Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics,
such as power laws governing recall capacity. Although abstract
random matrix models could explain such laws, the possibility of
their implementation in large networks of interacting neurons has so
far remained underexplored. We study an attractor network model
of long-term memory endowed with firing rate adaptation and global
inhibition. Under appropriate conditions, the transitioning behaviour
of the network from memory to memory is constrained by limit cycles
that prevent the network from recalling all memories, with scaling
similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the
standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free
recall literature, namely serial position effects, contiguity and forward
asymmetry effects, as well as the semantic effects found to guide
memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables
that affect recall, such as the role of rehearsal, presentation rates
and (continuous/end-of-list) distractor conditions. We predict that
recall capacity may be increased with the addition of small amounts
of noise, for example in the form of weak random stimuli during recall. Finally, we predict that although the statistics of the encoded
memories has a strong effect on the recall capacity, the power laws
governing recall capacity may still be expected to hold.