  A vast majority of the current research in the field of Machine Learning is
done using algorithms with strong arguments pointing to their biological
implausibility such as Backpropagation, deviating the field's focus from
understanding its original organic inspiration to a compulsive search for
optimal performance. Yet, there have been a few proposed models that respect
most of the biological constraints present in the human brain and are valid
candidates for mimicking some of its properties and mechanisms. In this paper,
we will focus on guiding the learning of a biologically plausible generative
model called the Helmholtz Machine in complex search spaces using a heuristic
based on the Human Image Perception mechanism. We hypothesize that this model's
learning algorithm is not fit for Deep Networks due to its Hebbian-like local
update rule, rendering it incapable of taking full advantage of the
compositional properties that multi-layer networks provide. We propose to
overcome this problem, by providing the network's hidden layers with visual
queues at different resolutions using a Multi-level Data representation. The
results on several image datasets showed the model was able to not only obtain
better overall quality but also a wider diversity in the generated images,
corroborating our intuition that using our proposed heuristic allows the model
to take more advantage of the network's depth growth. More importantly, they
show the unexplored possibilities underlying brain-inspired models and
techniques.
