Recently dogs (Canis familiaris) have been demonstrated to be a promising model species for studying human behavior as they have adapted to the human niche and developed human-like socio-cognitive skills. Research on dog behavior, however, has so far almost exclusively focused on awake functioning.Here we present a self-developed non-invasive easily replicable canine polysomnography method. N=22 adult pet dogs (with their owners present) and N=12 adult humans participated in Study I. From these subjects N=7 dogs returned on two more occasions for Study II.In Study I. we give a descriptive analysis of the sleep electroencephalogram of the dog and compare it to human data. In order to validate our canine polysomnography method in Study II. we compare the sleep macrostructure and the EEG spectrum of dogs after a behaviorally active versus passive day.In Study I. we found that dogsʼ sleep EEG resembled that of human subjects and was generally in accordance with previous literature using invasive technology. In Study II. we show that similarly to previous results on humans daytime load of novel experiences affects the macrostructural and spectral aspects of subsequent sleep.Our results validate the family dog as a model species for studying the effects of pre-sleep activities on the EEG pattern under natural conditions and thus broaden the perspectives of the rapidly growing fields of canine cognition and sleep research.

The active role of sleep in memory consolidation is still debated, and due to a large between-species variation, the investigation of a wide range of different animal species (besides humans and laboratory rodents) is necessary. The present study applied a fully non-invasive methodology to study sleep and memory in domestic dogs, a species proven to be a good model of human awake behaviours. Polysomnography recordings performed following a command learning task provide evidence that learning has an effect on dogs’ sleep EEG spectrum. Furthermore, spectral features of the EEG were related to post-sleep performance improvement. Testing an additional group of dogs in the command learning task revealed that sleep or awake activity during the retention interval has both short- and long-term effects. This is the first evidence to show that dogs’ human-analogue social learning skills might be related to sleep-dependent memory consolidation.

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

Dogs (Canis familiaris) are excellent models of human behavior as during domestication they have adapted to the same environment as humans. There have been many comparative studies on dog behavior; however, several easily measurable and analyzable psychophysiological variables that are widely used in humans are still largely unexplored in dogs. One such measure is rapid eye movement density (REMD) during REM sleep. The aim of this study was to test the viability of measuring REMD in dogs and to explore the relationship between the REMD and different variables (sex, age, body size, and REM sleep duration). Fifty family dogs of different breeds and ages (from 6 months to 15 years old) participated in a 3-h non-invasive polysomnography recording, and the data for 31 of them could be analyzed. The signal of the electro-oculogram (EOG) was used to detect the rapid eye movements during REM sleep, and REMD was calculated based on these data. The duration of REM sleep had a quadratic effect on REMD. Subjects' REMD increased with age, but only in male dogs with short REM sleep duration. Furthermore, in the case of dogs with short REM sleep, the interaction of body mass and REM sleep duration had a significant effect on REMD. No such effects were found in dogs with long REM duration. These results suggest that relationships may exist between REMD and several different variables. Keywords Rapid eye movement density. REMD. REM sleep. Dogs Most living creatures (from molluscs to mammals) spend a significant proportion of their time asleep despite its high costs (decreased sensitivity to environmental stimuli, inability to feed or breed, etc.) (Campbell & Tobler, 1984). Furthermore, sleep has been shown to relate to individual characteristics (e.g., personality: Gray & Watson, 2002) and waking behavior (e.g., pre-sleep learning: Wuyts et al., 2012). However, it is still not known how sleep evolved, and what the exact functions of sleep are (Krueger et al., 2016).

Although a positive link between sleep spindle occurrence and measures of post-sleep recall (learning success) is often reported for humans and replicated across species, the test–retest reliability of the effect is sometimes questioned. The largest to date study could not confirm the association, however methods for automatic spindle detection diverge in their estimates and vary between studies. Here we report that in dogs using the same detection method across different learning tasks is associated with observing a positive association between sleep spindle density (spindles/minute) and learning success. Our results suggest that reducing measurement error by averaging across measurements of density and learning can increase the visibility of this effect, implying that trait density (estimated through averaged occurrence) is a more reliable predictor of cognitive performance than estimates based on single measures.