Auditory Responses and Stimulus-Specific Adaptation in Rat Auditory Cortex
are Preserved Across NREM and REM Sleep
Yuval Nir1,2, Vladyslav V. Vyazovskiy1,3, Chiara Cirelli1, Matthew I. Banks4and Giulio Tononi1
1Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA,2Department of Physiology and
Pharmacology, Sackler School of Medicine, and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel,
3Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3QX, UK and4Department of
Anesthesiology, University of Wisconsin, Madison, WI 53706, USA
Address correspondence to Dr Yuval Nir, Department of Physiology and Pharmacology, Sackler School of Medicine, and Sagol School of
Neuroscience, Tel Aviv 69978, Israel. Email: firstname.lastname@example.org
Sleep entails a disconnection from the external environment. By and
large, sensory stimuli do not trigger behavioral responses and are
not consciously perceived as they usually are in wakefulness. Tra-
ditionally, sleep disconnection was ascribed to a thalamic “gate,”
which would prevent signal propagation along ascending sensory
pathways to primary cortical areas. Here, we compared single-unit
and LFP responses in core auditory cortex as freely moving rats
spontaneously switched between wakefulness and sleep states.
Despite robust differences in baseline neuronal activity, both the
selectivity and the magnitude of auditory-evoked responses were com-
parable across wakefulness, Nonrapid eye movement (NREM) and
rapid eye movement (REM) sleep (pairwise differences <8% between
states). The processing of deviant tones was also compared in sleep
and wakefulness using an oddball paradigm. Robust stimulus-specific
adaptation (SSA) was observed following the onset of repetitive tones,
and the strength of SSA effects (13–20%) was comparable across vigi-
lance states. Thus, responses in core auditory cortex are preserved
across sleep states, suggesting that evoked activity in primary sensory
cortices is driven by external physical stimuli with little modulation by
vigilance state. We suggest that sensory disconnection during sleep
occurs at a stage later than primarysensoryareas.
Keywords: auditory cortex, NREM sleep, oddball, rat, REM sleep, single unit
A defining feature of sleep is that it brings about a reversible
reduction in behavioral responsiveness. Such a high “arousal
threshold” is evident in NREM sleep and persists also in REM
sleep (Rechtschaffen et al. 1966; Neckelmann and Ursin 1993).
Accordingly, external stimuli fail to elicit an adequate behav-
ioral response unless they are strong enough to cause an awa-
kening, as may happen with the sound of an alarm clock.
Moreover, stimuli largely fail to be incorporated in the content
of dreams (Rechtschaffen 1978; Nir and Tononi 2010). For
example, if we are to sleep all night in front of the television,
our dreams will have little, if anything, to do with the contents
of the surrounding stream of sounds. During anesthesia and
NREM sleep, sensory processing may be expected to be differ-
ent than during wakefulness, since the thalamocortical system
is active in a drastically different mode with hyperpolarized
cortical neurons alternating between active and silent periods
(Steriade et al. 2001). However, a high arousal threshold per-
sists during REM sleep when the thalamocortical system is
depolarized with a wake-like “activated” low-voltage electro-
encephalogram (EEG), and when neurons fire tonically much
like during wakefulness (Steriade et al. 2001). Therefore,
disconnection continues throughout sleep and its diverse
modes of activity and poses an unsolved paradox.
Despite the disconnection from the external environment
during sleep, it is also clear that some sensory processing and
discriminative capacity persists. For example, sleeping humans
may respond more readily to their name being called when
compared with other names and sounds (Oswald et al. 1960;
McDonald et al. 1975) and evoked potentials triggered by
semantically incongruous words may be elicited in some
stages of human sleep (Bastuji et al. 2002). Given the discon-
nection on one hand, and the residual processing on the other,
it remains unclear to what extent the signal propagation along
ascending sensory pathways that occurs during wakefulness
is preserved during sleep. Compared with the large body of
literature in acute anesthetized preparations, only very few
studies examined sensory responses at the neuronal level
during natural sleep [see Hennevin et al. (2007) for review].
Owing to its burst-silence mode of activity during NREM
sleep, the thalamus has been traditionally considered as the
major processing node where sensory signal transmission is atte-
nuated during sleep. Accordingly, sleep disconnection may be
due to “thalamic gating” where peripheral sensory inputs are
not relayed effectively to the cortex (McCormick and Bal 1994;
Steriade 2003). Along this line, attenuated single-unit responses
in thalamic sensory relay nuclei have been observed during
NREM sleep in primates, cats, and rodents (Mukhametov and
Rizzolatti 1970; Livingstone and Hubel 1981; Mariotti et al. 1989;
Edeline et al. 2001). In addition, studies of primary visual (Evarts
1963; Livingstone and Hubel 1981) and somatosensory (Gucer
1979) cortices demonstrated attenuated responses in sleep, but
large variabilityexists between studies and between cortical cells.
In the auditory system, it remains particularly undecided to
what extent primary auditory cortex (PAC) responds to sounds
during sleep (PAC is used throughout this article to refer to
core auditory fields receiving input from the ventral division of
the medial geniculate nucleus of the thalamus). Early evoked
potential studies in the rat found enhanced responses during
NREM sleep and comparable responses in REM sleep (Hall and
Borbely 1970). Early single-unit studies in cats and primates
suggested that PAC responses were weaker in sleep compared
with wakefulness (Murata and Kameda 1963; Brugge and Mer-
zenich 1973) but recent single-unit studies in guinea pigs and
primates found comparable responses (Pena et al. 1999;
Edeline et al. 2001; Issa and Wang 2008) apart from potential
subtle differences (Issa and Wang 2011). Functional imaging in
humans has also yielded mixed results; some studies argue for
comparable activation in sleep and wakefulness at the level of
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PAC (Portas et al. 2000) whereas others argue for decreased
activity in sleep (Czisch et al. 2002).
Apart from basic processing of sounds in PAC (e.g., selectiv-
ity to acoustic features, response magnitude) which could be
preserved during sleep (Pena et al. 1999; Edeline et al. 2001;
Issa and Wang 2008), the degree to which context-dependent
processing is maintained in sleep remains unknown. In
humans, infrequent stimuli in a sequence of sounds elicits, in
awake subjects, a midlatency (150–250 ms) characteristic EEG
event-related potential (ERP) called the mismatch negativity
(MMN) followed by a late (>250 ms) P300/P3 wave (Naatanen
et al. 2011). In general, the MMN reflects preattentive proces-
sing whereas the P3 is correlated with perceptual awareness
(Dehaene and Changeux 2011). Evoked potential studies in
sleeping humans found evidence for differential responses
to oddball stimuli in some circumstances (Niiyama et al. 1994;
Perrin et al. 1999, 2000; Ruby et al. 2008), but the magnitude
and spatial extent of the P3 may be reduced (Cote 2002;
Colrain and Campbell 2007). Animal studies have also used
auditory oddball paradigms to examine, at the neuronal level,
both early and late components in the responses to frequent
and infrequent stimuli. Studies of early responses typically
focus on stimulus-specific adaptation (SSA) of single neurons
in the auditory thalamus and cortex. SSA has been extensively
studied in anesthetized cats and rodents, and is believed to rep-
resent the cellular information processing that eventually gives
rise to the MMN (Nelken and Ulanovsky 2007). Along this line,
deviance sensitivity observed in human midlatency potentials
suggests that SSA is present in human auditory cortex (Costa-
Faidella et al. 2011). Other studies reported that infrequent
deviant tones could also trigger long-latency (>200 ms) ERPs,
particularly, when rare tones were associated with rewards or
aversive outcomes through training (Yamaguchi et al. 1993;
Shinba 1997). However, the processing of deviant sounds at
the neuronal level during natural sleep remains unknown.
In the present study, we compared single-unit and local field
potential (LFP) responses as well as SSA in PAC of rats across
wakefulness and natural sleep. The aims of this study were
3-fold: 1) to examine the degree to which responses in PAC are
preserved during sleep in rodents, the most common model
system for studying sleep and sleep regulation, and one that is
most suitable for future studies in transgenic animals when
employing novel tools such as calcium imaging and optoge-
netics; 2) to check whether comparable PAC responses are pre-
served also in unrestrained animals as they undergo natural
physiological sleep. All previous studies of single-unit auditory
responses in sleep were conducted in restrained animals to
achieve maximal control over acoustic stimulation. However,
such conditions may not be favorable for neither alert wakeful-
ness (locomotion may have dramatic effects on activity in
primary sensory cortices (Keller et al. 2012)) or deep sleep
(associated with marked reduction in baseline firing rates and
neuronal bistability), thereby limiting the potential contrast
between these vigilance states, 3) to examine to what degree
differential responses to deviant stimuli (e.g., SSA) are main-
tained in single-unit responses during sleep.
Materials and Methods
Subjects and Surgery
Adult male WKY rats (n=6, 300–350 g, Harlan Ltd.) were housed indi-
vidually in transparent Plexiglas cages. Lighting and temperature were
kept constant (LD 12:12, light on at 10:00 AM, 23±1 °C; food and
water available ad libitum). All procedures related to animal handling,
recording, and surgery followed the NIH Guide for the Care and Use of
Laboratory Animals, and were approved by the Institutional Animal
Care and Use Committee (IACUC). Three days prior to surgery, rats
were placed in their home cage within the acoustic chamber for habitu-
ation to the experimental environment. One day before surgery
animals received an i.p. dose of dexamethasone (0.2 mg/kg) to sup-
press local immunological response and reduce edema (Vyazovskiy,
Olcese et al. 2011).
Under deep isoflurane anesthesia (1.5–2% volume), microwire
arrays were implanted targeting the right PAC in 6 animals (centered at
B: −4.3 mm, L: +6.7 mm, D: −4.6 mm) and in 3 animals also in the
right frontal cortex (centered at B: +2 mm, L: +2 mm, D: −2 mm;
Fig. 1A–C). The arrays consisted of 16-channel (2 rows of 8 wires
each), 33-µm polyimide-insulated tungsten microwires (Tucker-Davis
Technologies, Inc. (TDT), Alachua, FL, USA; spacing between micro-
wires: 175–250 µm; separation between rows: L–R: 375–500 µm, D–V:
0.5 mm). Surgery was performed in sterile conditions, using Ethylene
oxide sterilized materials, following procedures described in Kralik
et al. (2001) and Vyazovskiy, Olcese et al. (2011). Under microscopic
control, a ∼2×2-mm craniotomy was made using high-speed surgical
drills. The dura was carefully dissected and electrode arrays were
advanced into the brain tissue by penetrating the pia mater while care-
fully avoiding vasculature. Two-component silicone gel (KwikSil;
World Precision Instruments, FL, USA) was applied to seal the craniot-
omy and protect the brain surface. After gel polymerization, dental
acrylic was gently placed around the electrode, fixing the array to the
skull. EEG screws were placed over the left frontal and parietal cor-
tices. Ground and reference screw electrodes were placed above the
cerebellum, and neck muscle electrodes were implanted for electro-
We continuously recorded concurrently extracellular spike data
(filtered 300–5000 Hz), LFPs from the same microelectrodes (filtered
0.1–100 Hz), epidural EEGs (filtered 0.1–100 Hz), muscle tone via EMG
(filtered 10–100 Hz) using a TDT PZ amplifier and RZ2 acquisition
system, as well as synchronized continuous infrared video recordings
(OptiView Technologies, Inc.), as in Vyazovskiy, Olcese et al. (2011).
Amplitude thresholds for online spike detection were set manually
upon careful visual inspection (OpenEx software, TDT). In all cases,
thresholds exceeded −25 µV and ensured a SNR >2 (see Supplemen-
tary Fig. 1 for examples). Whenever the recorded voltage in the high-
pass filtered signal (>300 Hz) exceeded this threshold, a segment of 46
samples (1.84 ms) was extracted together with corresponding time-
stamps and stored for subsequent analysis. We refer to the resulting
binary signal with putative action potentials (prior to spike sorting) as
multiunit activity (MUA) throughout the article. Spike sorting was per-
formed offline by superparamagnetic clustering of wavelet coefficients
(Quian Quiroga et al. 2004), and complemented by visual inspection of
action potential waveforms, their variability, and interspike-interval
(ISI) distributions. Based on the consistency of waveforms and the
occurrence of ISIs within the expected refractory period (<3 ms), we
categorized clusters as either mixed clusters, single-unit clusters, or
noise clusters (the latter were not analyzed).
Scoring Vigilance States and Behavioral Analysis
Vigilance states were manually scored in 4-s epochs based on visual
inspection. SleepSign software (Kissei) was used to simultaneously
visualize offline EEG, LFP, EMG, and behavior (video). Wakefulness
was defined as epochs dominated by low-voltage, high-frequency
EEG/LFP patterns and phasic EMG activity. Epochs of eating, drinking,
and intense grooming (<5%) were excluded to minimize artifacts.
NREM sleep was defined as epochs with high-amplitude slow waves
and low tonic EMG activity. REM sleep was defined as epochs where
the EEG/LFP was similar to that during waking with slightly more pro-
nounced theta rhythms in posterior derivations, while the EMG con-
tained solely heartbeats and occasional twitches. Mixed epochs
(10.5%, including transitions and artifacts) were categorized separately
Preserved Responses in Auditory Cortex Across Sleep
• Nir et al.
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and excluded from further analysis. Joint distributions of EMG levels
and high-/low-frequency energy ratio in the EEG (as in Fig. 2D) were
computed for each session separately to further verify proper separ-
ation to vigilance states. The high-/low-frequency energy ratio was
computed using 25–100 and 1–4 Hz frequency bands, respectively.
Phasic EMG events reflecting a muscle twitch and/or startle response
evoked by a minority (<5%, see Results) of high-intensity (80 dB SPL)
sounds were automatically detected in Matlab (The MathWorks,
Natick, MA, USA). To this end, the instantaneous amplitude of the
EMG signal was extracted via the Hilbert transform and smoothed with
a Gaussian kernel (σ=10 ms). Events with amplitude above 3.5 SD oc-
curring within the first 75 ms following sound onset were identified.
These parameters were optimized after careful comparison of auto-
matically detected events with behavioral video recordingsto minimize
misses and false positives.
Acoustic Setup and Auditory Stimuli
All experiments were conducted in a foam-insulated cage placed
within a double-wall soundproof chamber (Industrial Acoustics
Company, Bronx, NY, USA). Sounds were synthesized online using
Matlab, transduced to voltage signals by a high-sampling rate (192
kHz) sound card (HDSP9632, RME, Germany), amplified (SA1, TDT),
and played free-field through a magnetic speaker (MF1, TDT) mounted
35 cm above the animal. All sounds were stereo signals where one
channel (containing the sound of interest) was routed to the mono
speaker, whereasthe otherchannel (containing a brief synchronization
pulse) was routed to the electrophysiologyacquisition system. Acoustic
calibration was performed in situ with a calibrated condenser micro-
phone placed on the cage’s floor 35 cm below the speaker (#4016,
ACO Pacific, Inc., Belmont, CA, USA). Intensity levels for frequencies
in the range of 1–64 kHz were measured, attenuation factors were com-
puted (all below 10 dB across frequencies), and then applied to the
original stimuli to reach a flat output (± few dB) independently for
output levels of 30, 55, and 80 dB SPL. Actual levels varied on each
trial given that stimuli were free-field and animals were not restrained.
The stimulus set consisted of 24 simple and complex sounds, includ-
ing 9 short (100 ms) pure tones (256 Hz to 64 KHz with octave
spacing; 5-ms onset/offset linear ramps); 3 long (600 ms) pure tones
(4, 8, and 16 kHz; 5-ms onset/offset linear ramps); 1 click; 1 click train
(interclick interval=20 ms, duration=500 ms); 3 environmental ecolo-
gically relevant sounds recorded at the laboratory (cage door opening
and closing, room door opening and closing, and experimenter’s
voice, durations=800 ms); 3 rat vocalizations (Avisoft Bioacoustics,
Germany) with carrier frequencies of 22, 40, and 60 kHz and durations
250–1000 ms; 2 “chirp-modulated” AM sounds (carrier frequency=
10 kHz, f(mod)=20–200 Hz or 200–20 Hz, duration=600 ms) as in
Artieda et al. (2004); and 2 frequency modulation (FM) sweeps (0.5–2
or 2–0.5 kHz, duration=100 ms). All sounds were digitally designed to
occupy 95% of the maximal dynamic range of the soundcard (voltage)
to avoid clipping, and subject to further amplification to reach final
outputs of 30, 55, and 80 dB SPL (see above).
One week was allowed for recovery after surgery, and experiments
started only after the sleep/waking cycle had normalized, as evidenced
by the entrainment of sleep and wake by the light/dark cycle and the
homeostatic time-course of slow-wave activity (power <4 Hz) in sleep
(Vyazovskiy, Cirelli et al. 2011). Prior to experiments, rats were well
habituated to the experimenter and to exposure of novel objects
(exposure to new objects every day at light onset for 30 min/day). In
addition, 5 days following surgery, we started to gradually expose the
animals to acoustic stimuli (session length and intensity levels were in-
cremented daily) until animals adapted and were able to comfortably
maintain consolidated sleep while sounds were played (no observable
muscle twitches for sounds at 30 and 55 dB SPL, and <10% of 80 dB
SPL trials associated with muscle twitches).
Experimental sessions started around noon (animals were allowed
to sleep uninterrupted for 2 h starting at lights-on at 10:00 AM) and
lasted 3–5 h. This timing was chosen in order to maximize the chances
that sufficient trials during REM sleep will be available for subsequent
Figure 1. Experimental setup. (A) Recording arrangement included a 16-microwire array implanted in the right auditory cortex (“A”, n=6), a control 16-microwire array placed in
the right motor cortex (“M”, n=3), EEGs from frontal “F” and parietal “P” derivations (red circles), ground “G” and reference “R” screws placed over the cerebellum (green circles),
and bilateral neck muscle electrodes for electromyography (EMG). (B) Sketch of surgical plan with oblique implantation of auditory microwires (red lines) superimposed with a
coronal diagram of the rat brain 4.5 mm posterior to Bregma (Paxinos and Watson 1986); dotted green lines denote borders of auditory cortex. (C) Representative histological
verification of location of electrodes coated with DiI fluorescent dye (Materials and Methods); dotted white lines denote putative borders of primary auditory cortex. Inset shows
fluorescence image corresponding to cyan area. (D) Example auditory stimulation protocol superimposed with changes in vigilance states. Sessions started around noon (time on
bottom) and lasted 3–5 h. Experiments included repeated identical blocks of sound stimulation (horizontal blue bars, top), interleaved with 10-min silent intervals. Rats were kept
continuously awake during the first block of stimulation (green box, W*) and were left undisturbed during all other blocks. W, N, and R correspond to wakefulness, NREM sleep, and
REM sleep, respectively. Note that percent time spent in each vigilance state does not add to 100% since mixed epochs were not furtheranalyzed (Materials and Methods).
Cerebral Cortex 3
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analysis. Each experiment consisted of repeated identical blocks of
sound stimulation (Fig. 1D; details below), interleaved with 10-min
intervals with no stimulation. During the first block of stimulation in
each session, rats were kept continuously awake by providing them
with novel objects (Vyazovskiy, Olcese et al. 2011), in order to allow
for separate subsequent analysis of auditory responses during consoli-
dated wake periods (W*). In all other stimulation blocks, animals were
left undisturbed. Two different experiments (main, oddball) were run
on separate days as given below.
In each 30-min block, all 24 sounds were presented at 3 intensity levels
(30/55/80 dB SPL) each for a total of 72 different stimuli. Each stimulus
waspresented15times (atotalof1080trials perblock) in apseudorandom
orderwithinterstimulus intervals (ISIs,offset to onset)of1250±250 ms.
Each 13.5-min block included 2 parts; in Part A, an 8-kHz tone was
presented frequently (n =450, 90%), whereas a 45-kHz tone was
Figure 2. Prestimulus baseline activity differs across vigilance states. (A) EMG dynamic range across vigilance states (n=10 sessions); muscle tone is highest during
wakefulness, lower in NREM sleep, and minimal in REM sleep; all asterisks mark statistical significance of at least P<10E-7 (paired t-tests). (B) Baseline neuronal firing rates.
Firing rates in auditory cortex (PAC, n=295) as well as motor cortex (M1, n=94) are higher in wakefulness than in NREM and REM sleep. Asterisks as in A (C) LFP power spectra
in motor cortex (left, n=80 channels) and auditory cortex (right, n=160 channels) across vigilance states. Note that slow-wave activity (power<4 Hz) is maximal in NREM
sleep (green), particularly in the frontal lobe, while high-frequency (>30 Hz) power is maximal in wakefulness (red) and REM sleep (blue). In addition, theta (5–9 Hz) power is higher
in wakefulness and maximal in REM sleep, particularly in the auditory cortex. (D) Joint distribution of EMG levels and high-/low-frequency energy ratio in the EEG in a representative
session. Colors as above. Note that both NREM and REM sleep are characterized by low EMG levels, whereas REM sleep and wakefulness are characterized by a dominance of
high-frequency power in the EEG.
Preserved Responses in Auditory Cortex Across Sleep
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presented rarely (n=50, 10%). Both tones were 100 ms in duration
with 5-ms onset/offset linear ramps. In Part B, tones were swapped
such that the 8-kHz tone was “standard” and the 45-kHz tone was
“deviant.” Both tones were presented at 55 dB SPL in a pseudorandom
order with ISIs of 750±100 ms.
Upon completion of the experiments, the position of electrodes was
verified by histology in all animals. Animals were perfused with 4%
paraformaldehyde/PBS under deep isoflurane anesthesia (3% in
oxygen), brains were postfixed, rapidly frozen on dry ice, cut into
50-µm serial coronal sections, and subjected to Cresyl Violet (Nissl)
staining. Histological verification confirmed that electrodes were
located within areas Au1/AuV/AuD as defined by (Paxinos and Watson
1986) and a recent study supporting a slightly extended dorsal border
(Doron et al. 2002). The most recent physiological studies in the rat
indicate that these anatomical regions are where tonotopically orga-
nized core (primary) auditory fields are located (Polley et al. 2007). In
2 of 6 animals, microwire arrays were coated 1 day prior to surgery
with a thin layer of DiI fluorescent dye (DiIC18(3), Invitrogen) under
microscopic control to facilitate subsequent localization (Magill et al.
2006), as seen in Figure 1C.
Analysis of Auditory Responses
All data analyses were carried out offline with Matlab. Evoked LFP and
MUA responses (Fig. 3) focused on tone pips (27 of 72 of all stimuli)
whose duration was identical (100 ms). Comparison of MUA onset
responses across stimulus intensities and vigilance states was per-
formed using a two-way ANOVA using the maximal peak of the
response in the interval 10–30 ms after stimulus onset. For isolated
SUA recordings, significant changes in firing rate in time intervals cor-
responding to onset, offset, and sustained periods were detected as
follows. First, spike trains were aligned on stimulus onset, averaged
across trials, and binned (bin size=25 ms). Onset and offset intervals
were defined as the first 25 ms bin following sound onset and offset,
respectively. Sustained intervals were defined as all other bins during
stimulus presentation. Changes in firing rate were detected in the inter-
val of interest (onset/offset/sustained) by comparison with prestimulus
baseline periods (600 ms prior to stimulus onset) in a particular state
via Student’s t-tests. Given that we conducted multiple t-tests (in the
main experiment, 389 units×72 stimuli×checking for increases/
decreases in onset/offset/sustained intervals resulted in 473428 tests),
we corrected for multiple comparisons using false discovery rate (FDR)
control for familywise error (Benjamini and Yekutieli 2001). FDR was
controlled such that q(FDR)<0.05 for each state separately (corre-
sponding to critical P-values in the range 0.0036–0.006), thereby
achieving the same statistical strength across vigilance states and utiliz-
ing all available data, despite having a different number of trials in
each state. Similar results were obtained when selecting the same
number of trials in each state for analysis. In general, our simple algor-
ithm performed well across different conditions, agreeing with visual
inspection of responses, and was robust to the precise choice of
threshold (e.g., P<0.01 or P<0.0001 uncorrected) and bin size (15,
25, 50, and 100 ms) such that these parameters did not affect the main
Figure 3. Overview of evoked responses in main experiment. Overview of evoked responses to 100-ms tones across vigilance states (n=10 sessions in 6 animals). (A) Average
event-related LFP responses recorded in auditory cortex (n=160 channels, N =2333, 4957, and 1458 tones in Wake, NREM, and REM sleep, respectively) in response to low
(30 dB, black), medium (55 dB, brown), and high-intensity (80 dB, red) tones. Horizontal green bars mark stimulus duration. Vertical green lines mark tone onset. Note that LFP
responses are nearly identical in wakefulness and REM sleep, whereas, in NREM sleep, they are larger in amplitude and followed by a positive peak (red asterisk) corresponding to a
poststimulus suppression of neuronal activity. (B) Evoked multiunit activity from the same channels in auditory cortex in response to tones; colors and intensities as above. Note that
both onset and offset MUA responses are parametrically related to stimulus intensity and highly similar across vigilance states, despite difference in baseline firing rates (horizontal
dotted cyan line). (C) EMG evoked by auditory stimuli reveals that high-intensity (80 dB) tones elicit detectable transient changes in muscle tone corresponding to a startle response,
whereas low- and medium-intensity stimuli do not elicit any changes above baseline. In addition, differences in prestimulus baseline values replicate those in Figure 2.
Cerebral Cortex 5
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Analysis of Latency of Excitatory Onset Responses
For each auditory unit for which an excitatory onset response was
detected for at least 1 stimulus (n =195/295 units, see above), a peristi-
mulus time histogram (PSTH) was created for each stimulus separately
(without binning) in a window beginning 600 ms before stimulus
onset and terminating 600 ms poststimulus onset. For each onset
response, onset latency was defined as in Polley et al. (2007), as the
intersection between the slope of the excitatory response profile and
the mean prestimulus firing rate (illustrated in Fig. 5B). Finally, the
onset latency of each neuron was defined as the average onset latency
across all stimuli that elicited onset responses for that neuron (latencies
did not significantly differ across intensities).
Comparison Across Vigilance States
Once every condition (stimulus) in every state (wake/sleep) was
tagged for the presence of various responses (i.e., changes in firing
rate), we proceeded to compare quantitatively the selectivity and
response magnitude across states. Selectivity was defined in this
context as the percent of suprathreshold stimuli (excluding 30SPL) for
which responses were detected, and response magnitudes were com-
puted using the mean discharge rate (spikes per second). Modulation
gain factors (Fig. 7, bottom) were extracted for each pair of vigilance
states in each recording session separately according to the following
formula, as in Issa and Wang (2008):
where Riand Rjare the discharge rates during vigilance states i and j,
respectively. Gains were computed for each stimulus separately. Con-
clusions were unaffected when expressing response magnitudes in
firing rates normalized by the baseline firing rate in each vigilance
states, instead of absolute spikes per second. For each pairwise com-
parison between states (e.g., NREM versus wake), we checked whether
the mean gain factor represented a significant deviation from a null
(zero-centered) distribution using bootstrapping as follows. For each
stimulus separately, we assigned a random vigilance state label instead
of the real one, and otherwise computed the mean gain value across
the entire dataset using the exact same procedure as for the real data.
This procedure was repeated across 10000 iterations to create the null
distribution of average gain values. The P-value associated with each
real pairwise comparison was computed by examining the percent of
random iterations that showed an average gain equal or greater than
the one observed in real data.
We also computed modulation gain factors between each pair of
vigilance states for each unit (collapsed across stimuli with significant
responses). For each pair of vigilance states and for each neuron separ-
ately, a two-tail Student t-test (P<0.05) was used to determine, if that
unit was significantly modulated in either direction. The overall con-
clusions were not affected by whether gain factors were computed per
stimulus or per neuron, given that the majority of units did not show
significant modulations (see Results).
Analysis of Stimulus-specific Adaptation Effects
Data from oddball experiments were analyzed as follows. Analysis was
limited only to cases in which both frequencies evoked significant
responses in at least the “deviant” condition to avoid cases in which
neuronal activity was not driven by both frequencies. The strength of
the LFP onset response was quantified by the depth of the maximal
negative trough of the average response in the interval 10–30 ms after
stimulus onset. Similarly, MUA onset responses were quantified by the
maximal positive peak of the average response in the interval 10–30 ms
after stimulus onset. To obtain estimates of instantaneous SUA firing
rate modulations (in order to quantify those in a comparable manner
to LFP and MUA signals above), spike trains were aligned on stimulus
onset, averaged across trials, and smoothed with a Gaussian kernel
(σ=5 ms). The strength of SUA responses in the oddball paradigm was
then quantified as the increase in firing (maximal positive peak in the
interval 10–30 ms after stimulus onset minus the baseline firing rate).
To quantify, for each frequency separately, the effect of presentation
probability (SSA), we calculated the contrast between the responses to
that frequency fi when it was standard and when it was deviant
(Fig. 9A), called the “SSA index” or SIi:
SIi¼dðfiÞ ? sðfiÞ
dðfiÞ þ sðfiÞ;
where d(fi) and s(fi) represent the peak responses to frequency fi
when it was deviant and standard, respectively (Ulanovsky et al. 2003;
Taaseh et al. 2011). In addition, the average SSA effect between
deviant and standard responses across both frequencies (Fig. 9B) was
further defined as the common SSA index (CSI):
CSI ¼dðf1Þ þ dðf2Þ ? sðf1Þ ? sðf2Þ
dðf1Þ þ dðf2Þ þ sðf1Þ þ sðf2Þ
Adult WKY rats were implanted with microwire arrays targeting
the PAC (n=6) and the motor cortex (n=3) in the right hemi-
sphere (Fig. 1). Histological verification confirmed that arrays
were indeed located within core auditory cortex (Fig. 1C). Aftera
week of recovery, sleep stabilization, and habituation to stimu-
lation, acoustic stimuli were presented as animals spontaneously
switched between vigilance states (Supplementary Fig. 2). In the
main experiment (n=10 sessions), responses to a wide battery
of stimuli included tones, clicks and click-trains, complex
environmental sounds, rat vocalizations, FM sweeps, and “chirp
AM” tones. LFPs and SUA (n=389) were recorded continuously
along with epidural EEG, EMG, and video (see Materials and
Methods foradditional information).
Prestimulus Baseline Activity Differs Across Vigilance
Vigilance states were scored in 4-s epochs on the basis of EEG,
LFP, EMG, and behavior, and each trial of sound presentation
was categorized as occurring during wakefulness (23.8±2.2%),
NREM sleep (50.6±1.5%), REM sleep (15.1±1.6%), or mixed
epochs (10.5±1.9%) which were not further analyzed. The
average duration of vigilance states was 70 s for wakefulness
(±6.9 s, median =28 s, range: 4–1584 s), 87 s for NREM sleep
(±2.9 s, median =56 s, range: 4–928 s), and 72 s for REM sleep
(±3.8 s, median = 40 s, range: 4–416 s). We first compared the
baseline activity in prestimulus intervals. As expected, muscle
tonewas highest during wakefulness (mean RMS 12.0±0.5 µV),
lower in NREM sleep (6.0± 0.2 µV), lowest during REM sleep
(5.2± 0.2 µV, Fig. 2A), and these differences were highly sig-
nificant statistically (one-way analysis of variance (ANOVA) on
mean RMS values; P< 8.29E−42, F> 106 for vigilance state).
Spontaneous neuronal firing rates were lower in sleep com-
pared with wakefulness (Fig. 2B). For example, in PAC (n =
295) firing in NREM and REM sleep was 79.9±1.6% and
77.4±2.1% of that in wakefulness, respectively (P< 0.0005,
F>7.8 via ANOVA for vigilance states). Firing rates in motor
cortex during NREM sleep (n =94) were likewise lower than
those in wakefulness (P< 10E−7 via paired t-test). Further-
more, power spectra of prestimulus baseline LFP signals
(Fig. 2C) showed many well-established properties of vigilance
states. Slow-wave activity (EEG spectral power below 4 Hz)
was maximal in NREM sleep, particularly in the frontal lobe,
while high-frequency (>30 Hz) power was maximal in wake-
fulness and REM sleep. In addition, theta (5–9 Hz) power was
higher in wakefulness and maximal in REM sleep, particularly
Preserved Responses in Auditory Cortex Across Sleep
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in the auditory cortex, probably due to its proximity to the
hippocampus. Joint distributions of EMG levels and high-/
low-frequency energy ratio in the EEG further verified proper
separation to vigilance states (Fig. 2D). Taken together, presti-
mulus baseline activity confirmed that trials categorized as
wakefulness, NREM sleep, and REM sleep, indeed, exhibited
the behavioral and electrophysiological markers associated
with these states, suggesting that normal sleep was largely pre-
served during auditory stimulation experiments.
Auditory Responses Are Largely Preserved Across Sleep
As a first step for comparing activity triggered by sounds in
wakefulness and sleep, evoked LFP responses were compared
across vigilance states (Fig. 3). Average LFP responses in audi-
tory cortex (n= 160 channels in 6 animals) had similar wave-
forms across vigilance states (Fig. 3A). However, despite the
gross similarity, the amplitude of the initial component (nega-
tive peak 10–30 ms poststimulus) was highest in NREM sleep
(−151± 12.5 µV), lower in wakefulness (−114 ±9.6 µV), and
lowest in REM sleep (−101 ±8.4 µV), and these differences
were statistically significant (ANOVA on amplitudes; P <
0.0025, F> 6.2 for vigilance state). In addition, NREM sleep
was associated with a positive peak around 240 ms following
stimulus onset (asterisk in Fig. 3A), corresponding to a poststi-
mulus suppression of neuronal activity that was observed
especially following high-intensity sounds. Average evoked
MUA responses from the same auditory channels exhibited a
clear onset response and a weaker offset response, and the in-
tensity of both these responses was parametrically dependent
on tone intensity (Fig. 3B). While sound intensity had a
marked effect on MUA responses, there was no effect of vigi-
lance state despite clear differences in baseline prestimulus
levels (horizontal cyan line in Fig. 3B). A two-way analysis of
variance (ANOVA) for MUA onset responses (10–30 ms) con-
firmed a highly significant main effect for sound intensity,
F1,102= 412, P< 3.9E−128, with no significant main effect of
vigilance state, F1,102= 0.50, P> 0.6 or interaction, F1,102=0.49,
P> 0.74. In contrast to the robust responses observed in audi-
tory cortex, LFP and MUA responses in frontal motor cortex
were weak and inconsistent (Supplementary Fig. 3) thereby
showing that responses in PAC were specific. However, the
positive peak around 240 ms following stimulus onset during
NREM sleep was observed also in motor cortex, suggesting
that this late wave, reflecting poststimulus suppression, was a
global phenomenon rather than being restricted to PAC alone.
We also examined the muscle-tone changes evoked by audi-
tory stimulation (Fig. 3C) to verify that auditory stimuli did not
regularly wake up the animals. Auditory stimulation did not
lead to long-lasting increases in muscle tone, as may be ex-
pected upon transition to wakefulness. Upon stimulation,
high-intensity (80 dB SPL) tones were associated with average
short-lasting (<100 ms) EMG changes. However, an analysis of
single trials revealed that such phasic startle-like responses
were only elicited in 3.6±1.0% of trials (Materials and
Methods). Importantly, no muscle-tone changes above base-
line were observed for 30/55 dB SPL stimuli, and all the results
reported here were also observed for these intensity levels.
Next, we examined auditory unit responses to all acoustic
stimuli in the main experiment (n= 295; 152 single units and
143 mixed clusters). Unit responses were heterogeneous and
included increases and (more rarely) decreases in firing
around onset and offset of stimuli, as well as sustained
responses throughout stimulus presentation, all of which were
detected automatically (Fig. 4). Neuronal responses often
showed selectivity to specific acoustic features of the auditory
stimuli, for example, frequency tuning, preference for specific
amplitude modulation rates, and tracking of temporal envel-
opes of the ultrasonic conspecific vocalizations (Supplemen-
tary Fig. 4); however, a detailed analysis of the precise acoustic
features driving the responses is beyond the scope of this
report. Analysis of onset response latencies (Fig. 5) revealed a
unimodal distribution with short latencies (11.6±0.3 ms). Of
295 auditory neurons, 194 (66%) exhibited a significant excit-
atory response to at least one stimulus (q(FDR)< 0.05 via t-test,
Materials and Methods). Importantly, both the proportion of
responsive neurons and their latencies are in line with recent
studies of PAC in the rat (Polley et al. 2007) and therefore
provide strong evidence that we recorded from PAC. Latencies
of onset responses were not significantly different between
vigilance states (one-way ANOVA on latency values; P> 0.2,
F= 1.6). On average, during wakefulness units responded with
onset, offset, and sustained increased responses to 21.2± 1.7%,
7.6±1.3%, and 17.5±1.8% of the suprathreshold stimuli,
respectively (without considering the near-threshold 30-dB
Single-unit responses were then compared across vigilance
states. Figure 6 shows representative auditory responses of a
single unit (see Supplementary 5 for additional examples). As
can be seen, neuronal responses were nearly indistinguishable
visually across wakefulness and sleep states. We proceeded to
compare quantitatively the selectivity as well as the magnitude
of auditory responses across states (Materials and Methods).
When comparing wakefulness with either NREM or REM sleep
separately, both selectivity and response magnitude were
largely comparable (Fig. 7). Specifically, the only case in which
we detected significant differences in selectivity across vigi-
lance states were onset responses, which were modestly (albeit
significantly) greater in NREM sleep compared with wakeful-
ness. Accordingly, there was a trend for neurons to respond
to the onset of slightly more auditory stimuli in NREM sleep
compared with wakefulness (24.5±1.9% vs. 21.2 ±1.7% of
stimuli). Given the large dataset, this difference was statistically
significant (P= 0.019 via paired t-test), but was associated with
a very weak effect size (Hedge’s g=−0.0123) (Hentschke and
Similarly, all comparisons of response magnitude across
states (quantified with spikes per second or alternatively ex-
pressed in percent of that state’s baseline firing rate—not
shown) could only reveal minor differences (e.g., maximal
difference of 4.6% and 7.2% increased gain in wake versus
NREM sleep and REM sleep, respectively, Fig. 7C). Given the
large dataset, some of the pairwise comparisons of response
magnitude reached statistical significance but were likewise
associated with weak effect sizes (maximal Hedge’s g value=
0.37). Along this line, when computing gain factors per unit
instead of per stimulus, we found that the vast majority of
neurons were not significantly modulated by vigilance state
(71–88%, depending on the pair of vigilance states under com-
parison and the type of response, i.e., onset/offset/sustained),
while a minority of units showed significant modulations in
both directions. Given the similar results across vigilance
states, we sought to verify that our analysis scheme was
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sufficiently sensitive to reveal differences when those were ex-
pected to be present. To this end, we compared response mag-
nitudes in the first block of forced wakefulness (when
neuronal firing rates were highest) with trials in NREM sleep.
As expected, this control comparison revealed robust differ-
ences of 14–22% reduction in sleep (Supplementary Fig. 6),
thereby demonstrating that the analysis employed was suffi-
ciently sensitive. On the whole, single-unit auditory responses
were largely preserved across sleep states. Differences in selec-
tivity and magnitudes were on the order of few percent, associ-
ated with weak effect sizes, and rarely reached statistical
Stimulus-specific Adaptation Is Largely Preserved Across
To examine to what extent the processing of deviant tones may
be different in sleep, we performed a second “oddball” exper-
iment (n= 8 sessions) in which two 100-ms tones were pre-
sented either frequently (standard) or rarely (deviant) (Figs 8
and 9 and Materials and Methods). Average evoked responses
tostandard anddeviant stimuliwere
qualitatively across vigilance states (Fig. 8B). LFP-evoked
responses (n =128 channels in 5 animals) had higher ampli-
tudes for deviant tones compared with standard tones. This
difference, particularly expressed immediately following tone
onset (10–30 ms), was largely comparable across wakefulness
and sleep states (further quantification below). In contrast, late
P3-like effects were absent or inconsistent in all vigilance states
including wakefulness. MUA responses (Fig. 8B) and ERPs (re-
corded from screws attached the skull) similarly showed
weaker responses following the onset of standard tones.
Next, we examined whether decreased responses to stan-
dard tones could be observed in the single-unit responses, and
whether those effects were stimulus-specific. Figure 8C shows
the responses of a representative PAC neuron during the
oddball paradigm. As can be seen, this neuron responded
strongly to low-frequency (8 kHz) tones, and to a lesser extent
to high-frequency (45 kHz) tones. Onset responses were stron-
ger for deviant stimuli, and this was especially the case for the
Next, SSA effects were quantified by computing a contrast
between the “standard” responses and the “deviant” responses
Figure 4. Types of auditory single-unit responses. Examples of different types of auditory responses to tones observed in 4 single units (A–D). In each panel, left and right columns
show responses to tones with two different frequencies. Raster plots are shown above and PSTH below. Six types of responses were identified automatically (Materials and
Methods) by considering increases and decreases in firing rates separately for onset, sustained, and offset responses. (A) This neuron responds to the 8-kHz tone (right) with a
strong increased onset response and a weaker increased offset response. (B) This neuron mainly shows increased offset responses, more strongly to the 45-kHz tone. (C) This
neuron shows decreased onset and offset responses to both tones (D) This neuron shows a sustained response to the 45-kHz tone and onset/offset responses to the 8-kHz tone.
Red and cyan bars mark responses that were automatically detected and categorized as increases or decreases, respectively.
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for the same tonal frequency, called the “SSA index” or SI
(Materials and Methods). SIs were computed separately for
LFP, MUA, and SUA (n= 225; 106 single-units and 119 mixed
clusters) responses and separately for wakefulness, NREM
sleep, and REM sleep (Fig. 9A). Across all vigilance states and
in all signals used to quantify SSA (LFP, MUA, and SUA), both
SI1 and SI2 tended to be mostly positive (gray shades in
Fig. 9A), demonstrating that SSA was present throughout
vigilance states. Finally, the common contrast between the
deviant and standard responses was used to characterize the
average effect of adaptation (CSI, see Materials and Methods).
The distribution of common contrast CSI values in different
vigilance states is shown in Figure 9B and was found to be en-
tirely overlapping. Thus, our results show that the deviant
tones elicit early SSA effects whose strength was similar across
wakefulness and sleep states.
In this study, we show that single-unit and LFP responses in
core auditory cortex of unrestrained rats are comparable across
wakefulness, NREM sleep, and REM sleep. Despite robust
changes in baseline neuronal firing, baseline LFP power
spectra and muscle tone across vigilance states, auditory
responses across the neuronal population were largely pre-
served. Comparable results were revealed for response selec-
tivity and response magnitude (when considered relative to
that state’s baseline firing rate or in terms of spikes per
second), and this was true across different auditory stimuli
(simple tones/clicks versus complex behaviorally relevant
stimuli). All differences in selectivity and response magnitude
between states were minimal (<8%) and the vast majority of
comparisons did not reach statistical significance. We also
examine for the first time the level of SSA following the onset
of repetitive tones in natural sleep and demonstrate that the
strength of such early (10–30 ms) effects in sleep (13–20% with
our protocol) are comparable to those occurring during wake-
fulness. Taken together, the results demonstrate that despite
the high arousal threshold brought about by sleep and the
robust changes in electrical activity and neuromodulation
between vigilance states, auditory responses in PAC are pre-
served—thereby supporting the notion that neuronal activity
in primary sensory cortices is primarily driven by external
physical stimuli with little modulation by vigilance state.
Comparison to Previous Studies
The present results differ from several previous studies in pri-
mates and cats that reported attenuated visual and somatosen-
sory responses during NREM sleep (Evarts 1963; Gucer 1979;
Livingstone and Hubel 1981). Similarly, early studies in the
auditory cortex of cats and primates found neurons to be less
responsive during NREM sleep (Murata and Kameda 1963;
Brugge and Merzenich 1973). Rather, the current results
extend the few recent single-unit studies in auditory cortex of
guinea pigs and marmoset monkeys during natural sleep
(Pena et al. 1999; Edeline et al. 2001; Issa and Wang 2008) that
demonstrated comparable responses across vigilance states
(Hennevin et al. 2007). One recent study (Issa and Wang 2011)
demonstrated that NREM sleep reduced the sensitivity of
auditory cortex to quiet sounds and reduced the extent of
sound-evoked response suppression. In an attempt to consider
these possibilities, we presented all sounds also at near-
threshold levels of 30 dB SPL. However, the results did not
reveal differences between vigilance states for such responses
(see, e.g., black traces in Fig. 3B for MUA). Response suppres-
sions were rare in our data and precluded systematic investi-
gation of this aspect.
It is interesting to note the difference between evoked
potentials and single-unit firing with respect to the comparison
Figure 5. Latency of auditory single-unit onset responses. (A) Raster plot of an
example single-unit excitatory onset response to 100-ms tone pips (8 kHz, 55 dB SPL).
Each row is a trial (#1–120). (B) Corresponding peristimulus time histogram. The
latency of onset responses was defined as in (Polley et al. 2007) as the intersection
between the slope of the excitatory response profile (oblique green line) and the mean
prestimulus baseline firing rate (horizontal red line). (C) Distribution of onset latencies
across all auditory neurons exhibiting an excitatory onset response (n=195). Red,
green, and blue denote latencies during wakefulness, NREM sleep, and REM sleep,
respectively. Black arrow denotes mean latency across all vigilance states (11.6 ms).
Note that the distribution of response latencies is unimodal with 98% of latencies
between 8 and 16 ms, in line with expected latencies in auditory core regions (Polley
et al. 2007).
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between vigilance states. The present results (Figs 3A and 8B)
join previous studies in showing that NREM sleep is associated
with larger amplitude-evoked potentials compared with wake-
fulness and REM sleep (Hall and Borbely 1970; Velluti 1997;
Phillips et al. 2011), while single-unit responses do not exhibit
such clear differences. Similarly, evoked potentials in human
scalp EEG exhibit higher amplitudes during NREM sleep in
response to auditory (Bastuji and Garcia-Larrea 1999; Colrain
and Campbell 2007; Hennevin et al. 2007) or transcranial mag-
netic stimulation (Massimini et al. 2007). Given that the LFP is
considered to reflect synaptic input (Mitzdorf 1985; Logothetis
et al. 2001), a possible explanation is that during NREM sleep,
synaptic input may not lead to differences in spiking activity.
However, we believe that the discrepancy stems primarily from
a difference in spatial scale—since the LFP captures combined
neuronal activity in a volume of tissue (Nir et al. 2007, 2008). It
is therefore possible that the response to the same stimulus re-
cruits a larger neuronal population in NREM sleep, thereby
giving rise to a larger amplitude potential in the LFP. Indeed,
neurons were found here to be slightly, albeit significantly, less
selective in NREM sleep, in accord also with human data
showing decreased selectivity of auditory processing during
NREM sleep (Perrin et al. 1999, 2002). Perhaps many neurons
that contribute to an enhanced evoked response during NREM
sleep are relatively inaccessible to extracellular recordings
(e.g., smaller neurons in superficial layers with sparse
firing). More generally, the LFP is more difficult to interpret
than single-unit firing because of inherent summation of
potentials from local and nonlocal sources and from different
contributors (Buzsaki et al. 2012). Indeed, estimates of the size
of the neuronal population giving rise to the LFP are highly
disparate—ranging from several hundred micrometers [e.g.,
Katzner et al. (2009)] to a few millimeters [e.g., Logothetis et al.
(2001)]. In addition, while postsynaptic currents are believed
to be the most ubiquitous contributors to the LFP, many nonsy-
naptic currents may play important roles, including spike after
hyperpolarizations and “down” states that are prevalent during
NREM sleep (Buzsaki et al. 2012; Reimann et al. 2013). Future
studies are needed to further explain the discrepancy between
enhanced evoked responses in LFP/EEG and preserved single-
unit spiking during NREM sleep.
The emerging notion from this and other recent single-unit
studies is that both selectivity and magnitude of responses
across the PAC population are comparable in both NREM and
REM sleep to those observed during wakefulness, and this is
the case for both simple stimuli (e.g., tones, clicks) as well as
complex behaviorally relevant stimuli. In addition, variability
exists between individual neurons in relation to the presence
and direction of modulation between vigilance states, as re-
ported here. The reasons underlying the discrepancy with
Figure 6. Representative auditory single-unit responses across vigilance states. (A) Representative auditory responses of a single-unit across vigilance states (rows) for 9 different
stimuli (columns). Rows (top to bottom) correspond to stimuli names and intensities, timing and structure of acoustic stimulus (pink over cyan), followed by raster plots and PSTHs
for each vigilance state. Inset on upper left shows mean±SEM of action potential waveform. W*, first wake trials; W, wakefulness trials; N, NREM sleep trials; R, REM sleep trials.
Firing rate in all bar graphs is expressed in terms of percent of wakefulness baseline and is shown with the same scale across all states and stimuli. Note that neuronal responses
are nearly indistinguishable visually across vigilance states.
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earlier studies are not clear but one potential factor that could
account for this variability isthe laminar location of recordings.
The present recordings were likely from granular and infragra-
nular layers, although the precise laminar profile of each re-
cording site is not available (see also Limitations).
Responses to Deviant Sounds in Oddball Paradigm
The processing of deviant tones was further compared in sleep
and wakefulness using an oddball paradigm. The results reveal
that the strength of SSA effects (13–20% with our protocol)
were comparable across vigilance states thereby showing, for
the first time, that early markers of deviance detection at the
neuronal level persist during NREM and REM sleep states.
Such comparable effects extend a growing body of literature
suggesting that SSA in auditory cortex (and early deviance
detection more generally) is not significantly modulated by be-
havioral state. Along this line, SSA in auditory cortex is readily
observed in anesthetized cats (Ulanovsky et al. 2003), is ob-
served in awake (von der Behrens et al. 2009; Farley et al.
2010) as well as anesthetized (Taaseh et al. 2011) rats, and
evoked potentials recorded over cat auditory cortex reveal
similar markers of deviance detection in wakefulness and
NREM sleep (Csepe et al. 1987). SSA has been shown to be
weak, rare, or nonexistent among neurons of the lemniscal
pathway providing cortical input (Ulanovsky et al. 2003;
Antunes et al. 2010), so that SSA in PAC (as measured here)
is believed to be generated within cortex by local mech-
anisms (Ulanovsky et al. 2003) [but see also Antunes and
Malmierca (2011)]. Depletion of synaptic vesicles in specific
thalamocortical synapses may be an underlying mechanism, as
SSA is expressed strongly already in thalamorecepient cortical
layers (Szymanski et al. 2009).
Although early studies of SSA in the cat suggested that it
could fully explain the generation of MMN, accumulating evi-
dence suggests that this is not the case. Instead, single-unit SSA
in PAC may lie upstream from neuronal processes generating
the MMN and may contribute to earlier changes in the P1–N1
complex and/or other midlatency potentials (Nelken and
Ulanovsky 2007; Winkler et al. 2009; Farley et al. 2010; Naata-
nen et al. 2011). This may help resolve the apparent discre-
pancy with the observation that in humans MMN is modulated
as a function of vigilance state. Indeed, MMN is attenuated in
sleep (Loewy et al. 1996; Ruby et al. 2008), under anesthesia
(Heinke et al. 2004), and may undergo complex changes in
patients with disorders of consciousness, although this is cur-
rently under debate (Bekinschtein et al. 2009; Boly et al. 2011;
King et al. 2011).
No late effects such as MMN or P3-like potentials were ob-
served in any state (including wakefulness) nor did we observe
long-lasting changes in induced LFP power suggestive of rever-
beratory activity (not shown). The absence of late effects is in
agreement with recent single-unit (von der Behrens et al.
2009) and ERP (Umbricht et al. 2005) studies in awake rodents
but at odds with earlier studies describing differences occur-
ring at 60–300 ms after stimulus onset (Yamaguchi et al. 1993;
Shinba 1997; Ruusuvirta et al. 1998). Both stimulation par-
ameters (e.g., probability of deviant tones and ISIs) and the
passive oddball paradigm employed here may account for
the absence of late P3-like effects, given that P3 amplitudes in
Figure 7. Quantitative comparison of auditory single-unit responses across vigilance states. (A) Quantitative comparison of auditory single-unit responses (n=295) in wakefulness
and NREM sleep. Columns (left to right) depict results for onset, offset, and sustained responses. Top row: scatter plot of selectivity (number of stimuli that trigger a response) in
NREM sleep (y-axis) versus wakefulness (x-axis). Each dot denotes one auditory neuron (n=165, 113, and 149 for onset, offset, and sustained responses in wakefulness,
respectively). Middle row: scatter plot of response magnitudes (spikes per second) in NREM sleep (y-axis) versus wakefulness (x-axis). Each dot denotes the response of one
neuron to a specific stimulus for which significant responses were identified in both vigilance states of interest (n=1345, 209, and 356 conditions for onset, offset, and sustained
responses, respectively). Red and green lines as above. Bottom row: distribution of gain factors computed for each stimulus separately. Vertical green line marks zero gain while
percentage at top right corner shows the mean gain factor (none of these mean gain factors were significantly different than zero when evaluated via bootstrapping, see
Supplementary Fig. 7). Positive (negative) gain values denote increased response magnitude in sleep (wakefulness). (B) Wakefulness versus REM sleep with format as in (A). Note
that by and large single-unit responses in sleep retain comparable selectivity and response magnitudes.
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Figure 8. Oddball paradigm and stimulus-specific adaptation (SSA). (A) A schematic description of one block used in the oddball paradigm (each such block is repeated multiple
times). A block consists of part A and part B. In each part, either f1 (8 kHz, 55 dB SPL) or f2 (45 kHz, 55 dB SPL) are presented pseudorandomly according to their probability of
occurrence (90% for standard and 10% for deviant). (B) Average LFP responses (top), MUA responses (middle), and EEG responses (bottom) for standard (black) and deviant (red)
stimuli across different vigilance states (columns); n=69300 standard trials (16632, 35343, and 10395 in Wake, NREM, and REM sleep, respectively) and 7700 deviant trials
(2333, 4957, and 1458 in Wake, NREM, and REM sleep, respectively), 8 recording sessions in 5 animals. Note that an enhanced early response to deviant tones persists across
sleep states. (C) Representative responses of one single unit during the oddball paradigm across all vigilance states. The raster plot shows 4050 presentations for the standard tone
(top) and 450 presentations for the oddball tone (bottom), separately for f1 (left) and f2 (right). Red, green, and blue ticks denote action potentials during wakefulness, NREM sleep,
and REM sleep, respectively. Line graphs (bottom) show PSTHs smoothed by a 5-ms Hamming window. Note that this neuron responds preferentially to f1 but exhibits enhanced
early responsesto both stimuli. Horizontal green bars mark stimulus.
Preserved Responses in Auditory Cortex Across Sleep
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the rat are markedly enhanced in active conditions when rare
tones are associated with rewards or aversive outcomes
through training (Shinba 1997; Sambeth et al. 2003).
On the whole, given that comparable SSA effects were
found here across wakefulness and sleep in the absence of late
MMN or P3-like potentials, the results support the notion that
Figure 9. Quantitative comparison of SSA across vigilance states. (A) Scatter plots of SI2(SSA index for f2=45 kHz, 55 dB SPL), versus SI1(SSA index for f1=8 kHz, 55 dB
SPL) for neuronal responses measured in LFP , MUA, and SUA (columns, left to right) separately during wakefulness, NREM sleep and REM sleep (rows, top to bottom). Note that
most responses show some SSA for both f1 and f2 (upper right quadrant in each scatter plot, gray shade. (B) Histograms of common SSA index (CSI across both frequencies,
Materials and Methods) for LFP , MUA, and SUA (columns, left to right). Red, green, and blue traces mark the superimposed distributions for wakefulness, NREM sleep, and REM
sleep, respectively. Note that SSA indices are comparable across vigilance states.
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SSA is a marker of neuronal activity occurring earlier than
global integrative processes generating the MMN. Thus, com-
parable SSA across wakefulness and sleep is best interpreted as
evidence that processes occurring within 50 ms of stimulus
onset within core auditory cortex are primarily driven by phys-
ical stimuli with little modulation by vigilance state.
Relation to Functional Imaging Studies in Humans
In humans, evoked potential studies have shown robust acti-
vation in response to sounds during sleep (Nielsen-Bohlman
et al. 1991; Bastuji et al. 2002; Cote 2002; Colrain and Campbell
2007; Hennevin et al. 2007). Similarly, most other fMRI studies
of auditory stimulation during NREM sleep (Portas et al. 2000;
Dang-Vu et al. 2011) have found that in auditory regions along
temporal cortex, responses resemble those found during wake-
fulness. On the other hand, a few studies report reduced activity
in auditory cortex during sleep (Czisch et al. 2002, 2004), and
others have noted that stimulation at times when sleep spindles
occur may result in different responses (Dang-Vu et al. 2011;
Schabus et al. 2012). Byand large, the present single-unit results
confirm the former set of findings at the neuronal level, but con-
flicting results and their relation to single-unit measures are diffi-
cult to resolve since BOLD fMRI measurements reflect average
activity over large neuronal populations and over time intervals
of seconds. Indeed, fMRI in human auditory cortex primarily re-
flects distributed correlated activity (Nir et al. 2007) while the
present results and other single-unit data show that, at a fine
neuralscale, sleep affects auditory cortical responses ina hetero-
geneous manner (Pena et al. 1999; Edeline et al. 2001; Issa and
Wang 2008). Temporally, auditory stimulation and particularly
high-intensity (loud) sounds used in many imaging studies
(given MR scanner noise) can often lead to an induced poststi-
mulus “OFF” period as we observe here (Fig. 3B). In such cases,
BOLD signals in sleep may often reflect integration over a (pre-
served) response and a poststimulus suppression.
Overall, human studies suggest that the main differences
between wakefulness and sleep are found at later processing
stages, in terms of both timing and cortical hierarchy, and may
depend on altered intercortical connectivity. With fMRI, it was
found that despite comparable responses in PAC, auditory
stimuli are not as effective in driving the activity of high-order
(e.g., parietal and prefrontal) regions (Portas et al. 2000). Also,
despite the existence of human ERPs to oddball stimuli in sleep
(Niiyama et al. 1994, 1999, 2000; Ruby et al. 2008), the magni-
tude and spatial extent of late P3 may be reduced during sleep
(Cote 2002; Colrain and Campbell 2007). Along this line, a func-
tional cortical disconnection during sleep (Massimini et al.
2005) may prevent activity in primary sensory regions from ef-
fectively driving higher order cortical regions. Modeling studies
suggest that NREM sleep may be associated with diminished in-
tercortical transmission (Esser et al. 2009), possibly mediated by
local OFF periods (Niret al. 2011) that could block signal propa-
gation. In REM sleep, there also seems to be dissociation of
primary visual cortex (V1) from high-order visual regions
(Braun et al. 1998). Overall, the possibility that primary sensory
regions cannot effectively drive high-orderactivity in sleep high-
lights the importance of recording sensory responses simul-
taneously from multiple cortical areas in future studies.
Sleep and Anesthesia
Both sleep and anesthesia entail behavioral and perceptual dis-
connection from external sensory stimuli. In addition, under
anesthesia and in NREM sleep, hyperpolarized neurons in the
thalamocortical system become bistable and give rise to slow
oscillations (Steriade et al. 1993, 2001). For these reasons and
for practical considerations (such as better control and easier
data collection), results from anesthetized preparations are
often used as an analogy to sleep (Timofeev et al. 1996). An-
esthesia has been shown to reduce responses in auditory
cortex, especially for sustained responses (Brugge and Merze-
nich 1973; DeWeese et al. 2003) and this raises the possibility
that similar processes (along with possible thalamic gating)
underlie sleep disconnection. How could the comparable
PAC responses reported here (observed also for sustained
responses) be explained in light of these findings? Using
the state of anesthesia as a model for natural sleep is an over-
simplification [see Hennevin et al. (2007) for review]. First, an-
esthesia is heterogeneous and may lead to disconnection and
unconsciousness via distinct pathways. While certain “dissocia-
tive” anesthetics such as ketamine lead to unresponsiveness
with some preserved conscious dream-like experience, other
anesthetics including volatile agents, urethane, and barbi-
turates globally deactivate brain activity in a manner that
is more akin to NREM sleep (Alkire et al. 2008). Second,
studies directly comparing responses in natural sleep with
GABA-agonist anesthetics in rodents and cats have noted impor-
tant difference in responses (Cotillon-Williams and Edeline
2003), and it is especially clear that responses during REM sleep
exhibit marked differences in comparison to anesthesia (Kishi-
kawa et al. 1995; Torterolo et al. 2002). Indeed, in REM sleep,
the thalamocortical system is depolarized, neurons fire tonically,
and low-amplitude high-frequency activity prevails in the LFPs
and in the EEG. Despite this strong cortical activation, a high
arousal threshold persists in REM sleep. Thus, disconnection
during sleep, and REM sleep in particular, cannot be explained
by considering anesthesia. Given the strong cortical activation,
REM sleep disconnection poses a highly intriguing unsolved
Identification of Cortical Auditory Fields
Core (primary) auditory cortex was identified based on his-
tology, neural responsiveness, and short unimodal latency dis-
tribution but the definition did not include a full tonotopic
mapping or a comparison of responses to pure tones versus
noise stimuli. However, this limitation should not affect the
conclusions since 1) the vast majority of responses are in PAC,
so conclusions about PAC should not be affected by additional
recordings outside PAC; 2) if cortical recording sites that were
responses across wakefulness and sleep, it may strengthen the
notion that in PAC responses are largely preserved.
Sounds were played in a free-field configuration as unrest-
rained animals were behaving freely in their home cages. Con-
sequently, slight differences in location and posture may affect
the exact levels of sounds in each trial. This may have poten-
tially confounded a result of a significant difference between
vigilance states (e.g., if during sleep a typical posture leads to
weaker stimulation) but given the observed comparable
responses this limitation should not affect the conclusions.
Preserved Responses in Auditory Cortex Across Sleep
• Nir et al.
by guest on December 9, 2013
Our extracellular recordings were not ideal for reliable dis-
crimination between the discharges of excitatory and inhibi-
tory neurons, nor could the precise laminar location be
determined in each recording site. Based on 1) those cases
where histology permitted identification of laminar locations,
2) baseline firing rates, 3) the proportion of responsive
neurons, and 4) the polarity of locally recorded sleep slow
waves in comparison to those recorded on the skull, it is likely
that most recordings tended to be in middle/infragranular
layers (Sakata and Harris 2009). Future studies employing
laminar recordings during natural sleep could determine
whether specific neuronal populations may modulate their
activity patterns as a function of vigilance states. With regards
to stability of recordings, the sleep in rodents provides an
advantage over studies in primates and humans, since vigi-
lance states were interleaved throughout the recording session
rather than one state typically occurring before another (e.g.,
wakefulness before NREM sleep) thereby confounding a com-
parison of states with stability issues.
Potential Inattention During Wakefulness
It could be that the comparable auditory responses observed
here can be explained in part by a state of inattentiveness
during wakefulness, since sounds were not associated with be-
havioral outcomes (reward/aversive outcome) through train-
ing. In animal studies, the effects of attention in auditory
cortex seem weak or variable (Miller et al. 1972; Benson and
Hienz 1978) but attention can robustly modulate activity in
human auditory cortex, especially when directed toward other
sensory events such as visual stimuli (Fritz et al. 2007; Zatorre
2007). Ultimately, this issue can be best clarified by conducting
human studies in which cognitive variables are carefully ma-
nipulated and compared with the effects of vigilance states in
the same participants.
Functional Significance and Future Directions
The present results join other studies (Pena et al. 1999; Edeline
et al. 2001; Issa and Wang 2008) in suggesting that ascending
signals along the auditory pathway drive overall activity in PAC
during sleep much as they typically do during wakefulness.
One possibility is that this may not be the case in other modal-
ities (i.e., somatosensory, visual) where responses in primary
cortical regions may be attenuated (Evarts 1963; Gucer 1979;
Livingstone and Hubel 1981). If so, audition may serve a
unique adaptive protective role by monitoring distant events
in the environment during an otherwise disconnected state
of sleep. Alternatively, comparable activity in primary sensory
regions across all modalities is not sufficient for external
sensory to be regularly perceived and elicit behavioral re-
sponses during sleep. Cortical functional disconnection, poss-
ibly triggered by a distinct neuromodulatory milieu in sleep,
may prevent activity in primary sensory regions to effectively
drive high-level regions and future studies are required to
investigate signal propagation simultaneously across cortical
regions and layers. It remains to be seen whether similar mech-
anisms underlie disconnection in NREM and REM sleep
despite their different electrical and neuromodulatory activi-
ties. One aspect that holds true for both NREM and REM sleep
is reduced activity of the locus coeruleus–norepinephrine (LC–
NE) system (Jones 2005), and this may play a key role in
maintaining disconnection. Indeed, during wakefulness LC–
NE activity is implicated in orienting toward behaviorally rel-
evant sensory stimuli and is related to evoked potentials such
as the P3 (Nieuwenhuis et al. 2005) which are well correlated
with perceptual awareness (Dehaene and Changeux 2011).
Overall, the present results provide a “lower bound” on the
search for mechanisms that underlie sleep disconnection, in
terms of candidate brain regions and time intervals in which
processing differences may occur. Understanding if, where,
when and in what ways processing of sensory stimuli differs in
sleep will help explain the disconnection that is a defining
feature of sleep. More generally, it can help elucidate the
effects of state-dependent cortical processing, the relation
between consciousness and underlying brain activity, and the
neural basis of neurological and psychiatric disorders that
involve disconnection including attention disorders, dementia,
schizophrenia, autism, and disorders of consciousness.
Supplementary can be found at: http://www.cercor.oxfordjournals.
This work was supported by the National Institute of Health
Director’s Pioneer Award to G.T., the Human Frontier
Science Program Organization long-term fellowship to Y.N.,
and the I-CORE Program of the Planning and Budgeting Com-
mittee and The Israel Science Foundation (grant No. 51/11)
We thank John Brugge, Israel Nelken, and Giovanni Santostasi for help
with acoustic setup and calibration. Paul Pool for AM and FM sounds.
Justin Williams, Tom Richner, and Sarah Brodnick for electrodes and
surgical procedures. Peter Magill for DiI protocols. Marie-Eve Trem-
blay, Luisa De-Vivo, and Martha Pfister-Genskow for help with his-
tology. Melissa Luck for soundproof chamber. Chadd Funk for
comments on earlier drafts. Conflict of Interest: None declared.
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