Memories replayed: reactivating past successes and new dilemmas

Abstract

Our experiences continue to be processed 'offline' in the ensuing hours of both wakefulness and sleep. During these different brain states, the memory formed during our experience is replayed or reactivated. Here, we discuss the unique challenges in studying offline reactivation, the growth in both the experimental and analytical techniques available across different animals from rodents to humans to capture these offline events, the important challenges this innovation has brought, our still modest understanding of how reactivation drives diverse synaptic changes across circuits, and how these changes differ (if at all), and perhaps complement, those at memory formation. Together, these discussions highlight critical emerging issues vital for identifying how reactivation affects circuits, and, in turn, behaviour, and provides a broader context for the contributions in this special issue. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Keywords: memory; memory consolidation; reactivation; replay; sleep.

Figure 1.

Methods to measure reactivation that use supervised-learning techniques. (a) Pairwise correlation showing the correlations between neuron pairs and how they change from pre-sleep (top), to during the task (middle) and post-sleep (bottom; for example, see [23]). Each dot on the circle represents a neuron and the line thickness indicates correlation strength. (b) Sequence replay in which each line represents the activity of one neuron, thus the sequence of activity during the task (left) can be found in a time-compressed replay during subsequent rest (right; for example, see [24]). (c) Similar time compression can also be found in template matching techniques, for which the actual sequence between neurons is not critical (for example, see [25]). (d) Finally, dimensionality reduction techniques such as principle component analysis (PCA) can also be used to identify cell groups and then be used to track the cell group activity across time (for example, see [26,27]). For examples of unsupervised-learning machine learning techniques that are used for memory reactivation analysis please see [17] in this issue. (Online version in colour.)

Figure 2.

Different oscillations that have been linked to memory reactivations. (a) The slow oscillation (SO) is caused by global on-off states during NREM sleep—which is visible on the surface EEG as a K-complex (0.5–1.5 Hz). (b) Slow wave activity (SWA, or delta waves, 1–4 Hz), which are owing to local on-off states occurring mainly during deep or slow wave sleep. (c) The sleep spindle (12–16 Hz). This is present throughout all NREM sleep. (d) The sharp-wave ripple (SWR) of the hippocampus. The SWR is comprised two different components—the ripple and the sharp wave—that are seen on different electrode sites. The ripple occurs in the pyramidal layer while the sharp wave occurs in the input layer.