  One of the most well established brain principles, hebbian learning, has led
to the theoretical concept of neural assemblies. Based on it, many interesting
brain theories have spawned. Palm's work implements this concept through binary
associative memory, in a model that not only has a wide cognitive explanatory
power but also makes neuroscientific predictions. Yet, associative memory can
only work with logarithmic sparse representations, which makes it extremely
difficult to apply the model to real data. We propose a biologically plausible
network that encodes images into codes that are suitable for associative
memory. It is organized into groups of neurons that specialize on local
receptive fields, and learn through a competitive scheme. After conducting
auto- and hetero-association experiments on two visual data sets, we can
conclude that our network not only beats sparse coding baselines, but also that
it comes close to the performance achieved using optimal random codes.
