Sleep-dependent memory triage: evolving generalization through selective processing

Abstract

The brain does not retain all the information it encodes in a day. Much is forgotten, and of those memories retained, their subsequent evolution can follow any of a number of pathways. Emerging data makes clear that sleep is a compelling candidate for performing many of these operations. But how does the sleeping brain know which information to preserve and which to forget? What should sleep do with that information it chooses to keep? For information that is retained, sleep can integrate it into existing memory networks, look for common patterns and distill overarching rules, or simply stabilize and strengthen the memory exactly as it was learned. We suggest such 'memory triage' lies at the heart of a sleep-dependent memory processing system that selects new information, in a discriminatory manner, and assimilates it into the brain's vast armamentarium of evolving knowledge, helping guide each organism through its own, unique life.

Figure 1. Selective memory consolidation

(A) Conceptual difference between uniform consolidation (top row), and selective consolidation (bottom row). In the latter, sleep returns discriminative offline memory retention, the selection of which is governed by instructional cues of relevance (red) and non-relevance (blue) created in the peri-encoding wake period. (B) Conceptual outcome of selective consolidation following sleep and an equivalent time wake (across the night or day) following differential tagged relevance at initial encoding.

Figure 2. Forms of memory evolution

Categories of offline memory processing. All of these forms of offline memory processing have been shown to occur preferentially during sleep. (a) Item consolidation. Individual item-memories can be stabilized and/or enhanced, or they can be forgotten. (b) Item integration. Individual new item memories can be integrated into existing associative memory networks, extending the range of the network and enriching the information associated with the new item memory. (c) Multi-item generalization. Related item-memories encoded over a brief time interval can generate a new memory network and conceptual schema.

Figure 3. Examples of memory evolution

The integration of memory is often enhanced by sleep (green) compared to equivalent periods of wake (red): (A) Spatial learning: Exploration of a virtual maze produces complex episodic memories of the experience. Sleep facilitates the extraction of a generalized spatial map of the maze, resulting in enhanced maze navigation speed, while an equivalent time spent awake leads to degraded maze navigation (from). (B) False memories: Extraction of the gist of a set of recent item-memories leads to the false belief that the gist was part of the original memory set. Sleep shows both of these consequences of gist extraction, including preservation of the gist memory while actual studied items are forgotten, and while memory for both item-memories and gist decrease across wake (from). (C) Transitive inference: Transitive inference was absent 20min after training (−T−S), but was seen after 12hr of wake (+T−S). After 12hr including a night of sleep (+T+S), performance on second-order inferences was significantly further enhanced (from). (D) Probabilistic Learning: Statistical sequence learning (left) was enhanced after a 12hr period containing a night of sleep, but not after equivalent periods without sleep (orange bar; from). Similarly, probabilistic category learning, studied in the weather prediction task (right), showed significant improvement following a night of sleep, and significantly more than after an equal period of daytime wake, when no significant improvement was seen. (E) Following wake, subjects rated the probabilities of four card stimuli predicting sunshine into pairs of high and low probabilities (red triangles), while following sleep, they more accurately described the cards’ individually varying probabilities (green triangles) (from). (F) Mathematical insight: LEFT – Subjects trained on a rote mathematical task were significantly more likely to discover a shortcut during retesting after a night of sleep (+N+S), as compared to after equivalent periods of wake across the day (−N−S) or night (+N−S). RIGHT – Those who failed to gain this insight instead showed significant improvement in the speed with which they performed the rote procedure (from).