Neural Variability Quenching Predicts Individual Perceptual Abilities

Abstract

Neural activity during repeated presentations of a sensory stimulus exhibits considerable trial-by-trial variability. Previous studies have reported that trial-by-trial neural variability is reduced (quenched) by the presentation of a stimulus. However, the functional significance and behavioral relevance of variability quenching and the potential physiological mechanisms that may drive it have been studied only rarely. Here, we recorded neural activity with EEG as subjects performed a two-interval forced-choice contrast discrimination task. Trial-by-trial neural variability was quenched by ϳ40% after the presentation of the stimulus relative to the variability apparent before stimulus presentation, yet there were large differences in the magnitude of variability quenching across subjects. Individual magnitudes of quenching predicted individual discrimination capabilities such that subjects who exhibited larger quenching had smaller contrast discrimination thresholds and steeper psychometric function slopes. Furthermore, the magnitude of variability quenching was strongly correlated with a reduction in broadband EEG power after stimulus presentation. Our results suggest that neural variability quenching is achieved by reducing the amplitude of broadband neural oscillations after sensory input, which yields relatively more reproducible cortical activity across trials and enables superior perceptual abilities in individuals who quench more.

Mathematics and the brain –a category theoretic approach to go beyond the neural correlates of consciousness

“…In terms of the response amplitude, low pre-stimulus activity levels lead to relative higher post-stimulus activity levels than high prestimulus activity levels [32,50,51]. Importantly, recent studies in both MEG [11,66] and fMRI [51] demonstrate that pre-stimulus variance and its non-additive impact on post-stimulus amplitude/variance is related to conscious contents [2,11,66,93] and the level/state of consciousness [51]. Most interestingly, a recent study demonstrated that pre-post-stimulus variance are accompanied by the Lempel-Zev Complexity (LZC) in the pre-stimulus interval [107,108].…”

“…To support this claim, we consider the empirical data in both fMRI [14,45,88,89] and EEG/MEG [2,8,11,66], which show that the amplitude and/or variance of pre-stimulus activity plays a major role in whether the subsequent stimulus and its respective contents becomes conscious or not. Typically, high pre-stimulus activity levels, e.g., high amplitude or variance, are more likely to allow for associating contents with consciousness than low pre-stimulus activity levels.…”

“…When there is an inclusion functor from C to D, C is called as a subcategory of D. 2 The intuition for the terms can be explained as follows (also see Fig 2b): Let us consider the situation that any object/arrow in C has a corresponding object/arrow in D, and the notion of dom/cod, composition and identity for C are in common with those for D. Then it is quite natural to think of C as subsystem of D and thus to call C a subcategory of D. In this situation, we can define an inclusion functor F as a map sending each object/arrow in C as object/arrow in D, i.e. F(X)=X and F(f)=f for any object X and arrow f (Here X and f in the left hand side are an object and an arrow in C and those in the right hand side are those considered in D).…”

Spatial attention enhances cortical tracking of quasi-rhythmic visual stimuli

“…This release from an internal pacemaker allows affected neuronal populations to couple more strongly to stimulus dynamics. Further evidence for this perspective comes from observations that stimulus presentation itself and the allocation of attention both reduce neural variability, a measure thought to reflect an increase in cortical signal-to-noise ratio (Arazi, Censor, & Dinstein, 2017;Arazi, Yeshurun, & Dinstein, 2019;Deco & Hugues, 2012). Interestingly, in turn, changes in neural variability seem to be tied most closely to fluctuations in the power of alpha rhythms (Daniel, Meindertsma, Arazi, Donner, & Dinstein, 2019).…”

The relationship between neural variability and neural oscillations

“…Neural variability quenching is a robust phenomenon that has been reported in intracellular membrane potential recordings in cats, extracellular recordings of spiking activity in monkeys (Churchland et al, 2010(Churchland et al, , 2006, and in human electroencephalography (EEG) (Arazi et al, 2017a(Arazi et al, , 2017bSchurger et al, 2015), electrocorticography (ECOG) (He and Zempel, 2013), MEG (Schurger et al, 2015), and functional magnetic resonance imaging (fMRI) recordings (Broday-Dvir et al, 2018;He, 2013). Furthermore, the phenomenon was reported during both awake and anaesthetized states, and in several cortical areas (Churchland et al, 2010;He, 2013) using a variety of sensory stimuli (Arazi et al, 2017b;Churchland et al, 2010).…”