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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.
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Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
The interrelated eect of sleep and
learning in dogs (Canis familiaris);
an EEG and behavioural study
Anna Kis1, Sára Szakadát2, Márta Gácsi3,4, Enikő Kovács5, Péter Simor6, Csenge Török1,7,
Ferenc Gombos8, Róbert Bódizs2,8 & József Topál1
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 dierent 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 eect 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 eects. This is the rst evidence to show that dogs’ human-analogue social learning skills
might be related to sleep-dependent memory consolidation.
Sleep is a fundamental, but compared to the awake processes oen neglected, behavioural state present in almost
all vertebrate species1. Despite the intertwined nature of sleep and awake states2, and the widely accepted notion
that sleep has a vital function, there is still no general, unifying and quantitative theory of sleep, which explains
the origins, features, mechanisms and functions in a detailed model3. One of the most studied, and yet debated4
functions of sleep is memory consolidation5 but evidence for this theory comes exclusively from human and
laboratory rodent data, except for some results on arthropods6. Variation exists in the nature and the amount of
sleep found in non-human species, and these variations suggest that functions of sleep may dier across species2,
calling for the integration of human and laboratory rodent research into a wider set of results from dierent ani-
mal species7. In an eort to widen the framework to study both the general features and functions of vertebrate
sleep8, here we investigate the relationship between sleep and memory in domestic dogs. Although extensive
research has been carried out on dogs’ sleep EEG with ‘traditional’ invasive methods9–12, which mostly focused on
neurological conditions such as epilepsy13,14 and narcolepsy15, this species has not been used previously to study
the function of sleep in a way directly comparable to that of human studies. Dogs are one of the most interesting
model species in comparative cognition research due their human-analogue social skills16,17 and their approxi-
mately 18–32 thousand years of domestication history18, during which they have adapted in evolutionary terms
to the same environmental challenges as humans.
A non-invasive canine polysomnography method was developed for dogs19, and used here to investigate the
dierences in sleep EEG spectrum following a command learning (CL), and a non-learning (NL) task, respec-
tively. Fieen dogs participated in two polysomnography recordings (3-hour-long each), that immediately fol-
lowed either CL, during which they had to associate unknown commands (unfamiliar words) to already known
actions (sit and lie down), or NL, during which they were required to perform the same two actions aer the
usual (known) commands, in the very same way as in the CL task (see Experimental Procedures). Aer an initial
adaptation session (where the polysomnography recording was not preceded by behavioural pre-treatment), dogs
1Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences, Budapest, Hungary. 2Institute
of Behavioural Sciences, Semmelweis University, Budapest, Hungary. 3MTA-ELTE Comparative Ethology Research
Group, Budapest, Hungary. 4Department of Ethology, Eötvös Loránd University, Budapest, Hungary. 5Department
of Ecology Faculty of Veterinary Sciences, Szent István University, Budapest, Hungary. 6Department of Cognitive
Science, Budapest University of Technology and Economics, Budapest, Hungary. 7Institute of Psychology, Eötvös
Loránd University, Budapest, Hungary. 8Department of General Psychology, Pázmány Péter Catholic University,
Budapest, Hungary. Correspondence and requests for materials should be addressed to A.K. (email: vargane.kis.
Received: 13 October 2016
Accepted: 30 December 2016
Published: 06 February 2017
Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
participated in both CL and NL conditions on two subsequent days in a counterbalanced order. Polysomnography
recordings aer the CL condition were followed by a post-sleep re-test session with the newly learned commands,
in order to asses any change in the dogs’ performance, and its relation to sleep EEG spectrum. Importantly, this
task allowed for the investigation of reward-related memory processing20, while current evidence for memory
consolidation in non-human species mainly comes from aversive conditioning.
Results and Discussion
The eect of learning on sleep physiology. e relative EEG spectrum (proportion of total power)
was rst calculated for 4 Hz frequency ranges. is showed a redistribution of EEG power in a way that Non-
REM sleep delta (1–4 Hz) activity increased (t(14) = 2.943, p = 0.011), while alpha (8–12 Hz) activity decreased
(t(14) = 2.225, p = 0.043), after the learning task. The decrease in theta (4–8 Hz) activity was not significant
(t(14) = 1.926, p = 0.075), and no dierence was found in beta (12–30 Hz) activity (t(14) = 1.311, p = 0.211). e
bin-by-bin (0.25 Hz resolution) analysis revealed that the relative delta activity increase occurred in the 1–1.5
and 2.75–3.25 Hz frequency ranges. ere was a signicant relative decrease in the 5–5.75 Hz (theta) range and in
the 7–10.25 Hz (alpha) range (Fig.1i). During REM sleep relative theta (4–8 Hz) activity increased aer learning
(t(10) = 3.130, p = 0.011), while the relative decrease in delta (1–4 Hz) activity was not signicant (t(10) = 1.898,
p = 0.087). No eect of learning on REM sleep EEG alpha (8–12 Hz; t(10) = 0.539, p = 0.602), or beta (12–30 Hz;
t(10) = 1.305, p = 0.221) activity was found. According to the bin-by-bin analysis, there was a signicant relative
decrease in the 1.5–2 Hz (delta) frequency range aer learning during REM sleep, while the relative increase in
the 3.5–4 Hz (delta) frequency did not remain signicant aer correction for multiple comparisons. No signicant
bin-wise dierences were found in the theta, alpha and beta ranges during REM sleep (Fig.1ii). Spectral changes
during Non-REM and REM sleep (when examining the dierence between CL and NL conditions), were found to
be related to each other in the theta range (pooled data, 4–8 Hz; r = 0.613, p = 0.045), but no such relationship
was found for the other ranges (delta, alpha, beta; all p > 0.1). Within both sleep stages the change in slow activity
(delta, 1–4 Hz), was negatively related to the change in fast activity (Non-REM alpha: r = 0.890, p < 0.001;
beta: r = 0.730, p = 0.002; REM beta: r = 0.793, p = 0.004). Learning did not aect sleep macrostructure (see
Supplemental Results), contrary to our expectations, but in line with some human studies, where similarly to our
ndings no dierences were found between learning and non-learning conditions, regarding the time spent in
dierent sleep stages21.
Behavioural data showed that subjects’ performance signicantly increased aer the 3-hour-long polysom-
nography recording compared to the pre-sleep baseline (t(14) = 3.833, p = 0.002), although the performance
increase was not related to sleep duration or any of the macrostructural variables (see Supplemental Results).
However, evidence was found for a correlation between performance improvement and relative EEG spec-
trum power. Decreased REM sleep delta (1–4 Hz) activity (Pearson correlation; r = 0.683, p = 0.01), as well as
increased REM sleep beta (12–30 Hz) activity (r = 0.536, p = 0.05), were related to higher performance (Fig.2).
Figure 1. Relative power spectra (proportion of total power) for (i). Non-REM and (ii). REM sleep, following
the command learning and the non-learning task. Bin-by-bin data (mean ± SE for the N = 15 participating
dogs) are shown on a logarithmic scale for both Non-REM and REM sleep.
Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
ere was no signicant correlation of performance improvement with theta or alpha activity during REM sleep,
or with any of the frequency ranges during Non-REM sleep.
ese results provide the rst evidence that learning new commands inuences sleep EEG spectrum in dogs,
and that the EEG spectrum during sleep is predictive of memory performance. Although “memory” is oen
used as a unitary term in the literature, it is not a single entity, and while in the case of humans there is a widely
accepted distinction between declarative and non-declarative memory, we know little about how learning in
non-human species ts into these categories. Our results suggest that command learning in dogs inuences both
REM and non-REM sleep, with the former being traditionally associated with non-declarative and the latter with
declarative memory consolidation22. During non-REM sleep an increased delta power was found aer learning,
which is consistent with human data23,24.
eta activity is typically thought to be implicated in many aspects of memory processing and consolidation,
mostly due to the neuronal re-play of memories in the hippocampus during REM sleep25, but the direction of this
relationship is controversial (e.g. in humans, learning of word pairs was reported to enhance theta activity during
REM sleep26, however, mice exhibited reduced REM sleep theta activity aer fear conditioning27). e present
study also provided inconsistent results in the case of dogs, with some indications for increased theta activity dur-
ing REM sleep aer learning, and also reduced theta activity during non-REM sleep. However, these two changes
were found to be functionally related, that is in line with the predictions of the two-stage model suggesting that
subsequent occurrence of non-REM and REM sleep is essential for memory consolidation28. A decrease in alpha
activity during non-REM sleep was also found, which together with the fact that alpha activity was negatively
related to slow wave activity, might signal an increase in sleep depth aer learning29.
The eect of sleep and awake activity on learning. Having demonstrated learning-induced changes in
sleep EEG spectrum and a relationship between sleep and memory formation in dogs, in the second experiment
we aimed to test how post-learning activities (sleep or awake) inuenced memory consolidation. A group of
task-naïve adult pet dogs (n = 53) participated in the previously described command learning task (CL), during
which their learning performance (Baseline) was assessed (see Experimental Procedures). Aer this, subjects
were randomly assigned to four short (1 h) retention interval conditions (RIC) (n = 12–14/group). ese either
included sleeping, or one of three awake activities of varying physical and mental intensity: on-leash walk (phys-
ical activity with minimal cognitive interference), learning an unrelated task (low physical activity with high
cognitive interference), playing with a dog toy Kong® while lying on the oor (minimal physical activity, high
emotional arousal). Subjects’ performance in response to previously known commands was also assessed in order
to control for obedience.
Subjects in the four conditions did not dier in obedience (F(3) = 0.799, p = 0.512), nor in baseline learn-
ing performance (F(3) = 1.812, p = 0.157). Subjects were retested on the newly learned commands immediately
aer the retention interval (Retest), and aer one week (Long-term), in order to assess short- and long-term
memory eects of the dierent RICs. A Generalized Linear Mixed Model (Poisson Log; Table1) showed that, as
expected, performance was inuenced by the interaction of test occasion (Baseline, Retest, Long-term) × RIC
(χ2(4) = 14.435, p = 0.006), suggesting that dierential learning patterns emerged as a consequence of the dierent
activities following the initial learning task (Fig.3).
Subjects’ obedience also influenced their performance in interaction with the other two factors
(Occasion × RIC × Obedience: χ2(4) = 16.332, p = 0.003; RIC × Obedience: χ2(2) = 9.037, p = 0.011; Fig.S1). e
eect of RIC was also signicant as a main eect (χ2(2) = 8.020, p = 0.018), but the main eect of test occasion did
Figure 2. Relationship between performance improvement (the relative dierence between pre-sleep and
post-sleep performance) in the learning task, and relative delta power (le) as well as beta power (right)
during post-learning REM sleep.
Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
not reach signicance (χ2(2) = 5.860, p = 0.053). e main eect of obedience (χ2(1) = 0.770, p = 0.380) as well as
its interaction with test occasion (Occasion × Obedience: χ2(2) = 2.300, p = 0.317) were also non-signicant. e
pairwise post hoc analysis revealed that in the Sleep condition, despite a tendency towards performance improve-
ment, there was no dierence between the post-sleep retest and the baseline (p > 0.05). is result seemingly con-
tradicts the ndings of our polysomnography study (see Exp. 1 above), where dogs’ performance increased aer
3 hours of sleep, but can probably be attributed to the dierence in the length of the retention interval (3 hours vs.
1 hour), as longer sleep durations have been found to yield greater memory improvements in humans30. Future
studies should determine the optimal amount of sleep needed to benet memory and how this might generalize
across species.
However, subjects in the Sleep condition did improve in the long run; they performed better when tested on
the Long-term occasion compared to both Baseline (p < 0.001) and Retest (p < 0.001). is suggests that memory
consolidation aer learning occurred during the subjects’ usual night-sleep at home. is is in line with previous
ndings showing that in the absence of interfering learning experience, sleep does not need to occur immediately
aer learning for memory consolidation to take place31 but should happen on the same day as the initial training32.
Subjects in the Walk condition showed the same learning pattern: there was no dierence between Baseline and
post-walk Retest (p > 0.05), but the Long-term performance was signicantly higher (compared to both Baseline:
p < 0.001; and Retest: p < 0.01). is suggests that being awake per se does not interfere with long-term memory
formation in dogs. Similar claims have been made for humans33, suggesting that slow EEG oscillations during
non-sleep resting state activity (mind-wandering) also facilitates memory consolidation.
Dogs that learned an unrelated task during the retention interval (Learning condition), not only remained
at their baseline performance on the Retest occasion (p > 0.05), but also did not improve aer a week (Baseline
vs. Long-term: p > 0.05), suggesting that an interfering learning experience impedes memory consolidation for
the previously learned information. In the Play condition subjects’ performance decreased at Retest compared
to Baseline (p < 0.001), which is indicative of emotional arousal having a deteriorative eect. However, subjects
in this condition also performed better on the Long-term occasion compared to both Baseline (p < 0.001) and
Retest (p < 0.001), suggesting that these subjects also beneted from the at-home night sleep aer learning, and
that play did not interfere with memory consolidation, but impacted on other domains (e.g. attention), which are
necessary for performance during re-test.
e results of these two studies provide the rst evidence of the interrelated eect of sleep and learning in
dogs, suggesting that a sleep-dependent memory consolidation takes place in this species. Further studies should
RIC Obedience
Test occasion
Baseline Retest Long-term
Sleep 83.73 ± 3.88 57.54 ± 3.33 59.52 ± 4.14 67.77 ± 3.52
Walk 85.32 ± 4.18 49.21 ± 4.03 54.37 ± 3.91 61.11 ± 3.53
Learn 78.24 ± 4.61 55.93 ± 2.60 51.85 ± 4.40 56.48 ± 4.58
Play 74.24 ± 5.31 48.99 ± 4.29 43.94 ± 8.46 63.13 ± 5.72
Table 1. Mean ± SE performance (% of correct trials) of subjects in the dierent retention interval
conditions (RICs). Obedience, Baseline, Retest and Long-term performances are given as the percentage of
correct responses in each of the 18-trial sessions.
Figure 3. e dierential learning patterns in the four retention interval conditions are revealed in
subjects’ performance change (mean ± SE) at the Retest and Long-term occasions compared to Baseline.
Values >0 indicate a performance improvement at the given occasion, while values <0 indicate a decreased
Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
determine if sleep and memory in dogs is similarly modulated by individual variation, as in the case of humans.
For example if age-related changes in sleep-wake pattern12, EEG spectrum19 and memory function34 lead to mem-
ory consolidation dierences in old dogs. Functional analogies in awake functioning between dogs and humans
have already been proposed both at the behavioural35 and neural36 level. Our results open up the possibility that
dogs’ human-analogue social learning skills might be related to sleep-dependent memory consolidation.
Ethic statement. Research was carried out in accordance with the Hungarian regulations on animal exper-
imentation and the Guidelines for the use of animals in research described by the Association for the Study
Animal Behaviour (ASAB). e Hungarian “Animal Experiments Scientic and Ethical Committee” issued a
statement (under the number PE/EA/853–2/2016), approving our experimental protocol by categorizing it as a
non-invasive study that causes less pain or suering than the equivalent of inserting a needle. All owners volun-
teered to participate in the study.
The eect of learning on sleep physiology. Subjects (N = 15 adult pet dogs, mean age ± SD: 3.67 ± 1.91;
8 males, 7 females; from 9 breeds and 3 mixed breeds), participated in 3-hour-long polysomnography recordings
(according to the protocol described in ref. 19), for a total of three occasions (see TableS1). e rst occasion
was a 3-hour-long adaptation sleep, followed by a command learning (CL) and a non-learning (NL) occasion in
a counterbalanced order (on three dierent days). In CL dogs were taught to perform two already known actions
(sit and lie down), using unfamiliar commands (English phrases instead of the familiar Hungarian ones). e
training procedure followed a standardized schedule and was concluded with an 18-trial baseline test session
(for details see Supplemental Experimental Procedures). In the NL task dogs had to execute the same sequence
of “Sit!” and “Lie down!” actions, but the experimenter always used the familiar commands (i.e. the Hungarian
phrases for sitting and lying down), accompanied by the familiar hand signals (see Supplemental Experimental
Procedures for details). Both the CL and NL tasks were followed by a 3-hour-long polysomnography recording.
In the CL occasion, the polysomnography recording was followed by an 18-trial session where the dog had to
execute the previously learned English commands (Retest).
Sleep recordings were visually scored according to standard criteria19 in 20 s epochs. Artefact rejection was
carried out manually on 4 s epochs before further automatic analyses on all recordings. Average power spectral
densities (1 Hz to 30 Hz) were calculated by a mixed-radix Fast Fourier Transformation (FFT) algorithm, applied
to the 50% overlapping, Hanning-tapered 4 sec windows of the EEG signal of the Fz-Cz derivation. Relative power
spectra were calculated separately for Non-REM and REM sleep for both the CL and NL occasions as proportion
of total (1–30 Hz) power. e two conditions were compared with regard to the four frequency ranges of delta
(1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (12–30 Hz), and additionally a bin-by-bin analysis was carried
out on the full (1–30 Hz) spectrum with 0.25 Hz resolution.
Behavioural data was obtained from the learning task; the percent of correct actions was calculated for both
the Baseline and the Retest sessions (18 trials each). e dierence between the re-test and test sessions (improve-
ment during sleep), was correlated with the relative spectrum in the four frequency ranges of delta (1–4 Hz), theta
(4–8 Hz), alpha (8–12 Hz) and beta (12–30 Hz), for both Non-REM and REM sleep.
The eect of sleep and awake activity on learning. Subjects (N = 53 adult pet dogs, mean age ± SD:
3.89 ± 2.59; 22 males, 31 females; from 21 breeds and 25 mixed breeds) participated in the command learning
task (CL) described in Exp. 1. e CL was concluded with a 18-trial Baseline test session and followed by a
1-hour-long retention interval (RI) during which dogs participated in one of the following activities according to
the condition they were quasi-randomly allocated: (1) sleeping in their owners’ parked car (N = 14); (2) walking
around the university campus on leash (N = 14); (3) learning new commands with the owner in 10–minute-long
sessions (N = 12); (4) playing with a Kong® (N = 13). Aer the RI, dogs participated in an 18-trial Retest ses-
sion as well as an 18-trial Obedience session with the known Hungarian commands. Approximately one week
(mean ± SE: 7.64 ± 0.43 days) aer the rst occasion, dogs returned for another session of 18 trials to assess their
long-term memory (Long-term; TableS2).
e percentage of correct actions was coded for the Baseline, Retest, Obedience and Long-term sessions
respectively. A Generalized Linear Model (Poisson loglinear) was run with performance as the dependent varia-
ble, Occasion (Baseline, Retest, Longterm) and RI condition (Sleep, Walk, Learn, Play) as factors and Obedience
as covariate.
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e study was supported by NestléPurina, the Hungarian Scientic Research Fund (OTKA K112138; K115862),
the Hungarian Academy of Sciences (F01/031) and János Bolyai Research Scholarship (to PS). We thank Ádám
Miklósi for his support and Lisa Wallis for correcting the English of the manuscript.
Author Contributions
Conceptualization: A.K., S.S., M.G., P.S., R.B. and J.T.; Methodology: A.K., S.S., M.G. and R.B.; Soware for data
analysis: F.G.; Investigation: A.K., S.S., E.K. and C.T.; Formal analysis: A.K., S.S., E.K. and C.T.; Resources: R.B.
and J.T.; Writing original dra: A.K.; Writing – Review & Editing: all authors; Visualization: A.K.; Supervision:
M.G., P.S., R.B. and J.T.; Project Administration: A.K. and S.S.; Funding Acquisition: A.K., M.G., R.B. and J.T.
Additional Information
Supplementary information accompanies this paper at
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Kis, A. et al. e interrelated eect of sleep and learning in dogs (Canis familiaris); an
EEG and behavioural study. Sci. Rep. 7, 41873; doi: 10.1038/srep41873 (2017).
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Supplementary resource (1)

... The practice of breeding for particular characteristics in dogs has ancient origins and is responsible for the extraordinary phenotypic variation among modern breeds (134,140,203,204). Because many working dog organizations maintain (and share) dedicated breeding populations, there is great potential for continual improvements in these populations through selective breeding. ...
... Preliminary research suggests that engaging a dog in activities that likely induce "pleasant arousal, " such as walking and play, directly after learning a new task has positive effects on their memory for that task when tested again 24 h (202), 1 week (203), or even up to 1 year (204) later. In contrast, having them immediately engage in learning of an unrelated task results in cognitive interference, thereby disrupting memory consolidation (203). Interference in memory tasks also seems to be critical for odor memory in dogs (205). ...
... Sleep appears to be another crucial variable (206). In dogs specifically, performance in learning new commands has been shown to be enhanced by sleep-related improvement in memory consolidation (203,207,208). Given that command learning is an integral part of working dog training, these findings, along with an emerging literature on the environmental factors that affect quality and quantity of sleep (209), are of great relevance. ...
Full-text available
During two retreats in 2017 and 2020, a group of international scientists convened to explore the Human-Animal Bond. The meetings, hosted by the Wallis Annenberg PetSpace Leadership Institute, took a broad view of the human-dog relationship and how interactions between the two may benefit us medically, psychologically or through their service as working dogs (e.g. guide dogs, explosive detection, search and rescue, cancer detection). This Frontiers’ Special Topic has collated the presentations into a broad collection of 14 theoretical and review papers summarizing the latest research and practice in the historical development of our deepening bond with dogs, the physiological and psychological changes that occur during human-dog interactions (to both humans and dogs) as well as the selection, training and welfare of companion animals and working dogs. The overarching goals of this collection are to contribute to the current standard of understanding of human-animal interaction, suggest future directions in applied research, and to consider the interdisciplinary societal implications of the findings.
... These include the above-discussed and other functional behavioural analogies between dogs and humans (for a review see: [10], dogs' cooperativeness, trainability [11] and a recent advance in noninvasive neuroscientific research methodologies in dogs, for example, functional magnetic resonance imaging (fMRI) [12,13], polysomnography (e.g. [14][15][16][17] and event-related potentials (ERPs) [18,19]). However, behavioural analogies do not necessarily mean the same underlying neurocognitive processes (e.g. ...
... However, behavioural analogies do not necessarily mean the same underlying neurocognitive processes (e.g. [12,15,16]), thus investigating the neural processes in parallel to behavioural observations is most certainly needful (e.g. [20]). ...
... The electrophysiological recordings were carried out according to the completely non-invasive polysomnography method developed and validated by Kis et al. [14] and applied in many studies since (e.g. [15,44,45]). According to the procedure, we recorded the EEG (including electrodes next to the eyes, used as eletrooculogram (EOG); mainly for detecting artefactual muscle movements), electrocardiogram and the respiratory signal of dogs, but only used the EEG signal in these analyses. ...
Full-text available
Recent advances in the field of canine neuro-cognition allow for the non-invasive research of brain mechanisms in family dogs. Considering the striking similarities between dog's and human (infant)'s socio-cognition at the behavioural level, both similarities and differences in neural background can be of particular relevance. The current study investigates brain responses of n = 17 family dogs to human and conspecific emotional vocalizations using a fully non-invasive event-related potential (ERP) paradigm. We found that similarly to humans, dogs show a differential ERP response depending on the species of the caller, demonstrated by a more positive ERP response to human vocalizations compared to dog vocalizations in a time window between 250 and 650 ms after stimulus onset. A later time window between 800 and 900 ms also revealed a valence-sensitive ERP response in interaction with the species of the caller. Our results are, to our knowledge, the first ERP evidence to show the species sensitivity of vocal neural processing in dogs along with indications of valence sensitive processes in later post-stimulus time periods.
... The latter was derived from an explorative, visual inspection of the EEG transients. The data set that we used was from Kis et al. 4 from an experiment specifically designed to test the contributions of sleep to memory consolidation. ...
... Subjects and Behavioural paradigm (adapted from Kis et al. 4 . 15 adult pet dogs, mean age ± SD: ...
... Polysomnographic method. The specifics of the polysomnographic method are detailed in previous publications 3,4 . The signal for our EEG analyses comes from a frontal electrode placed on the anterior of the skull midline (Fz) which was corrected (differential recording) for activity from a second recording electrode placed centrally along the same axis (Fz-Cz). ...
These proceedings contain oral and poster presentations from various experts on animal behaviour and animal welfare in veterinary medicine presented at the conference.
... For the present study, we assumed that age-related differences in the sleep architecture of dogs are generally similar to corresponding differences in other mammals. To test this, we analysed age-related differences in sleep macrostructure (drowsiness, NREM and REM) and spectral power (delta 1-4 Hz, theta 4-8 Hz, alpha 8-12 Hz, sigma 12-16 Hz, beta [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] variables. Weight was also included in our analyses. ...
... This consisted of 50 purebreds from 25 different breeds and 10 mongrels; 32 females (Mean age = 8, SD = 4.0; of the 32 females, 15 were neutered and for 1, there was no such information available) and 28 males (Mean age = 7.7, SD = 4.2; out of the 28 male dogs 3 were neutered). Second, to assess whether sleep parameters change beyond the age of 14 months, we extended our sample (and thus, the age range thereof) by adding data from our previous dog sleep EEG studies 7,[26][27][28][29] , so that the total sample consisted of 91 family dogs (Extended sample encompassing the Juvenile sample + n = 31, > 14 month-old dogs) with an age range of 2-30 months. This sample consisted of 75 purebreds from 30 different breeds and 16 mongrels; 48 females (Mean age = 12.9, SD = 8.3; of the 48 females, 26 were neutered, for 3, there was no such information available, and the remaining 19 were not in heat at the time of the sleep recording) and 43 males (Mean age = 12.8, SD = 8.1; of the 43 males, 10 were neutered and for 1, there was no such information available). ...
Full-text available
Age-related differences in dog sleep and the age at which dogs reach adulthood as indexed by sleep electrophysiology are unknown. We assessed, in (1) a Juvenile sample (n = 60) of 2–14-month-old dogs (weight range: 4–68 kg), associations between age, sleep macrostructure, and non-rapid eye movement (NREM) EEG power spectrum, whether weight moderates associations, and (2) an extended sample (n = 91) of 2–30-months-old dogs, when sleep parameters stabilise. In Juvenile dogs, age was positively associated with time in drowsiness between 2 and 8 months, and negatively with time in rapid eye movement (REM) sleep between 2 and 6 months. Age was negatively associated with delta and positively with theta and alpha power activity, between 8 and 14 months. Older dogs exhibited greater sigma and beta power activity. Larger, > 8-month-old dogs had less delta and more alpha and beta activity. In extended sample, descriptive data suggest age-related power spectrum differences do not stabilise by 14 months. Drowsiness, REM, and delta power findings are consistent with prior results. Sleep electrophysiology is a promising index of dog neurodevelopment; some parameters stabilise in adolescence and some later than one year. Determination of the effect of weight and timing of power spectrum stabilisation needs further inquiry. The dog central nervous system is not fully mature by 12 months of age.
... Preliminary research suggests that engaging a dog in activities that likely induce "pleasant arousal, " such as walking and play, directly after learning a new task has positive effects on their memory for that task when tested again 24 h (202), 1 week (203), or even up to 1 year (204) later. In contrast, having them immediately engage in learning of an unrelated task results in cognitive interference, thereby disrupting memory consolidation (203). Interference in memory tasks also seems to be critical for odor memory in dogs (205). ...
... Sleep appears to be another crucial variable (206). In dogs specifically, performance in learning new commands has been shown to be enhanced by sleep-related improvement in memory consolidation (203,207,208). Given that command learning is an integral part of working dog training, these findings, along with an emerging literature on the environmental factors that affect quality and quantity of sleep (209), are of great relevance. ...
Full-text available
Dogs are trained for a variety of working roles including assistance, protection, and detection work. Many canine working roles, in their modern iterations, were developed at the turn of the 20th century and training practices have since largely been passed down from trainer to trainer. In parallel, research in psychology has advanced our understanding of animal behavior, and specifically canine learning and cognition, over the last 20 years; however, this field has had little focus or practical impact on working dog training. The aims of this narrative review are to (1) orient the reader to key advances in animal behavior that we view as having important implications for working dog training, (2) highlight where such information is already implemented, and (3) indicate areas for future collaborative research bridging the gap between research and practice. Through a selective review of research on canine learning and behavior and training of working dogs, we hope to combine advances from scientists and practitioners to lead to better, more targeted, and functional research for working dogs.
... Our methodology has proved to be a reliable source of EEG data and a basis for meaningful comparisons with other species as demonstrated by a line of different experiments conducted in dogs. For example, it has been shown in dogs that their sleep spectral feautres are predictive of their memory performances 70 and that sleep spindles are associated with better learning 20,71 , similarly to humans 72,73 and rats 74,75 . Different types of learning tasks have also been successfully carried out with wolves in comparative studies (e.g. ...
Full-text available
Sleep research greatly benefits from comparative studies to understand the underlying physiological and environmental factors affecting the different features of sleep, also informing us about the possible evolutionary changes shaping them. Recently, the domestic dog became an exceedingly valuable model species in sleep studies, as the use of non-invasive polysomnography methodologies enables direct comparison with human sleep data. In this study, we applied the same polysomnography protocol to record the sleep of dog’s closest wild relative, the wolf. We measured the sleep of seven captive (six young and one senior), extensively socialized wolves using a fully non-invasive sleep EEG methodology, originally developed for family dogs. We provide the first descriptive analysis of the sleep macrostructure and NREM spectral power density of wolves using a completely non-invasive methodology. For (non-statistical) comparison, we included the same sleep data of similarly aged dogs. Although our sample size was inadequate to perform statistical analyses, we suggest that it may form the basis of an international, multi-site collection of similar samples using our methodology, allowing for generalizable, unbiased conclusions. As we managed to register both macrostructural and spectral sleep data, our procedure appears to be suitable for collecting valid data in other species too, increasing the comparability of non-invasive sleep studies.
... Optimal rest and sleep are critical for working dogs. Sleep is associated with emotional state in sentient animals and is necessary for consolidation of learning, immune function, optimal performance and recovery to ensure longevity in working dog roles (139)(140)(141)(142). Remote monitoring of canine sleep can be used to alert staff to disruption or change from normal sleep patterns that might impact animal welfare (143). ...
Full-text available
Working dogs are prevalent throughout our societies, assisting people in diverse contexts, from explosives detection and livestock herding, to therapy partners. Our scientific exploration and understanding of animal welfare have grown dramatically over the last decade. As community attitudes toward the use of animals continue to change, applying this new knowledge of welfare to improve the everyday lives of working dogs will underpin the sustainability of working with dogs in these roles. The aim of this report was to consider the scientific studies of working dogs from the last decade (2011–2021) in relation to modern ethics, human interaction, and the five domains of animal welfare: nutrition, environment, behavioral interaction, physical health, and mental state. Using this framework, we were able to analyze the concept and contribution of working dog welfare science. Noting some key advances across the full working dog life cycle, we identify future directions and opportunities for interdisciplinary research to optimize dog welfare. Prioritizing animal welfare in research and practice will be critical to assure the ongoing relationship between dogs and people as co-workers.
... Since we contrasted AI and ABS-AI values in each frequency bin, we had to consider the issue of multiple comparisons. In order to control for multiple comparisons, the so-called Rüger's areas 45 were delineated the same way as in previous sleep studies 26,29 . As a consequence, sets of frequency bins with conventionally significant (p < 0.05) results regarding LI were accepted or rejected as significant as a whole. ...
Full-text available
Functional hemispheric asymmetry was evidenced in many species during sleep. Dogs seem to show hemispheric asymmetry during wakefulness; however, their asymmetric neural activity during sleep was not yet explored. The present study investigated interhemispheric asymmetry in family dogs using non-invasive polysomnography. EEG recordings during 3-h-long afternoon naps were carried out (N = 19) on two occasions at the same location. Hemispheric asymmetry was assessed during NREM sleep, using bilateral EEG channels. To include periods with high homeostatic sleep pressure and to reduce the variance of the time spent in NREM sleep between dogs, the first two sleep cycles were analysed. Left hemispheric predominance of slow frequency range was detected in the first sleep cycle of sleep recording 1, compared to the baseline level of zero asymmetry as well as to the first sleep cycle of sleep recording 2. Regarding the strength of hemispheric asymmetry, we found greater absolute hemispheric asymmetry in the second sleep cycle of sleep recording 1 and 2 in the frequency ranges of alpha, sigma and beta, compared to the first sleep cycle. Differences between sleep recordings and consecutive sleep cycles might be indicative of adaptation-like processes, but do not closely resemble the results described in humans.
Full-text available
Human patients with chronic pain from osteoarthritis often report impaired sleep, but it is not yet known if sleep is also impaired in dogs with osteoarthritis. This study aimed to compare the night-time sleep behaviour of osteoarthritic (N=20) and healthy control (N=21) dogs over a 28-day period, using an actigraphic device (the FitBark activity monitor) and an owner questionnaire designed to measure sleep quality (the SNoRE). Actigraphic data were aggregated to estimate the time each dog spent resting each night, and questionnaires were completed every 7 days. Data were analysed using robust mixed-effects linear regression. The presence of clinical signs of osteoarthritis had a significant effect on actigraphic recordings, with osteoarthritic dogs spending lower proportions of the night period resting (and therefore higher proportions of the night period active) compared to control dogs (z=2.21; P=0.0268). However, there was no significant difference between the SNoRE scores of osteoarthritic and control dogs (z=-1.01, p=0.312). The actigraphic findings of this study suggest that dogs with osteoarthritis may experience impaired sleep, which could have important welfare implications and merits further study.
Full-text available
The incidence rates of depression are increasing year by year. As one of the main clinical manifestations of depression, sleep disorder is often the first complication. This complication may increase the severity of depression and lead to poor prognosis in patients. In the past decades, there have been many methods used to evaluate sleep disorders, such as polysomnography and electroencephalogram, actigraphy, and videography. A large number of rodents and non-human primate models have reproduced the symptoms of depression, which also show sleep disorders. The purpose of this review is to examine and discuss the relationship between sleep disorders and depression. To this end, we evaluated the prevalence, clinical features, phenotypic analysis, and pathophysiological brain mechanisms of depression-related sleep disturbances. We also emphasized the current situation, significance, and insights from animal models of depression, which would provide a better understanding for the pathophysiological mechanisms between sleep disturbance and depression.
Full-text available
The origin of domestic dogs remains controversial, with genetic data indicating a separation between modern dogs and wolves in the Late Pleistocene. However, only a few dog-like fossils are found prior to the Last Glacial Maximum, and it is widely accepted that the dog domestication predates the beginning of agriculture about 10,000 years ago. In order to evaluate the genetic relationship of one of the oldest dogs, we have isolated ancient DNA from the recently described putative 33,000-year old Pleistocene dog from Altai and analysed 413 nucleotides of the mitochondrial control region. Our analyses reveal that the unique haplotype of the Altai dog is more closely related to modern dogs and prehistoric New World canids than it is to contemporary wolves. Further genetic analyses of ancient canids may reveal a more exact date and centre of domestication.
We have previously presented a wealth of data refuting the proposal that memories are processed or consolidated in sleep. Our objections have been largely ignored, creating the impression that the hypothesized role for sleep in memory processing is an established fact rather than a highly controversial and unresolved issue. We briefly review the main arguments against a role for sleep in learning/memory.
Sleep plays an important role in stabilizing new memory traces after learning. Here we investigate whether sleep’s role in memory processing is similar in evolutionarily distant species and demonstrate that a context trigger during deep-sleep phases improves memory in invertebrates, as it does in humans. We show that in honeybees (Apis mellifera), exposure to an odor during deep sleep that has been present during learning improves memory performance the following day. Presentation of the context odor during wake phases or novel odors during sleep does not enhance memory. In humans, memory consolidation can be triggered by presentation of a context odor during slow-wave sleep that had been present during learning. Our results reveal that deep-sleep phases in honeybees have the potential to prompt memory consolidation, just as they do in humans. This study provides strong evidence for a conserved role of sleep—and how it affects memory processes—from insects to mammals.
During the approximately 18–32 thousand years of domestication [1], dogs and humans have shared a similar social environment [2]. Dog and human vocalizations are thus familiar and relevant to both species [3], although they belong to evolutionarily distant taxa, as their lineages split approximately 90–100 million years ago [4]. In this first comparative neuroimaging study of a nonprimate and a primate species, we made use of this special combination of shared environment and evolutionary distance. We presented dogs and humans with the same set of vocal and nonvocal stimuli to search for functionally analogous voice-sensitive cortical regions. We demonstrate that voice areas exist in dogs and that they show a similar pattern to anterior temporal voice areas in humans. Our findings also reveal that sensitivity to vocal emotional valence cues engages similarly located nonprimary auditory regions in dogs and humans. Although parallel evolution cannot be excluded, our findings suggest that voice areas may have a more ancient evolutionary origin than previously known.
The traditional and relatively narrow-focused research on ape-human comparisons has recently been significantly extended by investigations of different clades of animals, including the domestic dog (Canis familiaris). Here, we provide a short overview of how the comparative investigation of canine social behaviour advances our understanding of the evolution of social skills and argue that a system-level approach to dog social cognition provides a broader view on the 'human-likeness' of canine social competence. We introduce the concept of evolutionary social competence as a collateral notion of developmental social competence. We argue that such an extended perspective on social competence provides a useful tool for conceptualising wolf-dog differences in socio-cognitive functioning, as well as for considering specific social skills not in isolation, but as a part of a system.
Over more than a century of research has established the fact that sleep benefits the retention of memory. In this review we aim to comprehensively cover the field of "sleep and memory" research by providing a historical perspective on concepts and a discussion of more recent key findings. Whereas initial theories posed a passive role for sleep enhancing memories by protecting them from interfering stimuli, current theories highlight an active role for sleep in which memories undergo a process of system consolidation during sleep. Whereas older research concentrated on the role of rapid-eye-movement (REM) sleep, recent work has revealed the importance of slow-wave sleep (SWS) for memory consolidation and also enlightened some of the underlying electrophysiological, neurochemical, and genetic mechanisms, as well as developmental aspects in these processes. Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories. Consolidation originates from reactivation of recently encoded neuronal memory representations, which occur during SWS and transform respective representations for integration into long-term memory. Ensuing REM sleep may stabilize transformed memories. While elaborated with respect to hippocampus-dependent memories, the concept of an active redistribution of memory representations from networks serving as temporary store into long-term stores might hold also for non-hippocampus-dependent memory, and even for nonneuronal, i.e., immunological memories, giving rise to the idea that the offline consolidation of memory during sleep represents a principle of long-term memory formation established in quite different physiological systems.
The main aim of this study was to identify interictal epileptiform discharges in a group of dogs with seizures of known aetiology (symptomatic epilepsy, SE) and in dogs with idiopathic epilepsy (IE). Propofol was used for chemical restraint in all dogs. We found electroencephalographic (EEG) changes that could be considered epileptiform discharges (EDs) in 5 out of 40 dogs (12.5%). The EEG changes identified were spikes in four cases and periodic epileptiform discharges in one case. All EDs were seen in the SE group. We conclude that the interictal electroencephalographic examinations of propofolanaesthetised dogs suffering from IE and SE rarely show epileptic discharges and that the diagnostic value of such EEGs in the work-up for epilepsy seems to be low as epileptic discharges were unlikely to be detected. However, positive findings are more likely to be connected with SE. We found frequent, transient EEG phenomena (spindles, K-complexes, vertex waves, positive occipital sharp transients of sleep, cyclic alternating patterns), which are non-epileptic but their differentiation from epileptic phenomena is challenging.
Abstract  Although respiration in trained canines is well investigated, the process of preparing dogs has not been described in any great detail. Moreover, their daytime patterns of sleep and wakefulness during 1 or 2 h of electroencephalogram (EEG) and electrocardiogram (ECG) recordings are not clear. Therefore, we describe the process of selecting and training dogs, in which we recorded EEG and ECG in the laboratory. First, 14 of 1242 dogs dealt with over a 1 year period were chosen. They were trained for 2 h to lie quietly and to sleep in the laboratory; this training procedure was repeated 152 times. Three dogs were then selected and a permanent tracheostomy was performed in one. Finally, EEG and ECG were recorded with the bipolar fine needle electrodes; respiration was recorded simultaneously through a tube inserted to a tracheostomy in one dog. Wakefulness, slow wave sleep (SWS) and rapid eye movement (REM) sleep (REMS) were identified according to the EEG pattern and on the basis of the behavioral criteria. Recordings were performed 12 or 13 times in each dog. Complete sleep cycles, including wakefulness, SWS and REMS in this sequence, were observed 3.9–4.1 times. The mean duration of SWS was 2.2–4.4 min and that of REMS was 3.5–4.6 min. The REMS latency was 33.9–41.8 min. Fluctuation of heart rate with respiration, termed respiratory sinus arrhythmia, was noted in the ECG. Heart beat increased with inspiration and decreased with expiration. The present study demonstrates how to select and train sleeping dogs and shows their undisturbed daytime sleep and wakefulness patterns.