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• W4312888260 abstract "We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision." @default.
• W4312888260 created "2023-01-05" @default.
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• W4312888260 date "2022-07-18" @default.
• W4312888260 modified "2023-10-01" @default.
• W4312888260 title "Event Camera Data Classification Using Spiking Networks with Spike-Timing-Dependent Plasticity" @default.
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• W4312888260 doi "https://doi.org/10.1109/ijcnn55064.2022.9892362" @default.
• W4312888260 hasPublicationYear "2022" @default.
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