  Biological neurons use spikes to process and learn temporally dynamic inputs
in an energy and computationally efficient way. However, applying the
state-of-the-art gradient-based supervised algorithms to spiking neural
networks (SNN) is a challenge due to the non-differentiability of the
activation function of spiking neurons. Employing surrogate gradients is one of
the main solutions to overcome this challenge. Although SNNs naturally work in
the temporal domain, recent studies have focused on developing SNNs to solve
static image categorization tasks. In this paper, we employ a surrogate
gradient descent learning algorithm to recognize twelve human hand gestures
recorded by dynamic vision sensor (DVS) cameras. The proposed SNN could reach
97.2% recognition accuracy on test data.
