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• W2604594744 abstract "Real-time learning needs algorithms operating in a fast speed comparable to human or animal, however this is a huge challenge in processing visual inputs. Research shows a biological brain can process complicated real-life recognition scenarios at milliseconds scale. Inspired by biological system, in this paper, we proposed a novel real-time learning method by combing the spike timing-based feed-forward spiking neural network (SNN) and the fast unsupervised spike timing dependent plasticity learning method with dynamic post-synaptic thresholds. Fast cross-validated experiments using MNIST database showed the high efficiency of the proposed method at an acceptable accuracy." @default.
• W2604594744 created "2017-04-14" @default.
• W2604594744 creator A5035562985 @default.
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• W2604594744 date "2017-08-01" @default.
• W2604594744 modified "2023-09-24" @default.
• W2604594744 title "Fast unsupervised learning for visual pattern recognition using spike timing dependent plasticity" @default.
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• W2604594744 doi "https://doi.org/10.1016/j.neucom.2017.04.003" @default.
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