  Spiking neural networks (SNNs) offer a promising alternative to current
artificial neural networks to enable low-power event-driven neuromorphic
hardware. Spike-based neuromorphic applications require processing and
extracting meaningful information from spatio-temporal data, represented as
series of spike trains over time. In this paper, we propose a method to
synthesize images from multiple modalities in a spike-based environment. We use
spiking auto-encoders to convert image and audio inputs into compact
spatio-temporal representations that is then decoded for image synthesis. For
this, we use a direct training algorithm that computes loss on the membrane
potential of the output layer and back-propagates it by using a sigmoid
approximation of the neuron's activation function to enable differentiability.
The spiking autoencoders are benchmarked on MNIST and Fashion-MNIST and achieve
very low reconstruction loss, comparable to ANNs. Then, spiking autoencoders
are trained to learn meaningful spatio-temporal representations of the data,
across the two modalities - audio and visual. We synthesize images from audio
in a spike-based environment by first generating, and then utilizing such
shared multi-modal spatio-temporal representations. Our audio to image
synthesis model is tested on the task of converting TI-46 digits audio samples
to MNIST images. We are able to synthesize images with high fidelity and the
model achieves competitive performance against ANNs.
