Waveform detection by deep learning reveals multi-area spindles that are selectively modulated by memory load

Sleep is generally considered to be a state of large-scale synchrony across thalamus and neocortex; however, recent work has challenged this idea by reporting isolated sleep rhythms such as slow-oscillations and spindles. What is the spatial scale of sleep rhythms? To answer this question, we adapted deep learning algorithms initially developed for detecting earthquakes and gravitational waves in high-noise settings for analysis of neural recordings in sleep. We then studied sleep spindles in non-human primate ECoG, human EEG, and clinical intracranial recordings (iEEG) in the human. We find a widespread extent of spindles, which has direct implications for the spatiotemporal dynamics we have previously studied in spindle oscillations ([Muller et al., 2016][1]) and the distribution of memory engrams in the primate. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-38