Marked point process representation of oscillatory dynamics underlying working memory

摘要

Objective. Computational models of neural activity at the meso-scale suggest the involvement ofdiscrete oscillatory bursts as constructs of cognitive processing during behavioral tasks. Classicalsignal processing techniques that attempt to infer neural correlates of behavior from meso-scaleactivity employ spectral representations of the signal, exploiting power spectral density techniquesand time–frequency (T–F) energy distributions to capture band power features. However, suchanalyses demand more specialized methods that incorporate explicitly the concepts ofneurophysiological signal generation and time resolution in the tens of milliseconds. This paperfocuses on working memory (WM), a complex cognitive process involved in encoding, storing andretrieving sensory information, which has been shown to be characterized by oscillatory bursts inthe beta and gamma band. Employing a generative model for oscillatory dynamics, we present amarked point process (MPP) representation of bursts during memory creation and readout. Weshow that the markers of the point process quantify specific neural correlates of WM. Approach. Wedemonstrate our results on field potentials recorded from the prelimbic and secondary motorcortices 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 andamplitudes 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 forsignificance in encoding of task variables, sensory stimulus and executive control while comparingencoding capabilities of our model with other T–F methods. Main Results. Firstly, the advantagesof 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, wedemonstrate the necessity of a higher time resolution alternative to conventionally employed T–Fmethods. Finally, our results underscore the novelty in interpreting oscillatory dynamicsencompassed by the marked features of the point process. Significance. An MPP representation ofmeso-scale activity not just enables a rich, high-resolution parameter space for analysis but alsopresents a novel tool for diverse neural applications.