Targeted Memory Reactivation during Sleep Elicits Neural Signals Related to Learning Content

Abstract

Retrieval of learning-related neural activity patterns is thought to drive memory stabilization. However, finding reliable, noninvasive, content-specific indicators of memory retrieval remains a central challenge. Here, we attempted to decode the content of retrieved memories in the EEG during sleep. During encoding, male and female human subjects learned to associate spatial locations of visual objects with left-or right-hand movements, and each object was accompanied by an inherently related sound. During subsequent slow-wave sleep within an afternoon nap, we presented half of the sound cues that were associated (during wake) with left-and right-hand movements before bringing subjects back for a final postnap test. We trained a classifier on sleep EEG data (focusing on lateralized EEG features that discriminated left-vs right-sided trials during wake) to predict learning content when we cued the memories during sleep. Discrimination performance was significantly above chance and predicted subsequent memory, supporting the idea that retrieval leads to memory stabilization. Moreover, these lateralized signals increased with postcue sleep spindle power, demonstrating that retrieval has a strong relationship with spindles. These results show that lateralized activity related to individual memories can be decoded from sleep EEG, providing an effective indicator of offline retrieval.

Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

“…However, there is considerable variability in signal characteristics across SOs and spindles (e.g., event durations or peak times), and such across-event variability diminishes classification power which relies on spatiotemporal activation patterns common across events. That said, decoding levels observed here are in line with previous TMR studies examining sleep-related memory reactivation with multivariate classification 36 , 53 , 57 . Importantly, we found that higher decoding performance correlates with the behavioral expression of memory consolidation across participants, further corroborating the functional significance of reactivation.…”

Unsupervised neural network models of the ventral visual stream

“…Given the simplicity of the contrastive loss formula, it is not hard to imagine that it could be implemented by a real neural circuit driving plasticity based on both similarity and differentiation ( 67 ). Conceptually, this neural learning circuit would complement the neural system to be learned (e.g., the ventral pathway), computing and relaying errors back to the system as learning occurs, either rapidly in real time ( 68 ) or possibly with delayed batching as part of memory consolidation ( 69 ). Following up on these possibilities will require the detailed comparison of real-time empirical representation changes during learning ( 67 , 68 ) to fine-scale model updates during training.…”

Sleep Spindles: Mechanisms and Functions

“…In a similar approach, cued responses that successfully elicited sleep spindles also predicted the quality of later memory retrieval. Furthermore, the greater the associated sigma power increase, the better was the later performance (630). Cueing was also successfully used to probe potential different roles in SO-spindle coupling during N2 and N3 sleep in humans (241).…”