  Unsupervised learning of hierarchical representations has been one of the
most vibrant research directions in deep learning during recent years. In this
work we study biologically inspired unsupervised strategies in neural networks
based on local Hebbian learning. We propose new mechanisms to extend the
Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and
demonstrate their capability for unsupervised learning of salient hidden
representations when tested on the MNIST dataset.
