Intrinsic neuronal dynamics predict distinct functional roles during working memory

D F Wasmuht,E Spaak,E K Miller,T J Buschman,M G Stokes

doi:10.1038/s41467-018-05961-4

Abstract

Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF), and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.

Highlights

Working memory (WM) is characterized by the ability to maintain stable representations over time; neural activity associated with WM maintenance can be highly dynamic
Spikes were recorded from the lateral prefrontal cortex, frontal eye fields (FEF, n = 323), and lateral intraparietal cortex (LIP, n = 281) (Fig. 1a, brain schematic), while monkeys performed a delayed change detection WM task (Fig. 1a, left panel)
Important to WM function, mnemonic coding of long-timescale cells was more stable across time in lateral prefrontal cortex (lPFC) and FEF

Summary

Introduction

Working memory (WM) is characterized by the ability to maintain stable representations over time; neural activity associated with WM maintenance can be highly dynamic. A large body of experimental studies emphasize a coding scheme whereby information is maintained through persistent firing of single neurons[3,11,12,13] This view is supported by theoretical models demonstrating that persistent activity can emerge from either intrinsic cell properties[14,15] or reverberations in recurrently connected populations of selective neurons[16,17,18,19,20]. While those studies stress stable population coding despite overall heterogeneous neuronal dynamics, critically, both regimes interact during categorizations performed by monkeys during WM tasks, relying on both persistent and dynamic neurons[34] Both transient and persistent activity in single neurons seems to be important for WM. The source and function of the neuronal tuning and temporal variability underlying WM population dynamics remain poorly understood

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