Matches in SemOpenAlex for { <https://semopenalex.org/work/W3014020978> ?p ?o ?g. }
- W3014020978 abstract "Spiking neural networks (SNNs) can be used in low-power and embedded systems e.g. neuromorphic chips due to their event-based nature. They preserve conventional artificial neural networks (ANNs) properties with lower computation and memory costs. The temporal coding in layers of convolutional SNNs has not yet been studied. In this paper, we exploit the spatio-temporal feature extraction property of convolutional SNNs. Based on our analysis, we have shown that the shallow convolutional SNN outperforms spatio-temporal feature extractor methods such as C3D, ConvLstm, and cascaded Conv and LSTM. Furthermore, we present a new deep spiking architecture to tackle real-world classification and activity recognition tasks. This model is trained with our proposed hybrid training method. The proposed architecture achieved superior performance compared to other SNN methods on NMNIST (99.6%), DVS-CIFAR10 (69.2%), and DVS-Gesture (96.7%). Also, it achieves comparable results compared to ANN methods on UCF-101 (42.1%) and HMDB-51 (21.5%) datasets." @default.
- W3014020978 created "2020-04-03" @default.
- W3014020978 creator A5013200082 @default.
- W3014020978 creator A5034499491 @default.
- W3014020978 creator A5049221896 @default.
- W3014020978 creator A5056793787 @default.
- W3014020978 creator A5064555092 @default.
- W3014020978 date "2023-05-04" @default.
- W3014020978 modified "2023-09-27" @default.
- W3014020978 title "Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction" @default.
- W3014020978 cites W1522734439 @default.
- W3014020978 cites W1600744878 @default.
- W3014020978 cites W1645800954 @default.
- W3014020978 cites W1987659653 @default.
- W3014020978 cites W2014673260 @default.
- W3014020978 cites W2020676607 @default.
- W3014020978 cites W2054747732 @default.
- W3014020978 cites W2126579184 @default.
- W3014020978 cites W2155987738 @default.
- W3014020978 cites W2519559998 @default.
- W3014020978 cites W2530906228 @default.
- W3014020978 cites W2565755350 @default.
- W3014020978 cites W2619510810 @default.
- W3014020978 cites W2621826044 @default.
- W3014020978 cites W2733297609 @default.
- W3014020978 cites W2745933219 @default.
- W3014020978 cites W2766013930 @default.
- W3014020978 cites W2775079417 @default.
- W3014020978 cites W2779025322 @default.
- W3014020978 cites W2798878556 @default.
- W3014020978 cites W2808550672 @default.
- W3014020978 cites W2892077605 @default.
- W3014020978 cites W2901937441 @default.
- W3014020978 cites W2905533880 @default.
- W3014020978 cites W2922107638 @default.
- W3014020978 cites W2962804204 @default.
- W3014020978 cites W2962858109 @default.
- W3014020978 cites W2962934715 @default.
- W3014020978 cites W2963206832 @default.
- W3014020978 cites W2963510238 @default.
- W3014020978 cites W2964338223 @default.
- W3014020978 cites W2974328520 @default.
- W3014020978 cites W2976428661 @default.
- W3014020978 cites W2978279179 @default.
- W3014020978 cites W2984844508 @default.
- W3014020978 cites W2997498437 @default.
- W3014020978 cites W3035644810 @default.
- W3014020978 cites W3065747603 @default.
- W3014020978 cites W3202138477 @default.
- W3014020978 cites W4312383539 @default.
- W3014020978 cites W4312527777 @default.
- W3014020978 cites W4313046728 @default.
- W3014020978 doi "https://doi.org/10.1007/s11063-023-11247-8" @default.
- W3014020978 hasPublicationYear "2023" @default.
- W3014020978 type Work @default.
- W3014020978 sameAs 3014020978 @default.
- W3014020978 citedByCount "3" @default.
- W3014020978 countsByYear W30140209782021 @default.
- W3014020978 crossrefType "journal-article" @default.
- W3014020978 hasAuthorship W3014020978A5013200082 @default.
- W3014020978 hasAuthorship W3014020978A5034499491 @default.
- W3014020978 hasAuthorship W3014020978A5049221896 @default.
- W3014020978 hasAuthorship W3014020978A5056793787 @default.
- W3014020978 hasAuthorship W3014020978A5064555092 @default.
- W3014020978 hasBestOaLocation W30140209782 @default.
- W3014020978 hasConcept C11413529 @default.
- W3014020978 hasConcept C11731999 @default.
- W3014020978 hasConcept C138885662 @default.
- W3014020978 hasConcept C151927369 @default.
- W3014020978 hasConcept C153180895 @default.
- W3014020978 hasConcept C154945302 @default.
- W3014020978 hasConcept C2776401178 @default.
- W3014020978 hasConcept C41008148 @default.
- W3014020978 hasConcept C41895202 @default.
- W3014020978 hasConcept C45374587 @default.
- W3014020978 hasConcept C50644808 @default.
- W3014020978 hasConcept C52622490 @default.
- W3014020978 hasConcept C81363708 @default.
- W3014020978 hasConceptScore W3014020978C11413529 @default.
- W3014020978 hasConceptScore W3014020978C11731999 @default.
- W3014020978 hasConceptScore W3014020978C138885662 @default.
- W3014020978 hasConceptScore W3014020978C151927369 @default.
- W3014020978 hasConceptScore W3014020978C153180895 @default.
- W3014020978 hasConceptScore W3014020978C154945302 @default.
- W3014020978 hasConceptScore W3014020978C2776401178 @default.
- W3014020978 hasConceptScore W3014020978C41008148 @default.
- W3014020978 hasConceptScore W3014020978C41895202 @default.
- W3014020978 hasConceptScore W3014020978C45374587 @default.
- W3014020978 hasConceptScore W3014020978C50644808 @default.
- W3014020978 hasConceptScore W3014020978C52622490 @default.
- W3014020978 hasConceptScore W3014020978C81363708 @default.
- W3014020978 hasLocation W30140209781 @default.
- W3014020978 hasLocation W30140209782 @default.
- W3014020978 hasOpenAccess W3014020978 @default.
- W3014020978 hasPrimaryLocation W30140209781 @default.
- W3014020978 hasRelatedWork W2059299633 @default.
- W3014020978 hasRelatedWork W2546942002 @default.
- W3014020978 hasRelatedWork W2732542196 @default.
- W3014020978 hasRelatedWork W2760085659 @default.
- W3014020978 hasRelatedWork W2767651786 @default.