Matches in SemOpenAlex for { <https://semopenalex.org/work/W4297094906> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4297094906 endingPage "102668" @default.
- W4297094906 startingPage "102659" @default.
- W4297094906 abstract "Convolution helps spiking neural networks (SNNs) capture the spatio-temporal structures of neuromorphic (event) data as evident in the convolution-based SNNs (C-SNNs) with the state-of-the-art classification-accuracies on various datasets. However, the efficacy aside, the efficiency of C-SNN is questionable. In this regard, we propose SNNs with novel trainable dynamic time-surfaces (DTS-SNNs) as efficient alternatives to convolution. The novel dynamic time-surface proposed in this work features its high responsiveness to moving objects given the use of the zero-sum temporal kernel that is motivated by the simple cells’ receptive fields in the early stage visual pathway. We evaluated the performance and computational complexity of our DTS-SNNs on three real-world event-based datasets (DVS128 Gesture, Spiking Heidelberg dataset, N-Cars). The results highlight high classification accuracies and significant improvements in computational efficiency, e.g., merely 1.51% behind of the state-of-the-art result on DVS128 Gesture but a × 18 improvement in efficiency." @default.
- W4297094906 created "2022-09-27" @default.
- W4297094906 creator A5025440827 @default.
- W4297094906 creator A5082971950 @default.
- W4297094906 date "2022-01-01" @default.
- W4297094906 modified "2023-10-15" @default.
- W4297094906 title "DTS-SNN: Spiking Neural Networks With Dynamic Time-Surfaces" @default.
- W4297094906 cites W2138913040 @default.
- W4297094906 cites W2469278928 @default.
- W4297094906 cites W2621826044 @default.
- W4297094906 cites W2745933219 @default.
- W4297094906 cites W2749476078 @default.
- W4297094906 cites W2783525259 @default.
- W4297094906 cites W2894176442 @default.
- W4297094906 cites W2898323475 @default.
- W4297094906 cites W2907149254 @default.
- W4297094906 cites W2963510238 @default.
- W4297094906 cites W2966174534 @default.
- W4297094906 cites W2969407469 @default.
- W4297094906 cites W2980513133 @default.
- W4297094906 cites W3022766987 @default.
- W4297094906 cites W3025773901 @default.
- W4297094906 cites W3065747603 @default.
- W4297094906 cites W3093471076 @default.
- W4297094906 cites W3102040318 @default.
- W4297094906 cites W3102750118 @default.
- W4297094906 cites W3124478039 @default.
- W4297094906 cites W3198957722 @default.
- W4297094906 cites W3203906445 @default.
- W4297094906 cites W3206817059 @default.
- W4297094906 doi "https://doi.org/10.1109/access.2022.3209671" @default.
- W4297094906 hasPublicationYear "2022" @default.
- W4297094906 type Work @default.
- W4297094906 citedByCount "0" @default.
- W4297094906 crossrefType "journal-article" @default.
- W4297094906 hasAuthorship W4297094906A5025440827 @default.
- W4297094906 hasAuthorship W4297094906A5082971950 @default.
- W4297094906 hasBestOaLocation W42970949061 @default.
- W4297094906 hasConcept C114614502 @default.
- W4297094906 hasConcept C11731999 @default.
- W4297094906 hasConcept C121332964 @default.
- W4297094906 hasConcept C151927369 @default.
- W4297094906 hasConcept C153180895 @default.
- W4297094906 hasConcept C154945302 @default.
- W4297094906 hasConcept C2779662365 @default.
- W4297094906 hasConcept C33923547 @default.
- W4297094906 hasConcept C41008148 @default.
- W4297094906 hasConcept C45347329 @default.
- W4297094906 hasConcept C50644808 @default.
- W4297094906 hasConcept C62520636 @default.
- W4297094906 hasConcept C74193536 @default.
- W4297094906 hasConcept C81363708 @default.
- W4297094906 hasConceptScore W4297094906C114614502 @default.
- W4297094906 hasConceptScore W4297094906C11731999 @default.
- W4297094906 hasConceptScore W4297094906C121332964 @default.
- W4297094906 hasConceptScore W4297094906C151927369 @default.
- W4297094906 hasConceptScore W4297094906C153180895 @default.
- W4297094906 hasConceptScore W4297094906C154945302 @default.
- W4297094906 hasConceptScore W4297094906C2779662365 @default.
- W4297094906 hasConceptScore W4297094906C33923547 @default.
- W4297094906 hasConceptScore W4297094906C41008148 @default.
- W4297094906 hasConceptScore W4297094906C45347329 @default.
- W4297094906 hasConceptScore W4297094906C50644808 @default.
- W4297094906 hasConceptScore W4297094906C62520636 @default.
- W4297094906 hasConceptScore W4297094906C74193536 @default.
- W4297094906 hasConceptScore W4297094906C81363708 @default.
- W4297094906 hasFunder F4320322120 @default.
- W4297094906 hasLocation W42970949061 @default.
- W4297094906 hasOpenAccess W4297094906 @default.
- W4297094906 hasPrimaryLocation W42970949061 @default.
- W4297094906 hasRelatedWork W2883226409 @default.
- W4297094906 hasRelatedWork W2883831350 @default.
- W4297094906 hasRelatedWork W2896666858 @default.
- W4297094906 hasRelatedWork W2949189996 @default.
- W4297094906 hasRelatedWork W3011755776 @default.
- W4297094906 hasRelatedWork W3093612317 @default.
- W4297094906 hasRelatedWork W3159557112 @default.
- W4297094906 hasRelatedWork W3217644425 @default.
- W4297094906 hasRelatedWork W4294786480 @default.
- W4297094906 hasRelatedWork W4304998656 @default.
- W4297094906 hasVolume "10" @default.
- W4297094906 isParatext "false" @default.
- W4297094906 isRetracted "false" @default.
- W4297094906 workType "article" @default.