#neuroskyence #psychscisky

When memories are identified as targets for representational change, some of the plasticity required to implement those changes may occur later, during offline REM sleep. (9/9)

Our findings support the hypothesis that REM sleep drives representational change in the hippocampus, showing one way that REM sleep may support memory consolidation… (8/9)

We also hypothesized that neural differentiation would be correlated with the amount the predicted item came to mind during prediction errors, more so in the REM group than the Wake and non-REM sleep-only groups. This pattern was reliable (at an uncorrected threshold) in bilateral DG. (7/9)

We found more differentiation in the group with REM sleep than the Wake and non-REM sleep-only groups in the right CA2/3/DG (significant at an uncorrected threshold). An exploratory analysis found that the effect was concentrated in the right DG. (6/9)

Using the same task as Kim et al. (2017), we measured how the representations of A and B changed across a period of consolidation with fMRI. We manipulated the presence or absence of REM sleep in a daytime nap during that consolidation period (we also included a quiet wake control group). (5/9)

Kim et al. found that, when an item predicted in a particular context (e.g., A predicts B) failed to appear and was later restudied in a different context, the representations of A and B became less similar in the CA2/3/DG region of the hippocampus (Kim et al., 2017). (4/9)

Here, we sought to test the preregistered hypothesis that learning during REM sleep helps differentiate the neural representations of related memories, by expanding on a prior fMRI study by Kim et al. (2017) showing that prediction errors lead to neural differentiation in the hippocampus… (3/9)

Myriad studies have contributed to our understanding of non-REM sleep and its role in memory consolidation, but the role of REM sleep largely remains a puzzle. (2/9)

The paper also features explainer videos! For example, this video explains why associating two scenes to the same face in Favila et al. (2016) leads to differentiation: https://www.youtube.com/watch?v=dRIW6zVp4qw
(5/5)

The model predicts that, when differentiation occurs as a result of this unsupervised learning mechanism, it will be rapid and asymmetric, and it will give rise to anticorrelated representations in the region of the brain that is the source of the differentiation. (4/5)