  Associative memory, a form of content-addressable memory, facilitates
information storage and retrieval in many biological and physical systems. In
statistical mechanics models, associative memory at equilibrium is represented
through attractor basins in the free energy landscape. Here, we use the
Hopfield model, a paradigmatic model to describe associate memory, to
investigate the effect of non-equilibrium activity on memory retention and
recall. We introduce activity into the system as gaussian-colored noise which
breaks detailed balance and forces the system out of equilibrium. We observe
that, under these non-equilibrium conditions, the Hopfield network has a higher
storage capacity than that allowed at equilibrium. Using analytical and
numerical techniques, we show that the rate of entropy production modifies the
energy landscape and helps the system to access memory regions which were
previously inaccessible.
