  The model network is composed of many modules. Each module represents an
assembly of recurrently connected neurons of a neocortical functional unit and
operates as a Hebbian autoassociator, storing a number of local configurations
of neuronal activity, or features, which it can recall upon cue. The global
memory patterns are made of specific combinations of features sparsely
distributed across the modules. Intermodular connections are modelled as a
finite-connectivity random graph. Any pair of features in any respective pair
of connected modules is allowed to be involved in several memory patterns; the
network dynamics is defined in such a way as to overcome the consequent
ambiguity of associations. The effects of a hypothetical mechanism of
long-range homeostatic synaptic scaling are also investigated.
  The dynamical process of cued retrieval almost saturates a natural upper
bound while producing negligible spurious activation. The extent of finite-size
effects on storage capacity is quantitatively evaluated. In the limit of
infinite size, the functional relationship between storage capacity and number
of features per module reduces to that which other authors found by methods
from equilibrium statistical mechanics, which suggests that the origin of the
functional form is fundamentally of combinatorial nature. In contrast with its
apparent inevitability at intramodular level, long-range synaptic scaling
results to be of minor relevance to both retrieval and storage capacity. A
conjecture is posited about how statistical fluctuation of coordination number
across the network may underpin spontaneous emergence of semantic hierarchies.
