  Working memory (WM) is limited in its temporal length and capacity. Classic
conceptions of WM capacity assume the system possesses a finite number of
slots, but recent evidence suggests WM may be a continuous resource. Resource
models typically assume there is no hard upper bound on the number of items
that can be stored, but WM fidelity decreases with the number of items. We
analyze a neural field model of multi-item WM that associates each item with
the location of a bump in a finite spatial domain, considering items that span
a one-dimensional continuous feature space. Our analysis relates the neural
architecture of the network to accumulated errors and capacity limitations
arising during the delay period of a multi-item WM task. Networks with stronger
synapses support wider bumps that interact more, whereas networks with weaker
synapses support narrower bumps that are more susceptible to noise
perturbations. There is an optimal synaptic strength that both limits bump
interaction events and the effects of noise perturbations. This optimum shifts
to weaker synapses as the number of items stored in the network is increased.
Our model not only provides a neural circuit explanation for WM capacity, but
also speaks to how capacity relates to the arrangement of stored items in a
feature space.
