Sleep-dependent offline performance gain in visual perceptual learning is consistent with a learning-dependent model

Abstract

Are the sleep-dependent offline performance gains of visual perceptual learning (VPL) consistent with a use-dependent or learning-dependent model? Here, we found that a use-dependent model is inconsistent with the offline performance gains in VPL. In two training conditions with matched visual usages, one generated VPL (learning condition), while the other did not (interference condition). The use-dependent model predicts that slow-wave activity (SWA) during posttraining NREM sleep in the trained region increases in both conditions, in correlation with offline performance gains. However, compared with those in the interference condition, sigma activity, not SWA, during NREM sleep and theta activity during REM sleep, source-localized to the trained early visual areas, increased in the learning condition. Sigma activity correlated with offline performance gain. These significant differences in spontaneous activity between the conditions suggest that there is a learning-dependent process during posttraining sleep for the offline performance gains in VPL.

Visual Plasticity in Adulthood: Perspectives from Hebbian and Homeostatic Plasticity

“…It is noteworthy that during NREM sleep, the power of the spindle activity and E/I ratio increased within the early visual cortex (Bang and others 2014; Tamaki and Sasaki 2020; Tamaki and others 2020). This suggests a link between the spindle event and elevated E/I ratio during NREM sleep.…”

“…One study found that the number of slow waves initiated in the occipital areas was correlated with performance improvements (Mascetti and others 2013). However, other studies did not find any changes in the slow wave activity after visual training (Bang and others 2014;Tamaki and Sasaki 2020).…”

“…Among these oscillations, spindle and theta activities were shown to increase within the trained region of early visual areas (V1, V2, V3) during NREM and REM sleep, respectively (Bang and others 2014; Tamaki and Sasaki 2020). The strength of spindle activity predicted offline performance improvement (Bang and others 2014; Tamaki and Sasaki 2020) and the power of theta activity predicted the degree that presleep learning is protected against interference from postsleep learning (Tamaki and others 2020). Interestingly, the power of theta activity was negatively correlated with the E/I ratio during REM sleep, suggesting that the higher theta power is associated with lower E/I ratios in the early visual cortex (Tamaki and others 2020).…”

“…The increase of theta activity (Tamaki and Sasaki 2020) and decrease of E/I ratio (Tamaki and others 2020) in the early visual cortex during REM sleep suggest a connection between the two during REM sleep. A compelling explanation is that synaptic downscaling is carried via theta activity in the early visual cortex during REM sleep and that this synaptic downscaling results in synaptic pruning and a decreased E/I ratio (Pereira and Lewis 2020; Tamaki and others 2020).…”

“…It is widely accepted that sharp-wave ripple activity (Buzsáki 2015; Lee and Wilson 2002), spindle activity (Bergmann and others 2012; Cairney and others 2018), and slow-wave activity during NREM sleep (Tamaki and others 2013) as well as theta activity during REM sleep (Boyce and others 2016; Nishida and others 2009) are involved in memory consolidation. Among these oscillations, spindle and theta activities were shown to increase within the trained region of early visual areas (V1, V2, V3) during NREM and REM sleep, respectively (Bang and others 2014; Tamaki and Sasaki 2020). The strength of spindle activity predicted offline performance improvement (Bang and others 2014; Tamaki and Sasaki 2020) and the power of theta activity predicted the degree that presleep learning is protected against interference from postsleep learning (Tamaki and others 2020).…”