CBD lengthens sleep but shortens ripples and leads to intact simple but worse cumulative memory.

Cannabidiol (CBD) is on the rise as over-the-counter medication to treat sleep disturbances, anxiety, pain, and epilepsy due to its action on the excitatory/inhibitory balance in the brain. However, it remains unclear if CBD also leads to adverse effects on memory via changes of sleep macro- and microarchitecture. To investigate the effect of CBD on sleep and memory consolidation, we performed two experiments using the object space task testing for both simple and cumulative memory in rats. We show that oral CBD administration extended the sleep period but changed the properties of rest and non-REM sleep oscillations (delta, spindle, ripples). Specifically, CBD also led to less long (>100 ms) ripples and, consequently, worse cumulative memory consolidation. In contrast, simple memories were not affected. In sum, we can confirm the beneficial effect of CBD on sleep; however, this comes with changes in oscillations that negatively impact memory consolidation.

Increased cortical plasticity leads to memory interference and enhanced hippocampal-cortical interactions.

Our brain is continuously challenged by daily experiences. Thus, how to avoid systematic erasing of previously encoded memories? While it has been proposed that a dual-learning system with 'slow' learning in the cortex and 'fast' learning in the hippocampus could protect previous knowledge from interference, this has never been observed in the living organism. Here, we report that increasing plasticity via the viral-induced overexpression of RGS14414 in the prelimbic cortex leads to better one-trial memory, but that this comes at the price of increased interference in semantic-like memory. Indeed, electrophysiological recordings showed that this manipulation also resulted in shorter NonREM-sleep bouts, smaller delta-waves and decreased neuronal firing rates. In contrast, hippocampal-cortical interactions in form of theta coherence during wake and REM-sleep as well as oscillatory coupling during NonREM-sleep were enhanced. Thus, we provide the first experimental evidence for the long-standing and unproven fundamental idea that high thresholds for plasticity in the cortex protect preexisting memories and modulating these thresholds affects both memory encoding and consolidation mechanisms.

Sleep deprivation and hippocampal ripple disruption after one-session learning eliminate memory expression the next day.

Memory reactivation during non-rapid-eye-movement ripples is thought to communicate new information to a systems-wide network and thus can be a key player mediating the positive effect of sleep on memory consolidation. Causal experiments disrupting ripples have only been performed in multiday training paradigms, which decrease but do not eliminate memory performance, and no comparison with sleep deprivation has been made. To enable such investigations, we developed a one-session learning paradigm in a Plusmaze and show that disruption of either sleep with gentle handling or hippocampal ripples with electrical stimulation impaired long-term memory. Furthermore, we detected hippocampal ripples and parietal high-frequency oscillations after different behaviors, and a bimodal frequency distribution in the cortical events was observed. Faster cortical high-frequency oscillations increased after normal learning, a change not seen in the hippocampal ripple-disruption condition, consistent with these having a role in memory consolidation.

Disrupting ripples: Methods, results, and caveats in closed-loop approaches in rodents.

Hippocampal ripple oscillations have been associated with memory reactivations during wake and sleep. These reactivations should contribute to working memory and memory consolidation respectively. In the past decade studies have moved from being observational to actively disrupting ripple-related activity in closed-loop approaches to enable causal investigations into their function. All together these studies have been able to provide evidence that wake, task-related ripple activity is important for working memory and planning but less important for stabilisation of spatial representations. Rest and sleep-related ripple activity, in contrast, is important for long-term memory performance and thus memory consolidation. In this review, we summarise results from different closed-loop approaches in rodents. Further, we highlight differences in detection and stimulation methods as well as controls and discuss how these differences could influence outcomes.

Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?

Control of human-machine interfaces are well modeled by computational control models, which take into account the behavioral decisions people make in estimating task dynamics and state for a given control law. This control law is optimized according to a cost function, which for the sake of mathematical tractability is typically represented as a series of quadratic terms. Recent studies have found that people actually use cost functions for reaching tasks that are slightly different than a quadratic function, but it is unclear which of several cost functions best explain human behavior and if these cost functions generalize across tasks of similar nature but different scale. In this study, we used an inverse-decision-theory technique to reconstruct the cost function from empirical data collected on 24 able-bodied subjects controlling a myoelectric interface. Compared with previous studies, this experimental paradigm involved a different control source (myoelectric control, which has inherently large multiplicative noise), a different control interface (control signal was mapped to cursor velocity), and a different task (the tracking position dynamically moved on the screen throughout each trial). Several cost functions, including a linear-quadratic; an inverted Gaussian, and a power function, accurately described the behavior of subjects throughout this experiment better than a quadratic cost function or other explored candidate cost functions (p<0.05). Importantly, despite the differences in the experimental paradigm and a substantially larger scale of error, we found only one candidate cost function whose parameter was consistent with the previous studies: a power function (cost ∝ errorα) with a parameter value of α = 1.69 (1.53-1.78 interquartile range). This result suggests that a power-function is a representative function of user's error cost over a range of noise amplitudes for pointing and tracking tasks.