{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:32:42Z","timestamp":1763202762660,"version":"3.28.0"},"reference-count":56,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4]]},"DOI":"10.23919\/date56975.2023.10136914","type":"proceedings-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T15:32:57Z","timestamp":1685719977000},"page":"1-6","source":"Crossref","is-referenced-by-count":2,"title":["Brain-Inspired Spatiotemporal Processing Algorithms for Efficient Event-Based Perception"],"prefix":"10.23919","author":[{"given":"Biswadeep","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology,Electrical and Computer Engineering,Atlanta,Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uday","family":"Kamal","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,Electrical and Computer Engineering,Atlanta,Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueyuan","family":"She","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,Electrical and Computer Engineering,Atlanta,Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saurabh","family":"Dash","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,Electrical and Computer Engineering,Atlanta,Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saibal","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology,Electrical and Computer Engineering,Atlanta,Georgia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2769655"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MM.2018.112130359","article-title":"Loihi: A Neuromorphic Manycore Processor with On-Chip Learning","volume":"38","author":"davies","year":"2018","journal-title":"IEEE Micro"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01205"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.695357"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3122092"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2019.00209"},{"key":"ref52","first-page":"23","article-title":"Matrix-lstm: a differentiable recurrent surface for asynchronous event-based data","author":"cannici","year":"0"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2015.2474396"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00097"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2941506"},{"key":"ref54","first-page":"na","article-title":"Event-based Asynchronous Sparse Convo-lutional Networks","volume":"na","author":"messikommer","year":"2020","journal-title":"arXiv Computer Vision and Pattern Recognition"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00275"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2019.8714846"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/366\/1\/012042"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-008-0201-1"},{"key":"ref51","first-page":"5632","article-title":"ICCV - End-to-End Learning of Representations for Asynchronous Event-Based Data","volume":"na","author":"gehrig","year":"0"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2919301"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2392947"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.13665.1"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2574707"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/34.41384"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00089"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.04.079"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2012.04.001"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.781"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00186"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2018.00035"},{"article-title":"Associative memory augmented asynchronous spatiotemporal representation learning for event-based perception","year":"0","author":"kamal","key":"ref7"},{"key":"ref9","first-page":"635","article-title":"IJCNN - A self-driving robot using deep convolutional neural networks on neuromorphic hardware","volume":"na","author":"hwu","year":"0"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2020.615756","article-title":"A heterogeneous spiking neural network for unsu-pervised learning of spatiotemporal patterns","volume":"14","author":"she","year":"2021","journal-title":"Frontiers in Neuroscience"},{"key":"ref3","first-page":"5633","article-title":"End-to-end learning of representations for asyn-chronous event-based data","author":"gehrig","year":"0"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3098013"},{"journal-title":"Heterogeneous recurrent spiking neural network for spatio-temporal classification","year":"2022","author":"chakraborty","key":"ref5"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2011.2171044"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00199"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3023597"},{"key":"ref37","first-page":"1743","article-title":"Event-based action recognition using motion infor-mation and spiking neural networks","volume":"8","author":"liu","year":"0"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"article-title":"Sequence approximation using feedforward spiking neural network for spatiotemporal learning: Theory and optimization methods","year":"0","author":"she","key":"ref31"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-26022-3"},{"article-title":"Heterogeneous neuronal and synaptic dynamics for spike-efficient unsupervised learning: Theory and design principles","year":"0","author":"chakraborty","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1008673"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3048875"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.aaz9712"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00424"},{"key":"ref38","article-title":"Spike-frequency adaptation provides a long short-term memory to networks of spiking neurons","volume":"5","author":"salaj","year":"2020","journal-title":"BioRxiv"},{"key":"ref24","article-title":"Recurrent spiking neural network with dynamic presynaptic currents based on backpropagation","author":"wang","year":"2021","journal-title":"International Journal of Intelligent Systems"},{"key":"ref23","article-title":"Spike-train level backpropagation for training deep recurrent spiking neural networks","volume":"32","author":"zhang","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1142\/9781848162778_0008"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2010.00023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2936604"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00079"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9206681"},{"key":"ref28","article-title":"Increasing liquid state machine performance with edge-of-chaos dynamics organized by astrocyte-modulated plasticity","volume":"34","author":"ivanov","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.06.019"},{"article-title":"Sequence approximation using feedforward spiking neural network for spatiotemporal learning: Theory and optimization methods","year":"0","author":"she","key":"ref29"}],"event":{"name":"2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)","start":{"date-parts":[[2023,4,17]]},"location":"Antwerp, Belgium","end":{"date-parts":[[2023,4,19]]}},"container-title":["2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10136870\/10136706\/10136914.pdf?arnumber=10136914","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T13:53:19Z","timestamp":1687787599000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10136914\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4]]},"references-count":56,"URL":"https:\/\/doi.org\/10.23919\/date56975.2023.10136914","relation":{},"subject":[],"published":{"date-parts":[[2023,4]]}}}