  Autoassociative networks were proposed in the 80's as simplified models of
memory function in the brain, using recurrent connectivity with hebbian
plasticity to store patterns of neural activity that can be later recalled.
This type of computation has been suggested to take place in the CA3 region of
the hippocampus and at several levels in the cortex. One of the weaknesses of
these models is their apparent inability to store correlated patterns of
activity. We show, however, that a small and biologically plausible
modification in the `learning rule' (associating to each neuron a plasticity
threshold that reflects its popularity) enables the network to handle
correlations. We study the stability properties of the resulting memories (in
terms of their resistance to the damage of neurons or synapses), finding a
novel property of autoassociative networks: not all memories are equally
robust, and the most informative are also the most sensitive to damage. We
relate these results to category-specific effects in semantic memory patients,
where concepts related to `non-living things' are usually more resistant to
brain damage than those related to `living things', a phenomenon suspected to
be rooted in the correlation between representations of concepts in the cortex.
