Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
The interrelated eect 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 dierent 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 eect 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 eects. 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 oen 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 dier across species2,
calling for the integration of human and laboratory rodent research into a wider set of results from dierent ani-
mal species7. In an eort 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
dierences in sleep EEG spectrum following a command learning (CL), and a non-learning (NL) task, respec-
tively. Fieen 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 aer the
usual (known) commands, in the very same way as in the CL task (see Experimental Procedures). Aer 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 aer 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 eect 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 dierence 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 signicant 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 aer learning
(t(10) = 3.130, p = 0.011), while the relative decrease in delta (1–4 Hz) activity was not signicant (t(10) = 1.898,
p = 0.087). No eect 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 signicant relative
decrease in the 1.5–2 Hz (delta) frequency range aer learning during REM sleep, while the relative increase in
the 3.5–4 Hz (delta) frequency did not remain signicant aer correction for multiple comparisons. No signicant
bin-wise dierences 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 dierence 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 aect sleep macrostructure (see
Supplemental Results), contrary to our expectations, but in line with some human studies, where similarly to our
ndings no dierences were found between learning and non-learning conditions, regarding the time spent in
dierent sleep stages21.
Behavioural data showed that subjects’ performance signicantly increased aer 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 signicant 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 inuences sleep EEG spectrum in dogs,
and that the EEG spectrum during sleep is predictive of memory performance. Although “memory” is oen
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 inuences 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 aer 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 aer 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 aer 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 aer learning29.
The eect 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) inuenced 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). Aer 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 dier 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
aer the retention interval (Retest), and aer one week (Long-term), in order to assess short- and long-term
memory eects of the dierent RICs. A Generalized Linear Mixed Model (Poisson Log; Table1) showed that, as
expected, performance was inuenced by the interaction of test occasion (Baseline, Retest, Long-term) × RIC
(χ2(4) = 14.435, p = 0.006), suggesting that dierential learning patterns emerged as a consequence of the dierent
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
eect of RIC was also signicant as a main eect (χ2(2) = 8.020, p = 0.018), but the main eect of test occasion did
Figure 2. Relationship between performance improvement (the relative dierence 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 signicance (χ2(2) = 5.860, p = 0.053). e main eect 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-signicant. e
pairwise post hoc analysis revealed that in the Sleep condition, despite a tendency towards performance improve-
ment, there was no dierence 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 aer
3 hours of sleep, but can probably be attributed to the dierence 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 benet memory and how this might generalize
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 aer 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
aer 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 dierence between Baseline and
post-walk Retest (p > 0.05), but the Long-term performance was signicantly 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 aer 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 eect. 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 beneted from the at-home night sleep aer 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 eect of sleep and learning in
dogs, suggesting that a sleep-dependent memory consolidation takes place in this species. Further studies should
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 dierent 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 dierential 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 dierences 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 Scientic 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 suering than the equivalent of inserting a needle. All owners volun-
teered to participate in the study.
The eect 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 TableS1). 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 dierent 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 dierence 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 eect 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). Aer 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) aer the rst occasion, dogs returned for another session of 18 trials to assess their
long-term memory (Long-term; TableS2).
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
1. avanau, J. L. Vertebrates that never sleep: implications for sleep’s basic function. Brain es. Bull. 46, 269–79 (1998).
2. Siegel, J. M. Sleep viewed as a state of adaptive inactivity. Nat. ev. Neurosci. 10, 747–753 (2009).
3. Savage, V. M. & West, G. B. A quantitative, theoretical framewor for understanding mammalian sleep. Proc. Natl. Acad. Sci. USA
104, 1051–1056 (2007).
4. Vertes, . P. & Siegel, J. M. Time for the sleep community to tae a critical loo at the purported role of sleep in memory processing.
Sleep 28, 1223–1228 (2005).
5. Dieelmann, S. & Born, J. e memory f unction of sleep. Nat. ev. Neurosci. 11, 114–26 (2010).
6. Zwaa, H. et al. Context odor presentation during sleep enhances memory in honeybees. Curr. Biol. 25, 2869–2874 (2015).
7. Siegel, J. M. Clues to the functions of mammalian sleep. Nature 437, 1264–71 (2005).
8. Tobler, I. Is sleep fundamentally dierent between mammalian species? Behav. Brain es. 69, 35–41 (1995).
9. Lucas, E. A., Powell, E. & Murphree, O. Baseline sleep-wae patterns in the pointer dog. Physiol. Behav. 19, 285–291 (1977).
10. Wauquier, A., Verheyen, J. L., Van Den Broec, W. A. E. & Janssen, P. A. J. Visual and computer-based analysis of 24 h sleep-waing
patterns in the dog. Electroencephalogr. Clin. Neuropsychol. 46, 33–48 (1979).
Scientific RepoRts | 7:41873 | DOI: 10.1038/srep41873
11. Yasuma, F. et al. ecording of electroencephalograms and electrocardiograms during daytime sleep in trained canines: preparation
of the sleeping dogs. Psychiatry Clin. Neurosci. 51, 237–9 (1997).
12. Taeuchi, T. & Harada, E. Age-related changes in sleep-wae rhythm in dog. Behav. Brain es. 136, 193–9 (2002).
13. Fox, M. W. & Stone, A. B. An Electroencephalographic Study of Epilepsy in the Dog. J. Small Anim. Pract. 8, 703–708 (1967).
14. Páozdy, Á., alhammer, J., Leschni, M. & Halász, P. Electroencephalographic examination of epileptic dogs under propofol
restraint. Acta Vet. Hung. 60, 309–324 (2012).
15. Mitler, M. M. & Dement, W. C. Sleep studies on canine narcolepsy: pattern and cycle comparisons between aected and normal
dogs. Electroencephalogr. Clin. Neurophysiol. 43, 691–9 (1977).
16. Hare, B. & Tomasello, M. Human-lie social sills in dogs? Trends Cogn. Sci. 9, 439–444 (2005).
17. Milósi, Á. & Topál, J. What does it tae to become ‘best friends’? Evolutionary changes in canine social competence. Trends Cogn.
Sci. 17, 287–294 (2013).
18. Druzhova, A. S. et al. Ancient DNA Analysis Arms the Canid from Altai as a Primitive Dog. PLoS One 8, e57754 (2013).
19. is, A. et al. Development of a non-invasive polysomnography technique for dogs (Canis familiaris). Physiol. Behav. 130, 149–156
20. Perogamvros, L. & Schwartz, S. e roles of the reward system in sleep and dreaming. Neurosci. Biobehav. ev. 36, 1934–1951 (2012).
21. Gais, S., Mölle, M., Helms, . & Born, J. Learning-dependent increases in sleep spindle density. J. Neurosci. 22, 6830–4 (2002).
22. Maquet, P. e role of sleep in learning and memory. Science (80-). 294, 1048–52 (2001).
23. Huber, ., Ghilardi, M. F., Massimini, M. & Tononi, G. Local sleep and learning. Nature 430, 78–81 (2004).
24. Marshall, L., Helgadóttir, H., Mölle, M. & Born, J. Boosting slow oscillations during sleep potentiates memory. Nature 444, 610–613
25. Louie, . & Wilson, M. A. Temporally str ucture d replay of awae hippocampal ensemble activity dur ing rapid eye movement sleep.
Neuron 29, 145–156 (2001).
26. Fogel, S. M., Smith, C. T. & Cote, . A. Dissociable learning-dependent changes in EM and non-EM sleep in declarative and
procedural memory systems. Behav. Brain es. 180, 48–61 (2007).
27. Hellman, . & Abel, T. Fear conditioning increases NEM sleep. Behav. Neurosci. 121, 310–23 (2007).
28. asch, B. & Born, J. About Sleep’s rome in memory. Physiol. ev. 93, 681–766 (2013).
29. Mcinney, S. M., Dang-Vu, T. T., Buxton, O. M., Solet, J. M. & Ellenbogen, J. M. Cover t waing brain activity reveals instantaneous
sleep depth. PLoS One 6, 1–7 (2011).
30. Sticgold, ., Whidbee, D., Schirmer, B., Patel, V. & Hobson, J. a. Visual discrimination tas improvement: A multi-step process
occurring during sleep. J. Cogn. Neurosci. 12, 246–254 (2000).
31. orman, M. et al. Daytime sleep condenses the time course of motor memor y consolidation. Nat. Neurosci. 10, 1206–1213 (2007).
32. Sticgold, ., James, L. & Hobson, J. a. Visual discrimination learning requires sleep aer training. Nat. Neurosci. 3, 1237–8 (2000).
33. Broaw, . et al. esting state EEG correlates of memory consolidation. Neurobiol. Learn. Mem. 130, 17–25 (2016).
34. Adams, B. et al. Use of a delayed non-matching to position tas to model age-dependent cognitive decline in the dog. Behav. Brain
es. 108, 47–56 (2000).
35. Téglás, E. et al. Dogs’ gaze following is tuned to human communicative signals. Curr. Biol. 22, 209–212 (2012).
36. Andics, A., Gácsi, M., Faragó, T., is, A. & Milósi, Á. Voice-sensitive regions in the dog and human brain are revealed by
comparative fMI. Curr. Biol. 24, 574–578 (2014).
e study was supported by Nestlé−Purina, the Hungarian Scientic 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.
Conceptualization: A.K., S.S., M.G., P.S., R.B. and J.T.; Methodology: A.K., S.S., M.G. and R.B.; Soware 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.
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Kis, A. et al. e interrelated eect of sleep and learning in dogs (Canis familiaris); an
EEG and behavioural study. Sci. Rep. 7, 41873; doi: 10.1038/srep41873 (2017).
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
is work is licensed under a Creative Commons Attribution 4.0 International License. e images
or other third party material in this article are included in the article’s Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/
© e Author(s) 2017