Slot-like capacity and resource-like coding in a neural model of multiple-item working memory

Abstract

For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of slots, each with a fixed resolution. On the other hand, any number of items may be stored, but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data, nor speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioural data from human and non-human primate studies, and they demonstrate that feedback inhibition not only lowers capacity, but also lowers coding fidelity by all three measures. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. As such, the model provides a mechanistic explanation for experimental data showing that working memory precision decreases with increasing memory load before levelling off at capacity. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks.