3-4-methylenedioxymethamphetamine (MDMA) increases self-reported positive social feelings and decreases the ability to detect social threat in faces, but its effects on experiences of social acceptance and rejection have not been determined. We examined how an acute dose of MDMA affects subjective and autonomic responses to simulated social acceptance and rejection. We predicted that MDMA would decrease subjective responses to rejection. On an exploratory basis, we also examined the effect of MDMA on respiratory sinus arrhythmia (RSA), a measure of parasympathetic cardiac control often thought to index social engagement and emotional regulation. Over three sessions, healthy adult volunteers with previous MDMA experience (N = 36) received capsules containing placebo, 0.75 or 1.5 mg/kg of MDMA under counter-balanced double-blind conditions. During expected peak drug effect, participants played two rounds of a virtual social simulation task called “Cyberball” during which they experienced acceptance in one round and rejection in the other. During the task we also obtained electrocardiograms (ECGs), from which we calculated RSA. After each round, participants answered questionnaires about their mood and self-esteem. As predicted, MDMA decreased the effect of simulated social rejection on self-reported mood and self-esteem and decreased perceived intensity of rejection, measured as the percent of ball tosses participants reported receiving. Consistent with its sympathomimetic properties, MDMA decreased RSA as compared to placebo. Our finding that MDMA decreases perceptions of rejection in simulated social situations extends previous results indicating that MDMA reduces perception of social threat in faces. Together these findings suggest a cognitive mechanism by which MDMA might produce pro-social behavior and feelings and how the drug might function as an adjunct to psychotherapy. These phenomena merit further study in non-simulated social environments.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring.

We investigate how the population nonlinearities resulting from lateral inhibition and thresholding in sparse coding networks influence neural response selectivity and robustness. We show that when compared to pointwise nonlinear models, such population nonlinearities improve the selectivity to a preferred stimulus and protect against adversarial perturbations of the input. These findings are predicted from the geometry of the single-neuron iso-response surface, which provides new insight into the relationship between selectivity and adversarial robustness. Inhibitory lateral connections curve the iso-response surface outward in the direction of selectivity. Since adversarial perturbations are orthogonal to the iso-response surface, adversarial attacks tend to be aligned with directions of selectivity. Consequently, the network is less easily fooled by perceptually irrelevant perturbations to the input. Together, these findings point to benefits of integrating computational principles found in biological vision systems into artificial neural networks.