  This paper proposes new nonnegative (shallow and multi-layer) autoencoders by
combining the spiking Random Neural Network (RNN) model, the network
architecture typical used in deep-learning area and the training technique
inspired from nonnegative matrix factorization (NMF). The shallow autoencoder
is a simplified RNN model, which is then stacked into a multi-layer
architecture. The learning algorithm is based on the weight update rules in
NMF, subject to the nonnegative probability constraints of the RNN. The
autoencoders equipped with this learning algorithm are tested on typical image
datasets including the MNIST, Yale face and CIFAR-10 datasets, and also using
16 real-world datasets from different areas. The results obtained through these
tests yield the desired high learning and recognition accuracy. Also, numerical
simulations of the stochastic spiking behavior of this RNN auto encoder, show
that it can be implemented in a highly-distributed manner.
