  Learning internal representations from data using no or few labels is useful
for machine learning research, as it allows using massive amounts of unlabeled
data. In this work, we use the Bayesian Confidence Propagation Neural Network
(BCPNN) model developed as a biologically plausible model of the cortex. Recent
work has demonstrated that these networks can learn useful internal
representations from data using local Bayesian-Hebbian learning rules. In this
work, we show how such representations can be leveraged in a semi-supervised
setting by introducing and comparing different classifiers. We also evaluate
and compare such networks with other popular semi-supervised classifiers.
