  Although deep learning has solved difficult problems in visual pattern
recognition, it is mostly successful in tasks where there are lots of labeled
training data available. Furthermore, the global back-propagation based
training rule and the amount of employed layers represents a departure from
biological inspiration. The brain is able to perform most of these tasks in a
very general way from limited to no labeled data. For these reasons it is still
a key research question to look into computational principles in the brain that
can help guide models to unsupervisedly learn good representations which can
then be used to perform tasks like classification. In this work we explore some
of these principles to generate such representations for the MNIST data set. We
compare the obtained results with similar recent works and verify extremely
competitive results.
