Abstract:Spiking neural networks (SNNs) are receiving increased attention as a means to develop 'biologically plausible' machine learning models. These networks mimic synaptic connections in the human brain and produce spike trains, which can be approximated by binary values, precluding high computational cost with floating-point arithmetic circuits. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introdu… Show more
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