Synaptic efficacy shapes resource limitations in working memory

TL;DR

This paper presents a neural field model of working memory that links synaptic strength to capacity limitations and error accumulation, providing insights into how neural architecture influences memory fidelity and capacity.

Contribution

It introduces a neural model connecting synaptic efficacy to working memory capacity and error dynamics, highlighting an optimal synaptic strength for different memory loads.

Findings

Stronger synapses support wider bumps with more interaction.

Weaker synapses lead to narrower bumps more susceptible to noise.

An optimal synaptic strength balances bump interaction and noise effects.

Abstract

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.