Uninformative memories will prevail: the storage of correlated representations and its consequences

TL;DR

This paper demonstrates that a small modification to autoassociative network models allows them to store correlated memories, revealing that more informative memories are also more vulnerable to damage, with implications for understanding semantic memory.

Contribution

Introduces a biologically plausible learning rule that enables autoassociative networks to store correlated patterns, addressing a key limitation of previous models.

Findings

Modified learning rule improves storage of correlated patterns.

More informative memories are more sensitive to damage.

Results relate to category-specific effects in semantic memory patients.

Abstract

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.