  A model of the columnar functional organization of neocortical association
areas is studied. The neuronal network is composed of many Hebbian
autoassociators, or modules, each of which interacts with a relatively small
number of the others. Every module encodes and stores a number of elementary
percepts, or features. Memory items, or patterns, are peculiar combinations of
features sparsely distributed over the multi-modular network. Any feature
stored in any module can be involved in several of the stored patterns;
feature-sharing is in fact source of local ambiguities and, consequently, a
potential cause of erroneous memory retrieval activity spreading through the
model network.
  The memory retrieval dynamics of the large multi-modular autoassociator is
investigated by means of quantitative analysis and numerical simulations. An
oscillatory retrieval process is found to be very efficient in overcoming
feature-sharing drawbacks; it requires a mechanism that modulates the
robustness of local attractors to noise, and neuronal activity sparseness such
that quiescent and active modules are about equally noisy. Correlated
activation of interconnected modules and extramodular neuronal contacts more
effective than the intramodular ones seem to be general requirements in order
to efficiently achieve satisfactory quality of memory retrieval. It is also
shown that, even in ideal conditions, some spots of the network cannot be
reached by retrieval activity spread. The locations of these activity isles
depend on the pattern to retrieve and on the cue, while their extension only
depends on architecture of the graph and statistics of the stored patterns. The
existence of these isles determines an upper-bound to retrieval quality that
does not depend on the specific retrieval dynamics adopted, nor on whether
feature-sharing is permitted. The oscillatory retrieval process nearly
saturates this bound.
