Deep learning algorithms reveal increased social activity in rats at the onset of the dark phase of the light/dark cycle

Abstract

The rapid decrease of light intensity is a potent stimulus of rats' activity. The nature of this activity, including the character of social behavior and the composition of concomitant ultrasonic vocalizations (USVs), is unknown. Using deep learning algorithms, this study aimed to examine the social life of rat pairs kept in semi-natural conditions and observed during the transitions between light and dark, as well as between dark and light periods. Over six days, animals were video- and audio-recorded during the transition sessions, each starting 10 minutes before and ending 10 minutes after light change. The videos were used to train and apply the DeepLabCut neural network examining animals' movement in space and time. DeepLabCut data were subjected to the Simple Behavioral Analysis (SimBA) toolkit to build models of 11 distinct social and non-social behaviors. DeepSqueak toolkit was used to examine USVs. Deep learning algorithms revealed lights-off-induced increases in fighting, mounting, crawling, and rearing behaviors, as well as 22-kHz alarm calls and 50-kHz flat and short, but not frequency-modulated calls. In contrast, the lights-on stimulus increased general activity, adjacent lying (huddling), anogenital sniffing, and rearing behaviors. The animals adapted to the housing conditions by showing decreased ultrasonic calls as well as grooming and rearing behaviors, but not fighting. The present study shows a lights-off-induced increase in aggressive behavior but fails to demonstrate an increase in a positive affect defined by hedonic USVs. We further confirm and extend the utility of deep learning algorithms in analyzing rat social behavior and ultrasonic vocalizations.