A Flexible Model of Working Memory

Abstract

Working memory is fundamental to cognition, allowing one to hold information in mind and use it to guide behavior. A defining characteristic of working memory is its flexibility: we can hold anything in mind. However, typical models of working memory rely on finely tuned, content-specific, attractors to persistently maintain neural activity and therefore do not allow for the flexibility observed in behavior. Here we present a flexible model of working memory that maintains representations through random recurrent connections between two layers of neurons: a structured sensory layer and a randomly connected, unstructured, layer. As the interactions are untuned with respect to the content being stored, the network is able to maintain any arbitrary input. However, this flexibility comes at a cost: the random connections overlap, leading to interference between representations and limiting the memory capacity of the network. Additionally, our model captures several other key behavioral and neurophysiological characteristics of working memory.