  This paper shows how a folded Markov chain network can be applied to the
problem of processing data from multiple sensors, with an emphasis on the
special case of 2 sensors. It is necessary to design the network so that it can
transform a high dimensional input vector into a posterior probability, for
which purpose the partitioned mixture distribution network is ideally suited.
The underlying theory is presented in detail, and a simple numerical simulation
is given that shows the emergence of ocular dominance stripes.
