# Synaptic mechanisms of interference in working memory

## TL;DR

This paper presents a probabilistic inference model and a recurrent network mechanism explaining how interference from previous trials biases working memory responses, with implications for understanding neural dynamics and memory retention.

## Contribution

It introduces a novel probabilistic model linked to a recurrent network with short-term facilitation, explaining trial-to-trial bias dynamics in working memory tasks.

## Key findings

- Model accurately predicts bias in response locations.
- Dynamic connectivity improves memory retention for correlated targets.
- Low-dimensional description captures trial-to-trial bias dynamics.

## Abstract

Information from preceding trials of cognitive tasks can bias performance in the current trial, a phenomenon referred to as interference. Subjects performing visual working memory tasks exhibit interference in their trial-to-trial response correlations: the recalled target location in the current trial is biased in the direction of the target presented on the previous trial. We present modeling work that (a) develops a probabilistic inference model of this history-dependent bias, and (b) links our probabilistic model to computations of a recurrent network wherein short-term facilitation accounts for the dynamics of the observed bias. Network connectivity is reshaped dynamically during each trial, providing a mechanism for generating predictions from prior trial observations. Applying timescale separation methods, we can obtain a low-dimensional description of the trial-to-trial bias based on the history of target locations. The model has response statistics whose mean is centered at the true target location across many trials, typical of such visual working memory tasks. Furthermore, we demonstrate task protocols for which the plastic model performs better than a model with static connectivity: repetitively presented targets are better retained in working memory than targets drawn from uncorrelated sequences.