  We complement our previous work [arxiv: 0707.0565] with the full (non
diluted) solution describing the stable states of an attractor network that
stores correlated patterns of activity. The new solution provides a good fit of
simulations of a network storing the feature norms of McRae and colleagues
[McRae et al, 2005], experimentally obtained combinations of features
representing concepts in semantic memory. We discuss three ways to improve the
storage capacity of the network: adding uninformative neurons, removing
informative neurons and introducing popularity-modulated hebbian learning. We
show that if the strength of synapses is modulated by an exponential decay of
the popularity of the pre-synaptic neuron, any distribution of patterns can be
stored and retrieved with approximately an optimal storage capacity - i.e, C ~
I.p, the minimum number of connections per neuron needed to sustain the
retrieval of a pattern is proportional to the information content of the
pattern multiplied by the number of patterns stored in the network.
