Abstract: International audience; Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated-using rate-based neural networks trained with back-propagationthat having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a d...
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Topics: 
Artificial intelligence