Inverse forgetting in unconscious episodic memory.

Forming memories of experienced episodes calls upon the episodic memory system. Episodic encoding may proceed with and without awareness of episodes. While up to 60% of consciously encoded episodes are forgotten after 10 h, the fate of unconsciously encoded episodes is unknown. Here we track over 10 h, which are filled with sleep or daytime activities, the retention of unconsciously and consciously experienced episodes. The episodes were displayed in cartoon clips that were presented weakly and strongly masked for conscious and unconscious encoding, respectively. Clip retention was tested for distinct clips directly after encoding, 3 min and 10 h after encoding using a forced-choice test that demands deliberate responses in both consciousness conditions. When encoding was conscious, retrieval accuracy decreased by 25% from 3 min to 10 h, irrespective of sleep or wakefulness. When encoding was unconscious, retrieval accuracy increased from 3 min to 10 h and depended on sleep. Hence, opposite to the classic forgetting curve, unconsciously acquired episodic memories strengthen over time and hinge on sleep on the day of learning to gain influence over human behavior.

Closed-loop modulation of local slow oscillations in human NREM sleep.

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations.