Oscillations in working memory and neural binding: a mechanism for multiple memories and their interactions

Neural 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.

Intermuscular coherence reflects functional coordination

Coherence analysis has the ability to identify the presence of common descending drive shared by motor unit pools and reveals its spectral properties. However, the link between spectral properties of shared neural drive and functional interactions among muscles remains unclear. We assessed shared neural drive between muscles of the thumb and index finger while participants executed two mechanically distinct precision pinch tasks, each requiring distinct functional coordination among muscles. We found that shared neural drive was systematically reduced or enhanced at specific frequencies of interest (~10 and ~40 Hz). While amplitude correlations between surface EMG signals also exhibited changes across tasks, only their coherence has strong physiological underpinnings indicative of neural binding. Our results support the use of intermuscular coherence as a tool to detect when coactivated muscles are members of a functional group or synergy of neural origin. Furthermore, our results demonstrate the advantages of considering neural binding at 10, ~20, and >30 Hz, as indicators of task-dependent neural coordination strategies. NEW & NOTEWORTHY It is often unclear whether correlated activity among muscles reflects their neural binding or simply reflects the constraints defining the task. Using the fact that high-frequency coherence between EMG signals (>6 Hz) is thought to reflect shared neural drive, we demonstrate that coherence analysis can reveal the neural origin of distinct muscle coordination patterns required by different tasks.