A fundamental feature of human memory is the propensity for beneficial changes in information storage after initial encoding. Recent research findings favor the possibility that memory consolidation during sleep is instrumental for actively maintaining the storehouse of memories that individuals carry through their lives. The information that ultimately remains available for retrieval may tend to be that which is reactivated during sleep. A novel source of support for this idea comes from demonstrations that neurocognitive processing during sleep can benefit memory storage when memories are covertly cued via auditory or olfactory stimulation. Investigations of these subtle manipulations of memory processing during sleep can help elucidate the mechanisms of memory preservation in the human brain.

upgrading the sleeping brain with targeted memory reactivation : 142

Slow oscillations and sleep spindles are hallmarks of the EEG during slow-wave sleep (SWS). Both oscillatory events, especially when co-occurring in the constellation of spindles nesting in the slow oscillation upstate, are considered to support memory formation and underlying synaptic plasticity. The regulatory mechanisms of this function at the circuit level are poorly understood. Here, using two-photon imaging in mice, we relate EEG-recorded slow oscillations and spindles to calcium signals recorded from the soma of cortical putative pyramidal-like (Pyr) cells and neighboring parvalbumin-positive interneurons (PV-Ins) or somatostatin-positive interneurons (SOM-Ins). Pyr calcium activity was increased more than threefold when spindles co-occurred with slow oscillation upstates compared with slow oscillations or spindles occurring in isolation. Independent of whether or not a spindle was nested in the slow oscillation upstate, the slow oscillation downstate was preceded by enhanced calcium signal in SOM-Ins that vanished during the upstate, whereas spindles were associated with strongly increased PV-In calcium activity. Additional wide-field calcium imaging of Pyr cells confirmed the enhanced calcium activity and its widespread topography associated with spindles nested in slow oscillation upstates. In conclusion, when spindles are nested in slow oscillation upstates, maximum Pyr activity appears to concur with strong perisomatic inhibition of Pyr cells via PV-Ins and low dendritic inhibition via SOM-Ins (i.e., conditions that might optimize synaptic plasticity within local cortical circuits).

https://www.pnas.org/content/pnas/115/39/E9220.full.pdf

Cortical circuit activity underlying sleep slow oscillations and spindles.

The 0.180), indicating the presence of a mechanism preventing the development of hypersynchronicity during SO activity. Assessment of temporal dynamics revealed a rapidly fading phase-locked spindle activity during repetitive click stimulation, suggesting that spindle refractoriness contributes to this protective mechanism.

https://www.jneurosci.org/content/jneuro/35/17/6630.full.pdf

Driving Sleep Slow Oscillations by Auditory Closed-Loop Stimulation—A Self-Limiting Process

Visual scene analysis in humans has been characterized by the presence of regions in extrastriate cortex that are selectively responsive to scenes compared with objects or faces. While these regions have often been interpreted as representing high-level properties of scenes (e.g. category), they also exhibit substantial sensitivity to low-level (e.g. spatial frequency) and mid-level (e.g. spatial layout) properties, and it is unclear how these disparate findings can be united in a single framework. In this opinion piece, we suggest that this problem can be resolved by questioning the utility of the classical low- to high-level framework of visual perception for scene processing, and discuss why low- and mid-level properties may be particularly diagnostic for the behavioural goals specific to scene perception as compared to object recognition. In particular, we highlight the contributions of low-level vision to scene representation by reviewing (i) retinotopic biases and receptive field properties of scene-selective regions and (ii) the temporal dynamics of scene perception that demonstrate overlap of low- and mid-level feature representations with those of scene category. We discuss the relevance of these findings for scene perception and suggest a more expansive framework for visual scene analysis.This article is part of the themed issue 'Auditory and visual scene analysis'.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2016.0102

Contributions of low- and high-level properties to neural processing of visual scenes in the human brain

Slow-wave sleep: From the cell to the clinic.

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep.

/pdf/sound-asleep-processing-and-retention-of-slow-oscillation-5ooyh0b2o2.pdf

Sound asleep: Processing and retention of slow oscillation phase-targeted stimuli

Visual perception starts with localized filters that subdivide the image into fragments that undergo separate analyses. The visual system has to reconstruct objects by grouping image fragments that belong to the same object. A widely held view is that perceptual grouping occurs in parallel across the visual scene and without attention. To test this idea, we measured the speed of grouping in pictures of animals and vehicles. In a classification task, these pictures were categorized efficiently. In an image-parsing task, participants reported whether two cues fell on the same or different objects, and we measured reaction times. Despite the participants' fast object classification, perceptual grouping required more time if the distance between cues was larger, and we observed an additional delay when the cues fell on different parts of a single object. Parsing was also slower for inverted than for upright objects. These results imply that perception starts with rapid object classification and that rapid classification is followed by a serial perceptual grouping phase, which is more efficient for objects in a familiar orientation than for objects in an unfamiliar orientation.

The Time Course of Perceptual Grouping in Natural Scenes

Location and color biases have different influences on selective attention.

In a contour-grouping task, subjects decide whether contour elements belong to the same or different curves. Houtkamp, Spekreijse, and Roelfsema (2003) demonstrated that objectbased attention spreads gradually over contour elements that have to be grouped in perception. Crundall, Dewhurst, and Underwood (2008) challenged this spreading-attention model and suggested that attention in the contour-grouping task is not object based but rather has the shape of a zoom lens that moves along the relevant curve. To distinguish between object-based and spatial attention, they changed the stimulus and measured the impact on performance. Subjects were not able to correct for changes at the start of the relevant curve toward the end of the trial. They suggested that attention did not stay at the beginning of the curve, in accordance with a moving zoom lens model. Here, we examine the task of Crundall et al. and find that subjects perceive the changes but fail to correct their response. By measuring change detection directly, we find that performance is much better for the start of the relevant curve than for an irrelevant curve, at all times. Our findings do not support the zoom lens model but provide further support for the spreadingattention model.

/pdf/further-evidence-for-the-spread-of-attention-during-contour-17q37n3jv7.pdf

Further evidence for the spread of attention during contour grouping: a reply to Crundall, Dewhurst, and Underwood (2008)

Two prominent cognitive capacity limitations are the maximal number of objects we can place in working memory (WM) and the maximal number of objects we can track in a display. Both are believed to have a numeric value of 3 or 4, which has led to the proposal that we have a general cognitive capacity, and that this capacity is most likely linked to limitations of how many objects we can attend simultaneously. Based on previous results showing that we can memorize more objects if they come from different categories than if they come from the same category (e.g., Feigenson & Halberda, 2008; Wood, 2008; Wong, Peterson, & Thompson, 2008), we compare how category-based grouping affects performance for WM and multiple object tracking (MOT). We present participants with either “pure” displays of either cars or faces, or with “mixed” displays of cars and faces. Overall, the effects of category are weak. In some analyses but not others, we replicate the mixed advantage for WM, albeit with a small effect size. In contrast, we observe a weak pure advantage for MOT tasks, at least in a meta-analysis of five experiments, but not in all experiments. Accordingly, WM and MOT tasks differed significantly in their sensitivity to category membership. We also find that WM is slightly better for faces than for cars, but that no such difference exists for MOT. We tentatively suggest that cognitive capacity limitations in different domains are at least partially due to limitations of distinct mechanisms.

/pdf/category-based-grouping-in-working-memory-and-multiple-2202bt6nzu.pdf

Ilia Korjoukov

Papers

Sound asleep: Processing and retention of slow oscillation phase-targeted stimuli

The Time Course of Perceptual Grouping in Natural Scenes

Location and color biases have different influences on selective attention.

Further evidence for the spread of attention during contour grouping: a reply to Crundall, Dewhurst, and Underwood (2008)

Category-based grouping in working memory and multiple object tracking