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Neuromorphic Learning: Deep Spiking Neural Network

Recently, deep learning has revolutionized the field of machine learning, for computer vision in particular. Nevertheless, biological neurons use discrete spikes to compute and transmit information, and the spike times, in addition to the spike rates, matter. Spiking neural networks (SNNs) are, thus, biologically realistic and are arguably the only viable option if one wants to understand how the brain computes at the neuronal description level. Combined with bio-plausible local learning rules, this makes building low-power, neuromorphic hardware for SNNs easier. However, training deep SNNs remains a challenge. Here, we present supervised and unsupervised methods to train deep SNNs and compare them in terms of architecture and performance.