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- W2175459086 abstract "In spiking neural networks (SNNs), unlike traditional artificial neural networks, signals are propagated via a 'pulse code' instead of a 'rate code'. This results in incorporating the time dimension into the network and thus theoretically ensures a higher computational power. The different principle of operation makes learning in SNNs complicated. In this paper, two ideas have been proposed to assure spiking activity propagation --random spikes and parallel input layers. The proposed ideas have been illustrated with experimental results." @default.
- W2175459086 created "2016-06-24" @default.
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- W2175459086 date "2010-01-01" @default.
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- W2175459086 title "Random Spikes to Enhance Learning in Spiking Neural Networks." @default.
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