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.
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