  In this paper we study gradient estimation for a network of nonlinear
stochastic units known as the Little model. Many machine learning systems can
be described as networks of homogeneous units, and the Little model is of a
particularly general form, which includes as special cases several popular
machine learning architectures. However, since a closed form solution for the
stationary distribution is not known, gradient methods which work for similar
models such as the Boltzmann machine or sigmoid belief network cannot be used.
To address this we introduce a method to calculate derivatives for this system
based on measure-valued differentiation and simultaneous perturbation. This
extends previous works in which gradient estimation algorithms were presented
for networks with restrictive features like symmetry or acyclic connectivity.
