","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3389\/fnins.2017.00682","volume":"11","author":"B Rueckauer","year":"2017","unstructured":"Rueckauer, B., Lungu, I.A., Hu, Y., Pfeiffer, M., Liu, S.: Conversion of continuous-valued deep networks to efficient event-driven networks for image classification. Front. Neurosci. 11, 682\u2013693 (2017)","journal-title":"Front. Neurosci."},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Kim, S., Park, S., Na, B., Yoon, S.: Spiking-YOLO: spiking neural network for energy-efficient object detection. In: 2020 AAAI Conference on Artificial Intelligence, New York, pp. 11270\u201311277, February 2020","DOI":"10.1609\/aaai.v34i07.6787"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, J., Yan, J., Wang, C., Pu, S.: Deep spiking neural network for high-accuracy and energy-efficient face action unit recognition. In: 2021 International Joint Conference on Neural Networks, Virtual Event, pp. 1\u20137, July 2021","DOI":"10.1109\/IJCNN52387.2021.9533451"},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"George, A.M., Banerjee, D., Dey, S., Mukherjee, A., Balamurali, P.: A reservoir-based convolutional spiking neural network for gesture recognition from DVS input. In: 2020 International Joint Conference on Neural Networks, pp. 1\u20139, July 2020","DOI":"10.1109\/IJCNN48605.2020.9206681"},{"key":"24_CR8","first-page":"69","volume":"11","author":"P Priyadarshini","year":"2017","unstructured":"Priyadarshini, P., Kaushik, R.: Learning to generate sequences with combination of hebbian and non-hebbian plasticity in recurrent spiking neural networks. Front. Neurosci. 11, 69 (2017)","journal-title":"Front. Neurosci."},{"issue":"1","key":"24_CR9","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1109\/TNNLS.2021.3095724","volume":"34","author":"J Wu","year":"2023","unstructured":"Wu, J., Chua, Y., Zhang, M., et al.: A tandem learning rule for effective training and rapid inference of deep spiking neural networks. IEEE Trans. Neural Netw. Learn. Syst. 34(1), 446\u2013460 (2023)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"24_CR10","doi-asserted-by":"publisher","first-page":"7824","DOI":"10.1109\/TPAMI.2021.3114196","volume":"44","author":"J Wu","year":"2022","unstructured":"Wu, J., Xu, C., Zhou, D., et al.: Progressive tandem learning for pattern recognition with deep spiking neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7824\u20137840 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"24_CR11","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1016\/j.neucom.2016.08.037","volume":"216","author":"SS Liew","year":"2016","unstructured":"Liew, S.S., Khalil-Hani, M., Bakhteri, R.: Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems. Neurocomputing 216, 718\u2013734 (2016)","journal-title":"Neurocomputing"},{"key":"24_CR12","doi-asserted-by":"publisher","first-page":"508","DOI":"10.3389\/fnins.2016.00508","volume":"10","author":"JH Lee","year":"2016","unstructured":"Lee, J.H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 508 (2016)","journal-title":"Front. Neurosci."},{"key":"24_CR13","unstructured":"Zhang, W., Li, P.: Spike-train level backpropagation for training deep recurrent spiking neural networks. In: 2019 Conference on Neural Information Processing Systems, Montreal, Vancouver, pp. 1\u201312, December 2019"},{"key":"24_CR14","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.image.2018.09.003","volume":"71","author":"C Ma","year":"2018","unstructured":"Ma, C., Chen, M., Kira, Z., AlRegib, G., et al.: TS-LSTM and temporal-inception: exploiting spatiotemporal dynamics for activity recognition. Sig. Process. Image Commun. 71, 76\u201387 (2018)","journal-title":"Sig. Process. Image Commun."},{"key":"24_CR15","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: 2015 Proceedings of the IEEE International Conference on Computer Vision, Santiago, pp. 4489\u20134497, December 2015","DOI":"10.1109\/ICCV.2015.510"},{"key":"24_CR16","unstructured":"Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: 2014 International Conference on Neural Information Processing Systems, Montreal, pp. 1\u201311, December 2014"},{"key":"24_CR17","unstructured":"Samadzadeh, A., Far, F., Javadi, A., et al.: Convolutional spiking neural networks for spatio-temporal feature extraction, pp. 1\u201310, January 2021"},{"key":"24_CR18","first-page":"1","volume":"12","author":"P Priyadarshini","year":"2018","unstructured":"Priyadarshini, P., Narayan, S.: Learning to recognize actions from limited training examples using a recurrent spiking neural model. Front. Neurosci. 12, 1\u201315 (2018)","journal-title":"Front. Neurosci."},{"key":"24_CR19","unstructured":"NVIDIA Tesla V100 GPU architecture (2017)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30108-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T03:20:28Z","timestamp":1729221628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30108-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301070","9783031301087"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30108-7_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"359","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"44% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.65","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}