Every animal that has been carefully studied—from the fruit fly to humans—enters a daily state that outwardly resembles a rehearsal for death. A new review published in the journal Brain Medicine argues that sleep should be viewed not simply as a period of rest or the brain's "cleaning cycle," but as a mechanism that preserves the resilience of the brain's complex neural networks, preventing them from slipping into unstable states.

The authors propose a distinction between three concepts that are often used interchangeably. Stability refers to a system's ability to maintain its state despite small disturbances. Robustness describes the ability to continue functioning in the presence of noise, damage, or other disruptions. However, they argue that the most important property is resilience—the capacity to absorb stress, reorganize, and restore normal function after disruption. According to the researchers, this is the fundamental process supported by sleep.

The review explains that sleep consists of two major functional states. During non-rapid eye movement (NREM) sleep, particularly its deep stages, the brain shifts into slow-wave activity. Neural networks become more modular, and excessively strengthened connections formed during the day are "renormalized," helping prevent network overload.

During rapid eye movement (REM) sleep, the opposite pattern emerges. Brain activity becomes more dynamic and highly integrated, promoting the reorganization of neural circuits and increasing their flexibility.

The authors place particular emphasis on the transitions between sleep stages. They suggest that during these periods the brain reaches a state of maximal metastability—a condition that enables it to switch efficiently between different functional states. According to the review, these transitional phases may be where some of the brain's most important information processing takes place.

Deep sleep also activates the glymphatic system, which expands the spaces between brain cells and facilitates the removal of metabolic waste products, including beta-amyloid, a protein associated with Alzheimer's disease. In this way, the brain is simultaneously "cleaning" itself and reorganizing its neural networks.

The researchers draw parallels with artificial neural networks, which often suffer from catastrophic forgetting and performance degradation when learning new information sequentially. In these systems, the equivalent of sleep—periods of offline data processing—helps preserve previously acquired knowledge while improving the ability to adapt to new tasks.

According to the authors, similar principles involving alternating phases of reactivation and reorganization could inspire the development of more resilient artificial intelligence systems. They suggest that sleep may be valuable not because it represents inactivity, but because it restores a system's capacity to adapt and recover after disruption.

The researchers emphasize that this is a conceptual review rather than new experimental evidence, and many of the proposed mechanisms remain hypothetical. Nevertheless, they conclude that sleep may not be a period of inactivity at all, but rather the time when the brain performs one of its most sophisticated functions: preserving its ability to recover, adapt, and remain resilient.