  Spiking Neural Networks (SNNs) have shown great potential in solving deep
learning problems in an energy-efficient manner. However, they are still
limited to simple classification tasks. In this paper, we propose Spiking-GAN,
the first spike-based Generative Adversarial Network (GAN). It employs a kind
of temporal coding scheme called time-to-first-spike coding. We train it using
approximate backpropagation in the temporal domain. We use simple
integrate-and-fire (IF) neurons with very high refractory period for our
network which ensures a maximum of one spike per neuron. This makes the model
much sparser than a spike rate-based system. Our modified temporal loss
function called 'Aggressive TTFS' improves the inference time of the network by
over 33% and reduces the number of spikes in the network by more than 11%
compared to previous works. Our experiments show that on training the network
on the MNIST dataset using this approach, we can generate high quality samples.
Thereby demonstrating the potential of this framework for solving such problems
in the spiking domain.
