Slow rhythms of activity (～ 1 Hz) are a universal hallmark of slow-wave sleep and deep anesthesia across many animal species. A remarkably reproducible default mode with a low degree of complexity which opens a window on the brain multiscale organization, on top of which cognitive functions emerge during wakefulness. Understanding how such transition takes place starting from the characterization of a stereotyped state like the slow-wave activity, might shade light on the emergence of the rich repertoire of neuronal dynamics underlying brain computation. Sleep-wake transition is a widely studied phenomenon, however it is still debated how brain state changes occur. Here we show from intracortical recordings in anesthetized rats, that sleep-like rhythms fade out when wakefulness is approached giving rise to an alternation between slow Up/Down oscillations and awake-like activity periods. This phase of activity pattern bistability is captured by a mean-field rate-based model of a cortical column, in which for a transient range of anesthesia levels the coexistence of two metastable attractor states emerges. Alternation between these two states is granted by the ongoing competition of local features and exogenous modulations governing both the excitability and the fatigue level of the modeled column. Our results highlight a brain state transition which is not a continuous smooth change but rather a progressive modulation of the stability of two coexistent activity regimes, which in turn leave different fingerprints across cortical columns. Guided by this mean-field model, spiking neuron networks are devised to reproduce the electrophysiological changes displayed during the transition.
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