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• W2912797248 endingPage "36" @default.
• W2912797248 startingPage "6" @default.
• W2912797248 abstract "The random neural network (RNN) is a mathematical model for an “integrate and fire” spiking network that closely resembles the stochastic behavior of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods." @default.
• W2912797248 created "2019-02-21" @default.
• W2912797248 creator A5035643040 @default.
• W2912797248 date "2019-01-30" @default.
• W2912797248 modified "2023-09-26" @default.
• W2912797248 title "RANDOM NEURAL NETWORK METHODS AND DEEP LEARNING" @default.
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