Marked point process representation of oscillatory dynamics underlying working memory

Abstract

Objective. Computational models of neural activity at the meso-scale suggest the involvement of discrete oscillatory bursts as constructs of cognitive processing during behavioral tasks. Classical signal processing techniques that attempt to infer neural correlates of behavior from meso-scale activity employ spectral representations of the signal, exploiting power spectral density techniques and time–frequency (T–F) energy distributions to capture band power features. However, such analyses demand more specialized methods that incorporate explicitly the concepts of neurophysiological signal generation and time resolution in the tens of milliseconds. This paper focuses on working memory (WM), a complex cognitive process involved in encoding, storing and retrieving sensory information, which has been shown to be characterized by oscillatory bursts in the beta and gamma band. Employing a generative model for oscillatory dynamics, we present a marked point process (MPP) representation of bursts during memory creation and readout. We show that the markers of the point process quantify specific neural correlates of WM. Approach. We demonstrate our results on field potentials recorded from the prelimbic and secondary motor cortices of three rats while performing a WM task. The generative model for single channel, band-passed traces of field potentials characterizes with high-resolution, the timings and amplitudes of transient neuromodulations in the high gamma (80–150 Hz, γ) and beta (10–30 Hz, β) bands as an MPP. We use standard hypothesis testing methods on the MPP features to check for significance in encoding of task variables, sensory stimulus and executive control while comparing encoding capabilities of our model with other T–F methods. Main Results. Firstly, the advantages of an MPP approach in deciphering encoding mechanisms at the meso-scale is demonstrated. Secondly, the nature of state encoding by neuromodulatory events is determined. Third, we demonstrate the necessity of a higher time resolution alternative to conventionally employed T–F methods. Finally, our results underscore the novelty in interpreting oscillatory dynamics encompassed by the marked features of the point process. Significance. An MPP representation of meso-scale activity not just enables a rich, high-resolution parameter space for analysis but also presents a novel tool for diverse neural applications.