  One conjecture in both deep learning and classical connectionist viewpoint is
that the biological brain implements certain kinds of deep networks as its
back-end. However, to our knowledge, a detailed correspondence has not yet been
set up, which is important if we want to bridge between neuroscience and
machine learning. Recent researches emphasized the biological plausibility of
Linear-Nonlinear-Poisson (LNP) neuron model. We show that with neurally
plausible settings, the whole network is capable of representing any Boltzmann
machine and performing a semi-stochastic Bayesian inference algorithm lying
between Gibbs sampling and variational inference.
