Figure 3. Non-oscillatory electrophysiological features track excitability during sleep.

(A) Replay of information (green; baseline in orange) is not frequency-specific but exhibits broadband spectral signatures. Superimposed (dark and light grey) spectral slope estimates that show a shift along the y-axis and imply a rotation of the power spectrum. (B) Left: Electrophysiological power spectra during sleep. During wakefulness alpha oscillations at ~11 Hz are prominent (red), while slow wave sleep (SWS, blue) is characterized by increased delta (< 4 Hz) and spindle power (~16 Hz). Note that REM (green) does not exhibit any prominent oscillations as indicated by the absence of a characteristic bump exceeding the 1/f drop-off. Right: Quantification of the slope of the 1/f drop-off reliably distinguishes sleep stages. (C) Computational model linking the shape of the power spectrum to the balance between excitation and inhibition (E/I-balance). Increasing the level of inhibition from 1:2 (orange) to 1:6 (green) resulted in a steepening of the power spectrum slope. (D) Two-photon calcium imaging demonstrated a relative increase in inhibition. Left: Inhibitory parvalbumin+ interneuron (PV-IN) activity was increased during REM sleep. Right: Overall firing rates were reduced during SWS and REM sleep. This effect was highly significant for unlabeled cells, including pyramidal cells as well as somatostatin interneurons, thus, nicely mimicking the distribution as observed for human slope estimates at the population level (cf. right side in panel B). In summary, this set of findings indicates that a relative increase in inhibition is mediated by a decrease in overall firing as well as an increase in inhibitory drive. Panel A was modified with permission under the Creative Commons CC-BY license from [80], panel B was published under the CC-BY license and adapted from [18], panel C was adapted with permission from [87] and panel D was adapted with permission from [88].