An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval

Kerrén, Casper; Linde-Domingo, Juan; Hanslmayr, Simon; Wimber, Maria

doi:10.1016/j.cub.2018.08.065

Cited by 98 publications

(133 citation statements)

References 70 publications

(107 reference statements)

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“…For example, Clouter, Shapiro, and Hanslmayr () demonstrated that two stimuli presented at the same phase of theta are more likely to be successfully encoded than two stimuli that are presented at opposing phases, indicating that associative memory formation varies as a function of theta phase. Moreover, Kerrén, Linde‐Domingo, Hanslmayr, and Wimber () demonstrated that neural evidence for retrieved stimuli fluctuate at approximately 7 Hz, suggesting that episodic memory retrieval is also dependent on theta phase.…”

Section: A Role For Neural Oscillationsmentioning

confidence: 99%

Event conjunction: How the hippocampus integrates episodic memories across event boundaries

Griffiths

Fuentemilla

2019

Hippocampus

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Our lives are a continuous stream of experience. Our episodic memories on the other hand have a definitive beginning, middle, and end. Theories of event segmentation suggest that salient changes in our environment produce event boundaries which partition the past from the present and, as a result, produce discretized memories. However, event boundaries cannot completely discretize two memories; any shared conceptual link will lead to the rapid integration of these memories. Here, we present a new framework inspired by electrophysiological research that resolves this apparent contradiction. At its heart, the framework proposes that hippocampal theta‐gamma coupling maintains a highly abstract model of an ongoing event and serves to encode this model as an episodic memory. When a second but related event begins, this theta‐gamma model is rapidly reconstructed within the hippocampus where new details of the second event can be appended to the existing event model. The event conjunction framework is the first electrophysiological explanation of how event memories can be formed at, and integrated across, event boundaries.

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Section: A Role For Neural Oscillationsmentioning

confidence: 99%

Event conjunction: How the hippocampus integrates episodic memories across event boundaries

Griffiths

Fuentemilla

2019

Hippocampus

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“…Recent scalp (Kerrén et al, 2018; Michelmann et al, 2016) and intracranial EEG studies (Staresina et al, 2016; Zhang et al, 2015) further demonstrated the particular potential of frequency-transformed activity patterns in identifying memory-relevant reactivation of item-specific signatures. For example, Michelmann et al (2018) showed that desynchronized low-frequency brain oscillations carried stimulus-specific temporal activity patterns during an associative memory task.…”

Section: Introductionmentioning

confidence: 99%

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Sommer

Fandakova

Grandy

et al. 2019

Preprint

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18Age-related memory decline is associated with changes in neural functioning but little is known 19 about how aging affects the quality of information representation in the brain. Whereas a 20 long-standing hypothesis of the aging literature links cognitive impairments to less distinct 21 neural representations in old age, memory studies have shown that high similarity between 22 activity patterns benefits memory performance for the respective stimuli. Here, we addressed 23 this apparent conflict by investigating between-item representational similarity in 50 younger 24 (19-27 years old) and 63 older (63-75 years old) human adults (male and female) who studied 25 scene-word associations using a mnemonic imagery strategy while electroencephalography was 26 recorded. We compared the similarity of spatiotemporal frequency patterns elicited during 27 encoding of items with different subsequent memory fate. Compared to younger adults, older 28 adults' memory representations were more similar to each other but items that elicited the 29 most similar activity patterns early in the encoding trial were those that were best remembered 30 by older adults. In contrast, young adults' memory performance benefited from decreased 31 similarity between earlier and later periods in the encoding trials, which might reflect their 32 better success in forming unique memorable mental images of the joint picture-word pair. 33Our results advance the understanding of the representational properties that give rise to 34 memory quality as well as how these properties change in the course of aging. 35 3 Significance statement 36Declining memory abilities are one of the most evident limitations for humans when growing 37 older. Despite recent advances of our understanding of how the brain represents and sto-38 res information in distributed activation patterns, little is known about how the quality of 39 information representation changes during aging and thus affects memory performance. We 40 investigated how the similarity between neural representations relates to subsequent memory 41 quality in younger and older adults. We present novel evidence that the interaction of pattern 42 similarity and memory performance differs between age groups: Older adults benefited from 43 increased similarity during early encoding whereas young adults benefited from decreased si-44 milarity between early and later encoding. These results provide insights into the nature of 45 memory and age-related memory deficits. 46 4 47 A long-standing hypothesis in the cognitive neuroscience of aging holds that neural represen-48 tations become less specific with advancing age, with detrimental effects on cognitive perfor-49 mance. Reduced neural distinctiveness in older compared to young adults (Li et al., 2001) 50 has been observed as increased similarity and/or reduced discriminability between neural 51 activity patterns during different memory tasks (Carp et al., 2010; St-Laurent et al., 2011), 52 between different stimulus categories (Carp et al., 2011; Park e...

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“…The distance to the hyperplane indicates the amount of classifier evidence present in the data. Distance measures have previously been used as a measure of model activation (23,32) and have been linked to reaction time measurements (33). For each perception model and for each imagery trial, we identified the time of the absolute peak distance (23).…”

Section: Resultsmentioning

confidence: 99%

Neural dynamics of perceptual inference and its reversal during imagery

Dijkstra

Ambrogioni

Gerven

2019

Preprint

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After the presentation of a visual stimulus, cortical visual processing cascades from low-level sensory features in primary visual areas to increasingly abstract representations in higherlevel areas. It is often hypothesized that the reverse process underpins the human ability to generate mental images. Under this hypothesis, visual information feeds back from high-level areas as abstract representations are used to construct the sensory representation in primary visual cortices. Such reversals of information flow are also hypothesized to play a central role in later stages of perception. According to predictive processing theories, ambiguous sensory information is resolved using abstract representations coming from high-level areas through oscillatory rebounds between different levels of the visual hierarchy. However, despite the elegance of these theoretical models, to this day there is no direct experimental evidence of the reversion of visual information flow during mental imagery and perception. In the first part of this paper, we provide direct evidence in humans for a reverse order of activation of the visual hierarchy during imagery. Specifically, we show that classification machine learning models trained on brain data at different time points during the early feedforward phase of perception are reactivated in reverse order during mental imagery. In the second part of the paper, we report an 11Hz oscillatory pattern of feedforward and reversed visual processing phases during perception. Together, these results are in line with the idea that during perception, the high-level cause of sensory input is inferred through recurrent hypothesis updating, whereas during imagery, this learned forward mapping is reversed to generate sensory signals given abstract representations. Perception | Mental imagery | MEG | Multivariate pattern analysis | Generative models | Predictive processingCorrespondence: n.dijkstra@donders.ru.nl When light hits the retina, a complex cascade of neural processing is triggered. Light waves are transformed into electrical signals that travel via the lateral geniculate nucleus of the thalamus to the visual cortex (1, 2). First, low-level visual features such as orientation and spatial frequency are processed in primary, posterior visual areas (3) after which activation spreads forward towards secondary, more anterior visual areas where high-level features such as shape and eventually semantic category are processed (4-6). This initial feedforward flow through the visual hierarchy is completed within 150 ms (7,8) after which feedback processes are assumed to further sharpen representations over time until a stable percept is achieved (9, 10). Activation in visual areas can also be triggered internally, in the absence of external sensory signals. During mental imagery, information from memory is used to generate rich visual representations. Neural representations activated during imagery are highly similar to those activated during perception (11). Imagining an object activates similar obje...

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An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval

Cited by 98 publications

References 70 publications

Event conjunction: How the hippocampus integrates episodic memories across event boundaries

Event conjunction: How the hippocampus integrates episodic memories across event boundaries

Neural pattern similarity differentially affects memory performance of younger and older adults: Age differences in neural similarity and memory

Neural dynamics of perceptual inference and its reversal during imagery

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