{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:16:10Z","timestamp":1750220170494,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1954364"],"award-info":[{"award-number":["1954364"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"DOE U.S. Department of Energy","doi-asserted-by":"publisher","award":["77902"],"award-info":[{"award-number":["77902"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,27]]},"DOI":"10.1145\/3546790.3546814","type":"proceedings-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:10:51Z","timestamp":1662610251000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Think Fast: Time Control in Varying Paradigms of Spiking Neural Networks"],"prefix":"10.1145","author":[{"given":"Steven C.","family":"Nesbit","sequence":"first","affiliation":[{"name":"Drexel University, USA"}]},{"given":"Andrew","family":"O'Brien","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}]},{"given":"Jocelyn","family":"Rego","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}]},{"given":"Gavin","family":"Parpart","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, USA"}]},{"given":"Carlos","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, USA"}]},{"given":"Garrett T.","family":"Kenyon","sequence":"additional","affiliation":[{"name":"Los Alamos National Laboratory, USA"}]},{"given":"Edward","family":"Kim","sequence":"additional","affiliation":[{"name":"Drexel University, USA"}]},{"given":"Terrence C.","family":"Stewart","sequence":"additional","affiliation":[{"name":"National Research Council of Canada, Canada"}]},{"given":"Yijing","family":"Watkins","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2014.6889903"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2015.2474396"},{"key":"e_1_3_2_1_3_1","volume-title":"Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051(2020).","author":"F\u00a0Wolff Anthony Lasse","year":"2020","unstructured":"Lasse F\u00a0Wolff Anthony , Benjamin Kanding , and Raghavendra Selvan . 2020 . Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051(2020). Lasse F\u00a0Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan. 2020. Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051(2020)."},{"key":"#cr-split#-e_1_3_2_1_4_1.1","doi-asserted-by":"crossref","unstructured":"Rui Ara\u00fajo Nicolai Waniek and Jorg Conradt. 2014. Development of a Dynamically Extendable SpiNNaker Chip Computing Module. 821-828. https:\/\/doi.org\/10.1007\/978-3-319-11179-7_103 10.1007\/978-3-319-11179-7_103","DOI":"10.1007\/978-3-319-11179-7_103"},{"key":"#cr-split#-e_1_3_2_1_4_1.2","doi-asserted-by":"crossref","unstructured":"Rui Ara\u00fajo Nicolai Waniek and Jorg Conradt. 2014. Development of a Dynamically Extendable SpiNNaker Chip Computing Module. 821-828. https:\/\/doi.org\/10.1007\/978-3-319-11179-7_103","DOI":"10.1007\/978-3-319-11179-7_103"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2013.00048"},{"key":"e_1_3_2_1_6_1","volume-title":"Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates","author":"Brown Tom","year":"1877","unstructured":"Tom Brown , Benjamin Mann , Nick Ryder , Melanie Subbiah , Jared\u00a0 D Kaplan , Prafulla Dhariwal , Arvind Neelakantan , Pranav Shyam , Girish Sastry , Amanda Askell , Sandhini Agarwal , Ariel Herbert-Voss , Gretchen Krueger , Tom Henighan , Rewon Child , Aditya Ramesh , Daniel Ziegler , Jeffrey Wu , Clemens Winter , Chris Hesse , Mark Chen , Eric Sigler , Mateusz Litwin , Scott Gray , Benjamin Chess , Jack Clark , Christopher Berner , Sam McCandlish , Alec Radford , Ilya Sutskever , and Dario Amodei . 2020. Language Models are Few-Shot Learners . In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates , Inc ., 1877 \u20131901. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared\u00a0D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H.\u00a0Larochelle, M.\u00a0Ranzato, R.\u00a0Hadsell, M.F. Balcan, and H.\u00a0Lin (Eds.). Vol.\u00a033. Curran Associates, Inc., 1877\u20131901. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"e_1_3_2_1_7_1","volume-title":"A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.Biological cybernetics 95, 1","author":"Burkitt N.","year":"2006","unstructured":"Anthony\u00a0 N. Burkitt . 2006. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.Biological cybernetics 95, 1 ( 2006 ), 1\u201319. https:\/\/doi.org\/10.1007\/s00422-006-0068-6 10.1007\/s00422-006-0068-6 Anthony\u00a0N. Burkitt. 2006. A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input.Biological cybernetics 95, 1 (2006), 1\u201319. https:\/\/doi.org\/10.1007\/s00422-006-0068-6"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0788-3"},{"key":"e_1_3_2_1_9_1","volume-title":"Learning Spiking Neural Network Models of Drosophila Olfaction. In International Conference on Neuromorphic Systems","author":"Carter John","year":"2020","unstructured":"John Carter , Jocelyn Rego , Daniel Schwartz , Vikas Bhandawat , and Edward Kim . 2020 . Learning Spiking Neural Network Models of Drosophila Olfaction. In International Conference on Neuromorphic Systems 2020. 1\u20135. John Carter, Jocelyn Rego, Daniel Schwartz, Vikas Bhandawat, and Edward Kim. 2020. Learning Spiking Neural Network Models of Drosophila Olfaction. In International Conference on Neuromorphic Systems 2020. 1\u20135."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIU.2012.6204544"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3067593"},{"volume-title":"high-accuracy spiking deep networks through weight and threshold balancing. In 2015 International joint conference on neural networks (IJCNN)","author":"Diehl U","key":"e_1_3_2_1_13_1","unstructured":"Peter\u00a0 U Diehl , Daniel Neil , Jonathan Binas , Matthew Cook , Shih-Chii Liu , and Michael Pfeiffer . 2015. Fast-classifying , high-accuracy spiking deep networks through weight and threshold balancing. In 2015 International joint conference on neural networks (IJCNN) . IEEE , 1\u20138. Peter\u00a0U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer. 2015. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing. In 2015 International joint conference on neural networks (IJCNN). IEEE, 1\u20138."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01357"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2003.816058"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"#cr-split#-e_1_3_2_1_17_1.1","doi-asserted-by":"crossref","unstructured":"A.\u00a0L. Hodgkin and A.\u00a0F. Huxley. 1990. A quantitative description of membrane current and its application to conduction and excitation in nerve.Bltn Mathcal Biology 52(1990) 25-71. https:\/\/doi.org\/10.1007\/BF02459568 10.1007\/BF02459568","DOI":"10.1007\/BF02459568"},{"key":"#cr-split#-e_1_3_2_1_17_1.2","doi-asserted-by":"crossref","unstructured":"A.\u00a0L. Hodgkin and A.\u00a0F. Huxley. 1990. A quantitative description of membrane current and its application to conduction and excitation in nerve.Bltn Mathcal Biology 52(1990) 25-71. https:\/\/doi.org\/10.1007\/BF02459568","DOI":"10.1007\/BF02459568"},{"key":"#cr-split#-e_1_3_2_1_18_1.1","doi-asserted-by":"crossref","unstructured":"Bernd Illing Wulfram Gerstner and Johanni Brea. 2019. Biologically plausible deep learning - But how far can we go with shallow networks?Neural Networks 118(2019) 90-101. https:\/\/doi.org\/10.1016\/j.neunet.2019.06.001 10.1016\/j.neunet.2019.06.001","DOI":"10.1016\/j.neunet.2019.06.001"},{"key":"#cr-split#-e_1_3_2_1_18_1.2","doi-asserted-by":"crossref","unstructured":"Bernd Illing Wulfram Gerstner and Johanni Brea. 2019. Biologically plausible deep learning - But how far can we go with shallow networks?Neural Networks 118(2019) 90-101. https:\/\/doi.org\/10.1016\/j.neunet.2019.06.001","DOI":"10.1016\/j.neunet.2019.06.001"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3354265.3354277"},{"key":"e_1_3_2_1_20_1","unstructured":"C. Koch and I. Segev. 1998. Methods in Neuronal Modeling: from Ions to Networks 2nd Edition. (1998).  C. Koch and I. Segev. 1998. Methods in Neuronal Modeling: from Ions to Networks 2nd Edition. (1998)."},{"key":"e_1_3_2_1_21_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey\u00a0E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems Vol.\u00a025. https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf  Alex Krizhevsky Ilya Sutskever and Geoffrey\u00a0E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems Vol.\u00a025. https:\/\/proceedings.neurips.cc\/paper\/2012\/file\/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf"},{"key":"e_1_3_2_1_22_1","volume-title":"Recherches quantitatives sur l\u2019excitation electrique des nerfs traitee comme une polarization.Journal of Physiol Pathol G\u00e9n\u00e9rale 9","author":"Lapicque Louis","year":"1907","unstructured":"Louis Lapicque . 1907. Recherches quantitatives sur l\u2019excitation electrique des nerfs traitee comme une polarization.Journal of Physiol Pathol G\u00e9n\u00e9rale 9 ( 1907 ), 620\u2013635. Louis Lapicque. 1907. Recherches quantitatives sur l\u2019excitation electrique des nerfs traitee comme une polarization.Journal of Physiol Pathol G\u00e9n\u00e9rale 9 (1907), 620\u2013635."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_24_1","volume-title":"McCulloch and Walter Pitts","author":"S.","year":"1943","unstructured":"Warren\u00a0 S. McCulloch and Walter Pitts . 1943 . A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5, 4 (1943), 115\u2013133. Warren\u00a0S. McCulloch and Walter Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 5, 4 (1943), 115\u2013133."},{"key":"e_1_3_2_1_25_1","unstructured":"Maxim Milakov. 2014. Deep Learning With GPUs. nvidia.co.uk\/docs\/IO\/147844\/Deep-Learning-With-GPUs-MaximMilakov-NVIDIA.pdf  Maxim Milakov. 2014. Deep Learning With GPUs. nvidia.co.uk\/docs\/IO\/147844\/Deep-Learning-With-GPUs-MaximMilakov-NVIDIA.pdf"},{"key":"e_1_3_2_1_26_1","unstructured":"Marvin Minsky and Seymour Papert. 1969. Perceptrons.(1969).  Marvin Minsky and Seymour Papert. 1969. Perceptrons.(1969)."},{"key":"e_1_3_2_1_27_1","volume-title":"Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU. Computers 10 (08","author":"Ponomarev Evgeny","year":"2021","unstructured":"Evgeny Ponomarev , Sergey Matveev , Ivan Oseledets , and Valery Glukhov . 2021. Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU. Computers 10 (08 2021 ), 104. https:\/\/doi.org\/10.3390\/computers10080104 10.3390\/computers10080104 Evgeny Ponomarev, Sergey Matveev, Ivan Oseledets, and Valery Glukhov. 2021. Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU. Computers 10 (08 2021), 104. https:\/\/doi.org\/10.3390\/computers10080104"},{"key":"e_1_3_2_1_28_1","volume-title":"The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review 65, 6","author":"Rosenblatt Frank","year":"1958","unstructured":"Frank Rosenblatt . 1958. The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review 65, 6 ( 1958 ), 386. Frank Rosenblatt. 1958. The perceptron: a probabilistic model for information storage and organization in the brain.Psychological review 65, 6 (1958), 386."},{"key":"e_1_3_2_1_29_1","volume-title":"Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 7784","author":"Roy Kaushik","year":"2019","unstructured":"Kaushik Roy , Akhilesh Jaiswal , and Priyadarshini Panda . 2019. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 7784 ( 2019 ), 607\u2013617. Kaushik Roy, Akhilesh Jaiswal, and Priyadarshini Panda. 2019. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 7784 (2019), 607\u2013617."},{"key":"e_1_3_2_1_30_1","volume-title":"Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Frontiers in Neuroscience 11","author":"Rueckauer Bodo","year":"2017","unstructured":"Bodo Rueckauer , Iulia-Alexandra Lungu , Yuhuang Hu , Michael Pfeiffer , and Shih-Chii Liu . 2017. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Frontiers in Neuroscience 11 ( 2017 ). https:\/\/doi.org\/10.3389\/fnins.2017.00682 10.3389\/fnins.2017.00682 Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu. 2017. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification. Frontiers in Neuroscience 11 (2017). https:\/\/doi.org\/10.3389\/fnins.2017.00682"},{"key":"#cr-split#-e_1_3_2_1_31_1.1","unstructured":"Catherine\u00a0D. Schuman Thomas\u00a0E. Potok Robert\u00a0M. Patton J.\u00a0Douglas Birdwell Mark\u00a0E. Dean Garrett\u00a0S. Rose and James\u00a0S. Plank. 2017. A Survey of Neuromorphic Computing and Neural Networks in Hardware. https:\/\/doi.org\/10.48550\/ARXIV.1705.06963 10.48550\/ARXIV.1705.06963"},{"key":"#cr-split#-e_1_3_2_1_31_1.2","unstructured":"Catherine\u00a0D. Schuman Thomas\u00a0E. Potok Robert\u00a0M. Patton J.\u00a0Douglas Birdwell Mark\u00a0E. Dean Garrett\u00a0S. Rose and James\u00a0S. Plank. 2017. A Survey of Neuromorphic Computing and Neural Networks in Hardware. https:\/\/doi.org\/10.48550\/ARXIV.1705.06963"},{"key":"e_1_3_2_1_32_1","volume-title":"\u201cdark matter","author":"Shoham Shy","year":"2006","unstructured":"Shy Shoham , Daniel\u00a0 H O\u2019Connor , and Ronen Segev . 2006. How silent is the brain: is there a \u201cdark matter \u201d problem in neuroscience?Journal of Comparative Physiology A 192, 8 ( 2006 ), 777\u2013784. Shy Shoham, Daniel\u00a0H O\u2019Connor, and Ronen Segev. 2006. How silent is the brain: is there a \u201cdark matter\u201d problem in neuroscience?Journal of Comparative Physiology A 192, 8 (2006), 777\u2013784."},{"key":"#cr-split#-e_1_3_2_1_33_1.1","doi-asserted-by":"crossref","unstructured":"David Silver Aja Huang Chris\u00a0J. Maddison Arthur Guez Laurent Sifre George van\u00a0den Driessche Julian Schrittwieser Ioannis Antonoglou Veda Panneershelvam Marc Lanctot Sander Dieleman Dominik Grewe John Nham Nal Kalchbrenner Ilya Sutskever Timothy Lillicrap Madeleine Leach Koray Kavukcuoglu Thore Graepel and Demis Hassabis. 2016. Mastering the game of Go with deep neural networks and tree search.Nature 529(2016) 484-489. https:\/\/doi.org\/10.1038\/nature16961 10.1038\/nature16961","DOI":"10.1038\/nature16961"},{"key":"#cr-split#-e_1_3_2_1_33_1.2","doi-asserted-by":"crossref","unstructured":"David Silver Aja Huang Chris\u00a0J. Maddison Arthur Guez Laurent Sifre George van\u00a0den Driessche Julian Schrittwieser Ioannis Antonoglou Veda Panneershelvam Marc Lanctot Sander Dieleman Dominik Grewe John Nham Nal Kalchbrenner Ilya Sutskever Timothy Lillicrap Madeleine Leach Koray Kavukcuoglu Thore Graepel and Demis Hassabis. 2016. Mastering the game of Go with deep neural networks and tree search.Nature 529(2016) 484-489. https:\/\/doi.org\/10.1038\/nature16961","DOI":"10.1038\/nature16961"},{"key":"e_1_3_2_1_34_1","volume-title":"Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications. Frontiers in Neuroscience 14","author":"Sorbaro Martino","year":"2020","unstructured":"Martino Sorbaro , Qian Liu , Massimo Bortone , and Sadique Sheik . 2020. Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications. Frontiers in Neuroscience 14 ( 2020 ). https:\/\/doi.org\/10.3389\/fnins.2020.00662 10.3389\/fnins.2020.00662 Martino Sorbaro, Qian Liu, Massimo Bortone, and Sadique Sheik. 2020. Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications. Frontiers in Neuroscience 14 (2020). https:\/\/doi.org\/10.3389\/fnins.2020.00662"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i09.7123"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_2_1_38_1","unstructured":"Aaron\u00a0R. Voelker Daniel Rasmussen and Chris Eliasmith. 2020. A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions. CoRR abs\/2002.03553(2020).  Aaron\u00a0R. Voelker Daniel Rasmussen and Chris Eliasmith. 2020. A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions. CoRR abs\/2002.03553(2020)."},{"key":"e_1_3_2_1_39_1","unstructured":"Sally Ward-Foxton. 2021. Intel offers Loihi 2 neuromorphic chip and software framework. embedded.com\/intel-offers-loihi-2-neuromorphic-chip-and-software-framework\/  Sally Ward-Foxton. 2021. Intel offers Loihi 2 neuromorphic chip and software framework. embedded.com\/intel-offers-loihi-2-neuromorphic-chip-and-software-framework\/"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00188"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011319"}],"event":{"name":"ICONS: International Conference on Neuromorphic Systems","acronym":"ICONS","location":"Knoxville TN USA"},"container-title":["Proceedings of the International Conference on Neuromorphic Systems 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546790.3546814","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3546790.3546814","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3546790.3546814","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:40Z","timestamp":1750186840000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3546790.3546814"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,27]]},"references-count":45,"alternative-id":["10.1145\/3546790.3546814","10.1145\/3546790"],"URL":"https:\/\/doi.org\/10.1145\/3546790.3546814","relation":{},"subject":[],"published":{"date-parts":[[2022,7,27]]},"assertion":[{"value":"2022-09-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}