Prestimulus oscillatory neural activity in the visual cortex has large consequences for perception and can be influenced by top-down control from higher-order brain regions. Making a causal claim about the mechanistic role of oscillatory activity requires that oscillations be directly manipulated independently of cognitive instructions. There are indications that a direct manipulation, or entrainment, of visual alpha activity is possible through visual stimulation. However, three important questions remain: (1) Can the entrained alpha activity be endogenously maintained in the absence of continuous stimulation?; (2) Does entrainment of alpha activity reflect a global or a local process?; and (3) Does the entrained alpha activity influence perception? To address these questions, we presented human subjects with rhythmic stimuli in one visual hemifield, and arhythmic stimuli in the other. After rhythmic entrainment, we found a periodic pattern in detection performance of near-threshold targets specific to the entrained hemifield. Using magnetoencephalograhy to measure ongoing brain activity, we observed strong alpha activity contralateral to the rhythmic stimulation outlasting the stimulation by several cycles. This entrained alpha activity was produced locally in early visual cortex, as revealed by source analysis. Importantly, stronger alpha entrainment predicted a stronger phasic modulation of detection performance in the entrained hemifield. These findings argue for a cortically focal entrainment of ongoing alpha oscillations by visual stimulation, with concomitant consequences for perception. Our results support the notion that oscillatory brain activity in the alpha band provides a causal mechanism for the temporal organization of visual perception.

Summary While the mammalian neocortex has a clear laminar organization, layer-specific neuronal computations remain to be uncovered. Several studies suggest that gamma band activity in primary visual cortex (V1) is produced in granular and superficial layers and is associated with the processing of visual input [1–3]. Oscillatory alpha band activity in deeper layers has been proposed to modulate neuronal excitability associated with changes in arousal and cognitive factors [4–7]. To investigate the layer-specific interplay between these two phenomena, we characterized the coupling between alpha and gamma band activity of the local field potential (LFP) in V1 of the awake macaque. Using multicontact laminar electrodes to measure spontaneous signals simultaneously from all layers of V1, we found a robust coupling between alpha phase in the deeper layers and gamma amplitude in granular and superficial layers. Moreover, the power in the two frequency bands was anticorrelated. Taken together, these findings demonstrate robust inter-laminar cross-frequency coupling in the visual cortex, supporting the view that neuronal activity in the alpha frequency range phasically modulates processing in the cortical microcircuit in a top-down manner [7].

Working memory (WM) provides the stability necessary for high-level cognition. Influential theories typically assume that WM depends on the persistence of stable neural representations, yet increasing evidence suggests that neural states are highly dynamic. Here we apply multivariate pattern analysis to explore the population dynamics in primate lateral prefrontal cortex (PFC) during three variants of the classic memory-guided saccade task (recorded in four animals). We observed the hallmark of dynamic population coding across key phases of a working memory task: sensory processing, memory encoding, and response execution. Throughout both these dynamic epochs and the memory delay period, however, the neural representational geometry remained stable. We identified two characteristics that jointly explain these dynamics: (1) time-varying changes in the subpopulation of neurons coding for task variables (i.e., dynamic subpopulations); and (2) time-varying selectivity within neurons (i.e., dynamic selectivity). These results indicate that even in a very simple memory-guided saccade task, PFC neurons display complex dynamics to support stable representations for WM.SIGNIFICANCE STATEMENT Flexible, intelligent behavior requires the maintenance and manipulation of incoming information over various time spans. For short time spans, this faculty is labeled “working memory” (WM). Dominant models propose that WM is maintained by stable, persistent patterns of neural activity in prefrontal cortex (PFC). However, recent evidence suggests that neural activity in PFC is dynamic, even while the contents of WM remain stably represented. Here, we explored the neural dynamics in PFC during a memory-guided saccade task. We found evidence for dynamic population coding in various task epochs, despite striking stability in the neural representational geometry of WM. Furthermore, we identified two distinct cellular mechanisms that contribute to dynamic population coding.

Human perceptual decisions can be repelled away from (repulsive adaptation) or attracted towards recent visual experience (attractive serial dependence). It is currently unclear whether and how these repulsive and attractive biases interact during visual processing and what computational principles underlie these history dependencies. Here we disentangle repulsive and attractive biases by exploring their respective timescales. We find that perceptual decisions are concurrently attracted towards the short-term perceptual history and repelled from stimuli experienced up to minutes into the past. The temporal pattern of short-term attraction and long-term repulsion cannot be captured by an ideal Bayesian observer model alone. Instead, it is well captured by an ideal observer model with efficient encoding and Bayesian decoding of visual information in a slowly changing environment. Concurrent attractive and repulsive history biases in perceptual decisions may thus be the consequence of the need for visual processing to simultaneously satisfy constraints of efficiency and stability.

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.

New research suggests that magnetoencephalography (MEG) contains rich spatial information for decoding neural states. Even small differences in the angle of neighbouring dipoles generate subtle, but statistically separable field patterns. This implies MEG (and electroencephalography: EEG) is ideal for decoding neural states with high-temporal resolution in the human brain.

Recently there has been a strong interest in cross-frequency coupling, the interaction between neuronal oscillations in different frequency bands. In particular, measures quantifying the coupling between the phase of slow oscillations and the amplitude of fast oscillations have been applied to a wide range of data recorded from animals and humans. Some of the measures applied to detect phase-amplitude coupling have been criticized for being sensitive to nonsinusoidal properties of the oscillations and thus spuriously indicate the presence of coupling. While such instances of spurious identification of coupling have been observed, in this commentary we give concrete examples illustrating cases when the identification of cross-frequency coupling can be trusted. These examples are based on control analyses and empirical observations rather than signal-processing tools. Finally, we provide concrete advice on how to determine when measures of phase-amplitude coupling can be considered trustworthy.

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