  Restricted Boltzmann Machines and Deep Belief Networks have been successfully
used in probabilistic generative model applications such as image occlusion
removal, pattern completion and motion synthesis. Generative inference in such
algorithms can be performed very efficiently on hardware using a Markov Chain
Monte Carlo procedure called Gibbs sampling, where stochastic samples are drawn
from noisy integrate and fire neurons implemented on neuromorphic substrates.
Currently, no satisfactory metrics exist for evaluating the generative
performance of such algorithms implemented on high-dimensional data for
neuromorphic platforms. This paper demonstrates the application of
nonparametric goodness-of-fit testing to both quantify the generative
performance as well as provide decision-directed criteria for choosing the
parameters of the neuromorphic Gibbs sampler and optimizing usage of hardware
resources used during sampling.
