Oscillations in working memory and neural binding: a mechanism for multiple memories and their interactions
AbstractNeural oscillations have been implicated in many different basic brain and cognitive processes. Oscillatory activity has been suggested to play a role in neural binding, and more recently in the maintenance of information in working memory. This latter work has focused primarily on oscillations in terms of providing a “code” in working memory. However, oscillations may additionally play a fundamental role in essential properties and behaviors that neuronal networks must exhibit in order to produce functional working memory. In the present work, we present a biologically plausible working memory model and demonstrate that specific types of stable oscillatory dynamics may play a critical role in facilitating properties of working memory, including transitions between different memory states and a multi-item working memory capacity. We also show these oscillatory dynamics may facilitate and provide an underlying mechanism to enable a range of different types of binding in the context of working memory.Author summaryWorking memory is a form of short-term memory that is limited in capacity to perhaps 3 – 5 items. Various studies have shown that ensembles of neurons oscillate during working memory retention, and cross-frequency coupling (between, e.g., theta and gamma frequencies) has been conjectured as underlying the observed limited capacity. Binding occurs when different objects or concepts are associated with each other and can persist as working memory representations; neuronal synchrony has been hypothesized as the neural correlate. We propose a novel computational model of a network of oscillatory neuronal populations that capture salient attributes of working memory and binding by allowing for both stable synchronous and asynchronous activity. The oscillatory dynamics we describe may provide a mechanism that can facilitate aspects of working memory, such as maintaining multiple items active at once, creating rich neural representations of memories via binding, and rapidly transitioning activtation patterns based on selective inputs.