the midbrain, thalamus, and auditory cortex (AC). It has been proposed that the SSA observed subcortically originates in the AC as a
higher-order property that is transmitted to the subcortical nuclei via corticofugal pathways. Here we report that SSA in the auditory
thalamus of the rat remains intact when the AC is deactivated by cooling, thus demonstrating that the AC is not necessary for the
the detection of rare stimuli is reflected by stimulus-specific ad-
aptation (SSA), in which neurons respond strongly to rare stim-
ulus while adapting to frequently occurring ones (Ulanovsky et
al., 2003; Gutfreund and Knudsen, 2006; Reches and Gutfreund,
2008; Malmierca et al., 2009; von der Behrens et al., 2009; An-
of stimulation and on mechanisms operating at the inputs of the
at the output of the neuron (Ulanovsky et al., 2003, 2004). Neu-
rons showing SSA integrate sensory information to create a pre-
tory memory, recognition of acoustic objects, and scene analysis
(Nelken, 2004; Winkler et al., 2009). SSA to the frequency of the
acoustic stimulus has been identified in the midbrain, thalamus,
and cortex (Ulanovsky et al., 2003; Pérez-González et al., 2005;
Gutfreund and Knudsen, 2006; Reches and Gutfreund, 2008;
2010; Reches et al., 2010; Taaseh et al., 2011). SSA is strong in
nonlemniscal subcortical regions (Pérez-González et al., 2005;
Malmierca et al., 2009; Antunes et al., 2010), but the primary
spread and strong (Ulanovsky et al., 2003). Thus, SSA has been
processing that can be transmitted to subcortical nuclei in a top-
down manner (Nelken and Ulanovsky, 2007). However, this hy-
pothesis remains untested.
In the present study, we tested the hypothesis that SSA in
thalamic neurons is inherited via corticofugal projections. A re-
projections it receives. In the auditory thalamus [the medial
geniculate body (MGB)], these projections outnumber the as-
cending projections by a factor of 10 (Winer et al., 2001; Kimura
et al., 2005; Lee and Winer, 2005; Ojima and Rouiller, 2011) and
strongly modulate the responses of MGB neurons (Ryugo and
are small and modulatory in nature. A numerically smaller cor-
ticothalamic projection arises from layer V neurons and pro-
properties of the postsynaptic neuron (Rouiller and Welker,
1998; Bartlett et al., 2000).
by cooling (Ryugo and Weinberger, 1976; Villa et al., 1991;
Lomber, 1999; Lomber et al., 1999) while stimulating the animal
(Ulanovsky et al., 2003; Antunes et al., 2010). Our results dem-
onstrate that the responses of MGB neurons were significantly
modified, but the SSA levels and their temporal dynamics were
Author contributions: F.M.A. and M.S.M. designed research; F.M.A. performed research; F.M.A. and M.S.M.
JuntadeCastillayLeo ´n-Unio ´nEuropea(GrantGR221)toM.S.M.F.M.A.heldafellowshipfromtheSpanishMinisterio
helpful assistance and suggestions in setting up the cooling technique. We also thank Drs. Israel Nelken, Daniel
17306 • TheJournalofNeuroscience,November23,2011 • 31(47):17306–17316
mostly unaffected during AC deactivation. These findings dem-
onstrate that SSA in the MGB is not inherited from the AC, but
rather, the AC modulates the responses of MGB neurons, pre-
presented previously (Antunes and Malmierca, 2011).
Surgical procedures and electrophysiological recordings. Experiments were
performed on 17 female adult pigmented rats (Rattus norvegicus; Long–
Evans) with body weights between 150 and 250 g. All experiments were
methods conforming to the standards of the University of Salamanca
Animal Care Committee. Details of surgical and recording procedures
were as described in detail previously (Antunes et al., 2010). Surgical
anesthesia was induced and maintained with urethane (1.5 g/kg, i.p.; 0.5
g/kg, i.p., supplementary if needed). A craniotomy was performed to
expose the cerebral cortex overlying the MGB and the ipsilateral AC. A
tungsten electrode (1–2 M?) (Merrill and Ainsworth, 1972) was used to
record extracellular single-unit responses. Stimuli were delivered
through a sealed acoustic system (Rees, 1990; Malmierca et al., 2005,
ogies) driven by two ED1 (Tucker-Davis Technologies) modules. All
stimuli were generated and delivered to the contralateral ear using TDT
System 2 (Tucker-Davis Technologies) hardware and custom software
(Malmierca et al., 2009). The output of the system at each ear was cali-
brated in situ using a one-quarter inch condenser microphone (model
4136, Bru ¨el & Kjær) and a DI-2200 spectrum analyzer (Diagnostic In-
struments). The maximum output of the Tucker-Davis Technologies
system was flat from 0.3 to 5 kHz (?100 ? 7 dB SPL) and from 5 to 40
limited to 40 kHz. The second and third harmonic components in the
signal were ?40 dB below the level of the fundamental at the highest
output level (Malmierca et al., 2008, 2009). Action potentials were re-
output of which was further amplified and bandpass filtered (PC1,
Tucker-Davis Technologies; between 0.5 and 3 kHz) before passing
through a spike discriminator (SD1, Tucker-Davis Technologies). Spike
times were logged on a computer by feeding the output of the spike
discriminator into an event timer (ET1, Tucker-Davis Technologies)
synchronized to a timing generator (TG6, Tucker-Davis Technologies).
The monaural frequency response area (FRA) was obtained automati-
cally using a randomized stimulus presentation paradigm (frequency
steps and intensity levels) and plotted using MATLAB. Stimuli to mea-
sure FRAs in single units were 75 ms duration pure tones (5 ms rise/fall
time). Frequency and intensity of the stimulus were varied randomly
0.1 to 40 kHz, to cover approximately two to three octaves above and
Stimulus presentation paradigm. Stimuli were presented in an oddball
paradigm similar to that used previously (Ulanovsky et al., 2003; Malm-
ierca et al., 2009; Antunes et al., 2010), consisting of two different pure-
tone stimuli ( f1and f2), at the 10–40 dB level above threshold. Both
frequencies were within the excitatory FRA previously determined for
the neuron and had a frequency ratio of 0.141 octaves [i.e., a normalized
frequency difference (?f) ? 0.10, where ?f ? ( f2? f1)/( f2* f1)1/2]
(Ulanovsky et al., 2003; Malmierca et al., 2009; Antunes et al., 2010). A
train of 400 stimuli containing both frequencies in a pseudo-random
order, at a 4 Hz repetition rate, was presented, varying the probability of
each frequency, as follows: f1was presented with 90% probability (stan-
ulus) within the sequence. A second train was then presented, in which
the probabilities of the two stimuli were reversed ( f2as standard; f1as
response were due to the statistics of the stimulus ensemble. The param-
eters chosen were demonstrated to effectively elicit SSA in the MGB
neurons (Antunes et al., 2010). SSA was quantified as described previ-
ously (Ulanovsky et al., 2003; Malmierca et al., 2009; Antunes et al.,
2010). The frequency-specific SSA index (SI), SI( fi) (i ? 1 or 2), was
calculatedasSI( fi)?[d( fi)?s( fi)]/[d( fi)?s( fi)]whered( fi)ands( fi)
were responses (in spike counts/stimulus) to frequency fiwhen it was
deviant or standard, respectively. The amount of SSA for both frequen-
cies [common SSA index (CSI)] was calculated as CSI ? [d( f1) ?
d( f2) ? s( f1) ? s( f2)]/[d( f1) ? d( f2) ? s( f1) ? s( f2)]. These indices
reflect the extent to which the response to a tone, when standard, was
smaller than the response to the same tone, when deviant. The indices
deviant, was greater than the response to the same tone, when standard.
Reversible cooling AC deactivation. We recorded well isolated single
units from the MGB before, during, and after deactivating the ipsilat-
eral AC using the cooling technique (Fig. 1). This technique enables
the reversible deactivation of discrete regions of the brain (Lomber,
1999; Lomber and Malhotra, 2008; Carrasco and Lomber, 2009a,b;
interactions between cortical areas in cat (Ghosh et al., 1994; Lomber,
1999; Lomber et al., 1999; Lomber and Malhotra, 2008; Carrasco and
Lomber, 2009a,b) and the corticofugal modulation of subcortical nu-
clei in various sensory systems. In smaller animals, such as the guinea
pig, cooling has been demonstrated to reliably deactivate the AC
without causing a reduction in temperature sufficient to directly re-
duce neural activity in the MGB and other subcortical nuclei
(Coomber et al., 2011). It is assumed that ipsilateral AC cooling de-
activated all the descending inputs to the MGB, since corticofugal
fibers terminating in this nucleus are entirely ipsilateral (Bajo et al.,
1995, 2010; Ojima and Rouiller, 2011).
surface (Lomber et al., 1999). The loop was stereotaxically positioned
(Doron et al., 2002). The whole AC was deactivated to avoid incomplete
effects due to partial deactivation. Cooling was achieved by pumping
methanol cooled by dry ice through the cryoloop using a peristaltic
temperature of 3 ? 1°C (Lomber et al., 1999). A stable cortical temper-
ature was reached within ?5 min of cooling initiation (Fig. 1a). Record-
ings were started 5 min later to ensure deactivation of the deeper AC
layers where the corticofugal projection to the MGB originates (mostly
from layer VI with a minor proportion from layer V) (Bajo et al., 1995;
Bartlett et al., 2000; Kimura et al., 2007; Ojima and Rouiller, 2011).
cortex beneath the cryoloop (Lomber et al., 1999, 2007) as it places the
sion at the gray matter–white matter interface or deeper (Lomber et al.,
V/VI in rat (Games and Winer, 1988) by the cessation of activity, as
were measured in the MGB during AC cooling to confirm that the tem-
perature in the MGB was not reduced to 24–20°C, the range of temper-
atures sufficient to diminish neuronal function (Lomber et al., 1999)
(Fig. 1a). These measurements were made with an Omega 5SC-TT-KI
thermo-probe, composed of two wires (76 ?m diameter and plastic in-
SSA) were recorded during ?15 min of AC deactivation, after which the
cryoloop pump was turned off (Fig. 1). The normal AC surface temper-
took longer to return to normal (Nakamoto et al., 2008; Coomber et al.,
2011). After 30 min, the responses of the neurons returned to normal,
since cooling disrupts neither the structural nor functional integrity of
the cooled area (Lomber et al., 1999; Yang et al., 2006) (Fig. 1). The few
MBG neurons (3 of 51) that did not recover to within 20% of the pre-
cooling maximum spike count were removed from all analyses. In some
neurons, a moderate higher firing rate was observed in the recovery, a
AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamusJ.Neurosci.,November23,2011 • 31(47):17306–17316 • 17307
rebound presumably due to a higher synaptic
release probability during rewarming (Volgu-
shev et al., 2004).
Data analysis. Statistical tests were per-
formed using the nonparametric (two-sided)
Wilcoxon signed rank test to test differences
between distribution medians of varying con-
using two-way repeated-measures ANOVAs
(one-factor repetition; repeated factor was
“condition”). Correlation coefficients were
performed using Pearson’s correlation. Sta-
tistical tests were considered significant
when p ? 0.05. The SD for the CSI of each
strapping (1000 repetitions). The limits of
the 99% confidence intervals were calculated
by using the 0.5 and 99.5 percentiles of the
CSI bootstrap distribution obtained for each
neuron; the 1% confidence level was used to
determine statistically significant differences
in the CSI value between conditions. The
analysis and figures were done using Sigma-
plot 11 (Systat Software) and Matlab
Histological verification of recording sites.
Each track was marked with electrolytic le-
sions for subsequent histological localization
of the recording sites. At the end of each ex-
periment, the animal was perfused transcar-
dially with 0.5% NaNO3in PBS followed by
fixative (1% paraformaldehyde and 1% glu-
tissue was sectioned in the transverse plane
into 40-?m-thick sections that were Nissl
stained with 0.1% cresyl violet. Recording sites
were marked on standard sections from a rat
brain atlas (Paxinos and Watson, 2005), and
units were assigned to one of the three main
divisions (ventral, dorsal, and medial) of the
MGB (Winer et al., 1999).
we recorded the responses of 51 well isolated single units in the
MGB, before, during, and after reversibly deactivating the AC by
in the MGB while the AC was cooled (Fig. 1). There was a small
drop in the MGB temperature (?5°C), but it never fell to 26°C
SSA in the MGB neurons, we used trains of two different pure-
tone stimuli in an oddball paradigm ( f1and f2; 400 stimulus
presentations in two blocks) (Figs. 2, 3), presenting each tone
with different probability of occurrence in the sequence (90%,
ditions that elicited the most SSA in the MGB neurons (a fre-
et al., 2010). To quantify SSA, we calculated the SI( fi) and CSI
tone, when standard, was smaller than the response to the same
positive if the response to a tone, when deviant, was greater than
the response to the same tone, when standard [compare Figs.
stimuli; i.e., did not show SSA)]. Here, we found a similar distri-
bution of values across the population to that found in our pre-
vious work, for the same set of conditions (Antunes et al., 2010).
All but three neurons showed full recovery following AC de-
activation (48 of 51 neurons) (Figs. 1–4); these 3 neurons were
excluded from subsequent analysis. Of the 48 neurons recorded,
30 demonstrated SSA (CSI ?0.18) (Antunes et al., 2010). Our
main finding is that these neurons had significant SSA levels
during cortical deactivation (Figs. 1, 2, 3a–l, 4). However,
other response properties of the MGB neurons that we tested
simultaneously did change significantly under AC deactivation.
These included spectral response patterns (Figs. 2, 3, FRAs),
spontaneous activity (the units in Fig. 2 reduced their spontane-
ous activity; the unit in Fig. 3a–l augmented their spontaneous
activity), firing rate (the units in Figs. 1, 2, and 3m–x were sup-
pressed; the unit in Fig. 3a–l were facilitated) (Fig. 5), and
latency (Fig. 6). We will describe in detail the changes in firing
rate and latency, after characterizing the effects of AC deacti-
vation on SSA.
Neurons that showed SSA had a stronger response to the de-
viant than to the standard stimulus under all conditions (warm,
cool, and recovery) (Figs. 1, 2, 3a–l) (similar CSI value in all
conditions for all neurons). Neurons that did not show SSA
were no significant differences between the median CSI for the
(b) and m–x (c), shows in the five stages of the cycle. Both units had a stronger response to the deviant than to the standard
stimulus (i.e., showed SSA) in all conditions. Note the strong decrease in activity during the cool condition compared with the
Stages of the AC deactivation cycle. a, Temperature changes recorded at the cooling loop and in MGB during a
17308 • J.Neurosci.,November23,2011 • 31(47):17306–17316AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus
whole population when the AC was active or when it was deacti-
ditions) (Fig. 4a). Eleven neurons were not included in the pre-
ceased firing during cooling, and so it was not possible to deter-
were completely suppressed to one of the frequencies of the pair
chosen in the warm condition. There were no significant dif-
ferences between the median CSI during warm and recovery
conditions (median CSI: 0.27 and 0.25, respectively; n ? 48;
frequencies during cooling). The overall distribution of CSI val-
ues in the population of neurons that were active during AC
the three conditions (Fig. 4a).
To determine whether there were significant differences be-
tween the CSI values of individual neurons in the different con-
the warm condition, for each neuron (Fig. 4b). No significant
differences were found between the CSI values in the different
conditions for the majority of neurons (46 of 48 neurons).
Only 2 of the 48 neurons had a significantly lower CSI value
during the cool condition [Fig. 4b, units 40 and 48 in the cool
condition (asterisks); Fig. 3a–l, unit 40]. Nevertheless, these
neurons retained SSA during AC deactivation (CSI ? 0.39 and
To look for a possible alteration in the dynamics of SSA with
cortical deactivation, we calculated the average population firing
rate in response to the standard stimulus across trials, before,
during, and after cortical deactivation (Fig. 4c,d). Since these dy-
namics will be influenced by all neurons in the population, we
analyzed separately the responses of neurons with and without
ulus through the trials in each condition (Fig. 4e).
The responses to the standard of neurons with SSA declined
strongly after the first few trials, under all conditions (Fig. 4c).
Although these neurons reduced their firing rate during AC de-
activation (cool, blue trace), they recovered to the rate exhibited
before deactivation (warm, red trace) after AC rewarming (re-
covery, green trace), and maintained similar dynamics of adap-
tation under all conditions (Fig. 4c). Indeed, the model that
provided the best fit to these responses in the warm condition
when the AC was active, explained a high proportion of the ad-
threeconditions,forthefirstblock( f1/f2asstandard/deviant)(d–f)andsecondblock( f2/f1as
AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus J.Neurosci.,November23,2011 • 31(47):17306–17316 • 17309
nomial inverse model: f ? y0? (a/x) ? (b/x2); r2? 0.61, 0.53,
and 0.55, respectively, for warm, cool, and recovery; p ? 0.0001
for all conditions). Hence, the neurons showing SSA maintained
a similar adaptation dynamics when the AC was deactivated to
that when it was active. This demonstrates that the AC exerts no
significant effect on the SSA exhibited by the majority of MGB
neurons and on its dynamics over time, and that SSA in this
thalamic nucleus is not inherited from the AC.
The responses of neurons without SSA showed a minor dec-
rement in firing rate after the first trials in the warm condition
that was lost during AC deactivation (Fig. 4d) (the variance ex-
plained by the same polynomial inverse model described above
and 0.516, respectively). The overall firing rate to the standard of
these neurons was strongly reduced with cortical deactivation in
relation to the warm condition. This effect was less pronounced in
In the whole population, the average firing rate evoked by both
stimuli was significantly lower while the AC was cooled and de-
activated (only 9 of 48 neurons augmented their firing rate) (Fig.
3a–l) than while it was active (n ? 48; median firing rate to the
deviant: 1.34 and 0.54 spikes/stimulus, warm and cool condi-
tions, respectively; median firing rate to the standard: 0.85 and
Wilcoxon signed rank test, between the warm and cool condi-
dard; p ? 0.001, both stimuli; Fig. 5a,c) (Figs. 1, 2, 3m–x).
Neurons recovered their initial firing rate after AC rewarming
(n ? 48; median firing rate to the deviant: 1.34 spikes/stimulus,
spikes/stimulus, respectively, warm and recovery conditions;
Wilcoxon signed rank test, between the warm and recovery con-
ditions: Z ? ?0.369 and ?0.944, p ? 0.716 and 0.348, deviant
and standard, respectively) (Figs. 1–3, 5b,c).
To determine the effect of AC deactivation on the discharge
rate of neurons across SSA levels, we plotted the CSI versus the
each neuron (Fig. 5d,e, standard and deviant, respectively): pos-
itive values for this difference indicate a reduction in firing rate
with AC deactivation; and negative values indicate an increment
(Fig. 5d,e, values above and below the horizontal line at the ori-
gin, respectively). This difference in firing rate was inversely cor-
related with CSI for both stimuli (Pearson: r ? ?0.645 and
both stimuli; Fig. 5d,e). Although the correlation coefficient is
higher for the standard than for the deviant stimulus, this differ-
regression lines are not significantly different from each other
vation. a–c, The FRAs in the three conditions of a neuron localized to the MGM that was
second block ( f2/f1as standard/deviant) (g–i) of stimulus presentations (stacked along the
duration: 3 ms) averaged over the two blocks [( f1? f2)/2; blue line is standard, red line is
deviant]. The CSI calculated for each condition is noted as an insert on the PSTHs. m–x, Re-
17310 • J.Neurosci.,November23,2011 • 31(47):17306–17316 AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus
(ANCOVA: main effect of stimuli, F(1,92)? 1.89, p ? 0.172;
p ? 0.634; n ? 48; Fig. 5d,e). Neurons without SSA were sup-
pressed during AC deactivation to both stimuli [Fig. 5d,e, CSI
values around zero (vertical dashed line)]; neurons with higher
tively). Furthermore, some highly adapting neurons were facili-
tated during AC deactivation to both stimuli (Figs. 2a–l, 5d,e).
This suggests that the AC has a differential effect upon the dis-
rons without SSA are mainly facilitated, but some neurons with
high SSA are suppressed by the AC.
(Fig. 5d,e, green, blue, and red dots, respectively; Figs. 2a–l, 3a–l,
units from the MGV). SSA was stronger in the medial subdivi-
sion; intermediate in the dorsal, and weakest in the ventral sub-
divisions (mean CSI ? 0.65, 0.36, and 0.1, respectively) (but see
Antunes et al., 2010). Our sample is biased toward the nonlem-
niscal subdivisions (total of 33 units from the MGD and MGM)
where SSA is stronger and neurons show predominantly non-V-
shaped type FRAs. Although there was a significant effect of AC
deactivation on the firing rate of this population, there was no
vision (n ? 45; Two-way repeated-measures ANOVA, for the re-
sponses to the deviants: F(1,42)? 21.95, p ? 0.001, main effect of
condition; F(2,42)? 2.96, p ? 0.06, main effect of subdivision; and
F(2,42)? 0.12, p ? 0.89, interaction; two-way repeated-measures
ANOVA, for the responses to the standards: main effect of condi-
consistent with the fact that the MGD shows a range of firing rate
changes that extends through the CSI range. Moreover, the MGM
ing rate changes suggests that the degree of SSA exhibited by the
MGB neurons determines the modulatory effect exerted by the AC
on their discharge rate rather than their localization within
a significantly longer median latency (mean first-spike latency)
latency to the standard: 17.5 ms and 21.3 ms, respectively, warm
and cool condition; Wilcoxon signed rank test, between warm
and cool conditions: Z ? 3.764 and 4.244, deviant and standard,
respectively; p ? 0.001, both stimuli; this analysis included the
ing cooling since they were active and responded to the other
frequency; Fig. 6a,c). This lengthening of latency caused by AC
deactivation indicates that the AC modulates the latency of the
MGB neurons, shortening their latencies to both stimuli. The
neurons recovered their latencies after AC deactivation: no dif-
AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamusJ.Neurosci.,November23,2011 • 31(47):17306–17316 • 17311
AC rewarming and reactivation (n ? 48;
median latencies to the deviant: 17.4 and
18.5 ms, respectively, warm and recovery
condition; median latencies to the stan-
and recovery condition; Wilcoxon signed
rank test, between warm and recovery
conditions: Z ? 0.697 and 0; p ? 0.489
tively; this analysis included the neu-
rons that were inactive during cooling;
This analysis also showed that the la-
tency of the responses to the deviant was
significantly shorter than that to the stan-
dard, before AC deactivation (median la-
tency, 17.1 and 17.5 ms, respectively;
Wilcoxon signed rank test: n ? 41, Z ?
4.879, p ? 0.001) (Fig. 6c) (but see An-
tunes et al., 2010); and that this latency
difference was maintained during AC de-
activation (median latency: 19.5 and 21.3
ms, respectively, deviant and standard;
Wilcoxon signed rank test: n ? 41, Z ?
2.041, p ? 0.04; Fig. 6c). This indicates
that the AC is not generating the latency
the deviant exhibited by the MGB
To further evaluate whether the AC
deactivation had a differential effect on
the latency across SSA levels, we plotted
the CSI of the neurons versus the differ-
ence in the mean first-spike latency be-
tween the warm and cool conditions (Fig.
6e,f, standard and deviant, respectively;
n ? 41 active neurons during cooling):
negative values indicate a lengthening in
the latency during AC deactivation; and
positive values indicate a shortening (Fig.
6e,f, below and above the horizontal line
in latency was significantly and positively
correlated with CSI for the responses to
p ? 0.042; Fig. 6e). The latency of the re-
sponses to the deviant stimulus showed
the same trend, but the correlation was
not significant (Pearson: r ? 0.262, p ?
0.099; Fig. 6f). The slopes of the standard
icantly different from each other indicat-
ing that there is no significant difference
between the correlation coefficient ob-
tained for both stimuli (ANCOVA: main
effect of stimuli, F(1,78)? 0.34, p ? 0.561; main effect of CSI,
F(1,78)? 7.14, p ? 0.009; interaction, F(1,78)? 0.05, p ? 0.829;
n ? 41; Fig. 6e,f). This analysis showed that all neurons not
showing SSA (CSI ?0.18) had a longer latency during AC deac-
tivation to both stimuli (Fig. 6e,f; Wilcoxon signed rank test,
between warm and cool conditions: n ? 15, Z ? 2.95 and 3.408,
p ? 0.002 and ?0.001, deviant and standard, respectively). By
contrast, the few neurons that had a shorter latency during AC
deactivation, showed some level of adaptation (CSI ?0.18; Fig.
6e,f, dots above the horizontal line); but still, the median latency
of the population of neurons with SSA was significantly longer
during AC deactivation to both stimuli (Wilcoxon signed rank
test, between control and cooling, CSI ?0.18: n ? 26, Z ? 2.451
and 2.578, p ? 0.015 and 0.01, deviant and standard, respec-
tively). The higher warm–cool differences were exhibited by neu-
rons with zero or intermediate CSI values (?0.45) from the MGV
to the ventral (n ? 12), dorsal (n ? 24), and medial (n ? 9) subdivisions of the MGB, respectively (n ? 45). Positive values
17312 • J.Neurosci.,November23,2011 • 31(47):17306–17316AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus
subdivision (two-way repeated-measures ANOVA, for the re-
0.04; main effect of subdivision, F(2,36)? 2.103, p ? 0.137; interac-
tion, F(2,36)? 0.821, p ? 0.448; two-way repeated-measures
ANOVA, for the responses to the standard:
0.013; main effect of subdivision, F(2,36)?
AC using the cooling technique to silence
its neurons and descending (ipsilateral)
projections to the auditory thalamus, to
examine whether the AC exerts an effect
on the SSA exhibited by MGB neurons.
Overall, our results demonstrate that
AC deactivation produced nonsignifi-
cant changes in the mean SSA levels over
the population and small changes in the
individual neurons in the MGB. Further-
more, the temporal dynamics of SSA in
the MGB were not affected by AC deac-
SSA exhibited by MGB neurons is not a
property inherited from the AC but in-
stead can be inherited from lower levels
such as the inferior colliculus (IC) and/or
that SSA generated at lower levels can be
transmitted in a bottom-up manner and
likely can be modulated intrinsically at
each level of the auditory pathway. Such
bottom-up transmission of SSA would
take the responses of the previous level to
eliminate as much statistical redundancy
as possible (Schwartz and Simoncelli,
can be a potential mechanism underlying
SSA (Chung et al., 2002; Rothman et al.,
2009), since it is input specific and causes
the responses of a neuron to depend on
the previous history of afferent firing, en-
hancing its sensitivity to nonrepeated
stimuli (Abbott et al., 1997; Rothman et
the participation of additional mecha-
ties (Abolafia et al., 2011), which can
The insensitivity of SSA to AC deacti-
vation is all the more remarkable given
the significant alteration of many other
properties of the MGB neurons during
AC deactivation, such as their FRAs,
spontaneous activity, discharge rates, and latencies. These
changes occurred in all neurons, although to different degrees in
and not the consequence of technical artifacts. Furthermore,
these findings confirm previous studies of corticofugal projec-
tions that used a cooling technique (Ryugo and Weinberger,
1976; Villa and Abeles, 1990; Villa et al., 1991, 1999; Palmer et
al., 2007) or electrical stimulation (He, 2003a,b; Ojima and
Rouiller, 2011), and corroborate the strong corticofugal mod-
AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus J.Neurosci.,November23,2011 • 31(47):17306–17316 • 17313
ulation that the MGB, like other subcortical nuclei, receive
(Villa and Abeles, 1990; Villa et al., 1991, 1999; Nakamoto et
al., 2008, 2010; Yu et al., 2009; Bajo et al., 2010; Malmierca and
Ryugo, 2011), as also demonstrated in the visual (Sillito et al.,
1994; Rushmore et al., 2005) and somatosensory systems
(Ghosh et al., 1994). Here, we demonstrate that such cortico-
fugal modulation does not account significantly for the SSA
exhibited in MGB neurons but, rather, modulates the dis-
charge rate of these neurons affecting similarly the responses
to the standard and the deviant stimuli. As a result, the degree
of SSA quantified by a ratio of driven rates was largely unaf-
fected by cooling. This strongly suggests that the corticofugal
pathway exerts a gain control in the MGB neurons. Indeed,
recent studies suggest that the corticofugal system can partic-
salient stimuli (Robinson and McAlpine, 2009) and possibly
underlies auditory attention (He, 2003b) and learning-
with the role of the corticofugal pathway in scaling the sensi-
tivity of the MGB neurons to its driving inputs by controlling
Although SSA in the MGB is only weakly affected by corti-
cal deactivation, we did find a highly significant relationship
between SSA and changes elicited by cooling. Our findings
demonstrate that the corticofugal modulation of the discharge
rate of MGB neurons vary significantly with the SSA level that
they exhibited, such that the facilitation exerted by the AC on
the MGB neurons reduces as the SSA increases, with some
highly adapting neurons being suppressed (Fig. 5). This rela-
tion is not dependent on the anatomical subdivision to which
the MGB neurons belong, but on the SSA they exhibit, linking
this property to the type of corticofugal modulation that they
This is an important finding, since the general view of corti-
cothalamic interactions is one of very large variability with many
different effects. Consistent with this view is our finding that the
deactivation. Therefore, our finding that the amount of cortical
modulation depends on the level of SSA constitutes strong evi-
high SSA neurons from the nonlemniscal MGB receive suppres-
sive influence from the corticofugal pathway, inhibition is a pos-
sible mechanism underlying such modulation, via the thalamic
reticular nucleus (TRN) (He, 2003b; Yu et al., 2009), a nucleus
that also exhibits SSA (Yu et al., 2009). Our results show that the
possible inhibition driven by the corticofugal pathway does not
underlie SSA in the MGB neurons. Hence, if inhibition plays a
2003; Yu et al., 2009; Richardson et al., 2011), other pathways
Furthermore, we demonstrate that the AC modulates the la-
tency of the MGB neurons, mainly shortening their latencies, as
previously shown in some subcortical neurons using electrical
stimulation of AC (Luo et al., 2008). This effect becomes less
pronounced as the neuronal adaptation increases. The slight la-
tency shift that the strong adapting neurons from the thalamus
and the midbrain with the shortest latencies (?25 ms) exhibit
comparatively to the weaker or nonadapting ones (Malmierca et
al., 2009; Antunes et al., 2010) may be due to a weaker cortical
influence that mainly shortens the latency of the MGB neurons.
The MGB neurons maintained a shorter latency to the deviant
than to the standard stimulus (but see Antunes et al., 2010) dur-
ing cortical deactivation, demonstrating that this latency phe-
nomenon is not of cortical origin. However, all of these latency
phenomena can be due to changes in the excitability of the MGB
neurons, either controlled by the cortex or elicited by different
stimulus probabilities, since increased excitability will usually be
rons with different SSA levels, since neurons that exhibit high
The fact that the nonadapting neurons are mainly facilitated and
their latencies shortened by corticofugal modulation can be
achieved by direct excitation from the AC and/or by a release of
inhibitory inputs coming from neurons in the thalamus and its
et al., 2000) via TRN inhibition on these inhibitory inputs.
Together, our findings demonstrate that the AC and cortico-
fugal pathways modulate the general responses of the MGB neu-
probably by providing a gating or gain control mechanism (Villa
and Abeles, 1990; Villa et al., 1991; He, 2003b; Robinson and
McAlpine, 2009; Yu et al., 2009). However, a subset of neurons
had their acoustic responsiveness eliminated with cortical deac-
tivation (four in the MGD; two in the MGV; the other was not
histologically localized). These data are in agreement with the
drivers and modulators hypothesis proposed originally by Guil-
lery (1995) and Sherman and Guillery (1998). The main cortico-
fugal projections to the MGB arise from layer VI neurons, whose
1991; Ojima, 1994; Bajo et al., 1995; Bartlett et al., 2000; Ojima
and Rouiller, 2011). In addition, a few pyramidal neurons from
layer V with large terminal boutons of the driver type project to
the MGD and MGV subdivisions (Rouiller and Welker, 1991;
Ojima, 1994; Bajo et al., 1995; Bartlett et al., 2000; Ojima and
Rouiller, 2011). The seven neurons that ceased firing during AC
cooling had nonexistent or very low levels of SSA (CSI ?0.28),
agreeing with our main result that SSA in the MGB is not inher-
ited from the AC.
In summary, we demonstrate that the corticofugal pathway
does not drive or transform SSA in the MGB but modulates the
responses of the MGB neurons, presumably by changing their
gain. The amount of gain exerted by the AC in the MGB varies
to detect rare sounds in the environment. We suggest that SSA
could be generated in a bottom-up manner throughout the au-
sory inputs to behaviorally relevant aspects.
Abbott LF, Varela JA, Sen K, Nelson SB (1997) Synaptic depression and
cortical gain control. Science 275:220–224.
Abolafia JM, Vergara R, Arnold MM, Reig R, Sanchez-Vives MV (2011)
Cortical auditory adaptation in the awake rat and the role of potassium
currents. Cereb Cortex 21:977–990.
Antunes FM, Malmierca MS (2011) Top-down modulation of stimulus-
specific adaptation in the auditory thalamus. In: Abstracts of the 34th
MidWinter Meeting of the Association for Research in Otolaryngology,
pp 93–94. Mt. Royal, NJ: Association for Research in Otolaryngology.
Antunes FM, Nelken I, Covey E, Malmierca MS (2010) Stimulus-specific
adaptation in the auditory thalamus of the anesthetized rat. PLoS One
Bajo VM, Rouiller EM, Welker E, Clarke S, Villa AE, de Ribaupierre Y, de
Ribaupierre F (1995) Morphology and spatial distribution of cortico-
thalamic terminals originating from the cat auditory cortex. Hear Res
17314 • J.Neurosci.,November23,2011 • 31(47):17306–17316AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus
BajoVM,NodalFR,MooreDR,KingAJ (2010) Thedescendingcorticocol-
licular pathway mediates learning-induced auditory plasticity. Nat Neu-
Bartlett EL, Stark JM, Guillery RW, Smith PH (2000) Comparison of the
fine structure of cortical and collicular terminals in the rat medial genic-
ulate body. Neuroscience 100:811–828.
Carrasco A, Lomber SG (2009a) Differential modulatory influences be-
tween primary auditory cortex and the anterior auditory field. J Neurosci
Carrasco A, Lomber SG (2009b) Evidence for hierarchical processing in cat
auditory cortex: nonreciprocal influence of primary auditory cortex on
the posterior auditory field. J Neurosci 29:14323–14333.
Chung S, Li X, Nelson SB (2002) Short-term depression at thalamocortical
synapses contributes to rapid adaptation of cortical sensory responses in
vivo. Neuron 34:437–446.
Coomber B, Edwards D, Jones SJ, Shackleton T.M., Goldschmidt J, Wallace
MN and Palmer AR (2011) Cortical inactivation by cooling in smaller
animals. Front Syst Neurosci. Advance online publication. Retrieved Oc-
tober 22, 2011. doi:10.3389/fnsys.2011.00053.
DoronNN,LedouxJE,SempleMN (2002) Redefiningthetonotopiccoreof
rat auditory cortex: physiological evidence for a posterior field. J Comp
Eytan D, Brenner N, Marom S (2003) Selective adaptation in networks of
cortical neurons. J Neurosci 23:9349–9356.
Games KD, Winer JA (1988) Layer V in rat auditory cortex: projections to
the inferior colliculus and contralateral cortex. Hear Res 34:1–25.
GhoshS,MurrayGM,TurmanAB,RoweMJ (1994) Corticothalamicinflu-
ences on transmission of tactile information in the ventroposterolateral
thalamus of the cat: effect of reversible inactivation of somatosensory
cortical areas I and II. Exp Brain Res 100:276–286.
Guillery RW (1995) Anatomical evidence concerning the role of the thala-
mus in corticocortical communication: a brief review. J Anat
Gutfreund Y, Knudsen EI (2006) Adaptation in the auditory space map of
the barn owl. J Neurophysiol 96:813–825.
He J (2003a) Corticofugal modulation on both ON and OFF responses in
the nonlemniscal auditory thalamus of the guinea pig. J Neurophysiol
He J (2003b) Corticofugal modulation of the auditory thalamus. Exp Brain
Hernández O, Espinosa N, Pérez-González D, Malmierca MS (2005) The
response areas. Neuroscience 132:203–217.
Kimura A, Donishi T, Okamoto K, Tamai Y (2005) Topography of projec-
tions from the primary and non-primary auditory cortical areas to the
medial geniculate body and thalamic reticular nucleus in the rat. Neuro-
Kimura A, Donishi T, Okamoto K, Imbe H, Tamai Y (2007) Efferent
connections of the ventral auditory area in the rat cortex: implications
for auditory processing related to emotion. Eur J Neurosci
LeeCC,WinerJA (2005) Principlesgoverningauditorycortexconnections.
Cereb Cortex 15:1804–1814.
Lomber SG (1999) The advantages and limitations of permanent or revers-
ible deactivation techniques in the assessment of neural function. J Neu-
rosci Methods 86:109–117.
Lomber SG, Malhotra S (2008) Double dissociation of ‘what’ and ‘where’
processing in auditory cortex. Nat Neurosci 11:609–616.
Lomber SG, Payne BR, Horel JA (1999) The cryoloop: an adaptable revers-
ible cooling deactivation method for behavioral or electrophysiological
assessment of neural function. J Neurosci Methods 86:179–194.
Lomber SG, Malhotra S, Hall AJ (2007) Functional specialization in non-
primary auditory cortex of the cat: areal and laminar contributions to
sound localization. Hear Res 229:31–45.
Luo F, Wang Q, Kashani A, Yan J (2008) Corticofugal modulation of initial
sound processing in the brain. J Neurosci 28:11615–11621.
Malmierca MS, Ryugo DK (2011) Descending connections of auditory cor-
tex to the midbrain and brian stem. In: The auditory cortex (Winer JA,
Schreiner CE, eds), pp 189–208. New York: Springer.
Malmierca MS, Herna ´ndez O, Rees A (2005) Intercollicular commissural
projections modulate neuronal responses in the inferior colliculus. Eur
J Neurosci 21:2701–2710.
MalmiercaMS,IzquierdoMA,CristaudoS,Herna ´ndezO,Pe ´rez-Gonza ´lezD,
Covey E, Oliver DL (2008) A discontinuous tonotopic organization in
the inferior colliculus of the rat. J Neurosci 28:4767–4776.
Malmierca MS, Cristaudo S, Pe ´rez-Gonza ´lez D, Covey E (2009) Stimulus-
MerrillEG,AinsworthA (1972) Glass-coatedplatinumcoatedtungstenmi-
croelectrodes. Med Biol Eng 10:662–672.
Nakamoto KT, Jones SJ, Palmer AR (2008) Descending projections from
auditory cortex modulate sensitivity in the midbrain to cues for spatial
position. J Neurophysiol 99:2347–2356.
Nakamoto KT, Shackleton TM, Palmer AR (2010) Responses in the in-
ferior colliculus of the guinea pig to concurrent harmonic series and
the effect of inactivation of descending controls. J Neurophysiol
Nelken I (2004) Processing of complex stimuli and natural scenes in the
auditory cortex. Curr Opin Neurobiol 14:474–480.
Nelken I, Ulanovsky N (2007) Mismatch negativity and stimulus-specific
adaptation in animal models. J Psychophysiol 21:214–223.
Ojima H (1994) Terminal morphology and distribution of corticothalamic
fibers originating from layers 5 and 6 of cat primary auditory cortex.
Cereb Cortex 4:646–663.
Ojima H, Rouiller EM (2011) Auditory cortical projections to the medial
geniculate body. In: The auditory cortex (Winer JA, Schreiner CE, eds),
pp 171–188. New York: Springer.
Palmer AR, Hall DA, Sumner C, Barrett DJ, Jones S, Nakamoto K, Moore
DR (2007) Some investigations into non-passive listening. Hear Res
Paxinos G, Watson C (2005) The rat brain in stereotaxic coordinates. Bur-
lington, VT: Elsevier-Academic.
Pe ´rez-Gonza ´lez D, Malmierca MS, Covey E (2005) Novelty detector
neurons in the mammalian auditory midbrain. Eur J Neurosci
PeruzziD,BartlettE,SmithPH,OliverDL (1997) AmonosynapticGABAe-
J Neurosci 17:3766–3777.
Ranganath C, Rainer G (2003) Neural mechanisms for detecting and re-
membering novel events. Nat Rev Neurosci 4:193–202.
Reches A, Gutfreund Y (2008) Stimulus-specific adaptations in the gaze
control system of the barn owl. J Neurosci 28:1523–1533.
Reches A, Netser S, Gutfreund Y (2010) Interactions between stimulus-
specific adaptation and visual auditory integration in the forebrain of the
barn owl. J Neurosci 30:6991–6998.
Rees A (1990) A close-field sound system for auditory neurophysiology.
J Physiol 430:2.
Richardson BD, Ling LL, Uteshev VV, Caspary DM (2011) Extrasynaptic
GABA(A) receptors and tonic inhibition in rat auditory thalamus. PLoS
RobinsonBL,McAlpineD (2009) Gaincontrolmechanismsintheauditory
pathway. Curr Opin Neurobiol 19:402–407.
Rothman JS, Cathala L, Steuber V, Silver RA (2009) Synaptic depression
enables neuronal gain control. Nature 457:1015–1018.
Rouiller EM, Welker E (1991) Morphology of corticothalamic terminals
arising from the auditory cortex of the rat: a Phaseolus vulgaris-
leucoagglutinin (PHA-L) tracing study. Hear Res 56:179–190.
RouillerEM,WelkerE (2000) Acomparativeanalysisofthemorphologyof
corticothalamic projections in mammals. Brain Res Bull 53:727–741.
Rushmore RJ, Payne BR, Lomber SG (2005) Functional impact of primary
and midbrain. J Comp Neurol 488:414–426.
Ryugo DK, Weinberger NM (1976) Corticofugal modulation of the medial
geniculate body. Exp Neurol 51:377–391.
Schwartz O, Simoncelli EP (2001) Natural signal statistics and sensory gain
control. Nat Neurosci 4:819–825.
Sherman SM, Guillery RW (1998) On the actions that one nerve cell can
have on another: distinguishing “drivers” from “modulators.” Proc Natl
Acad Sci U S A 95:7121–7126.
Sillito AM, Jones HE, Gerstein GL, West DC (1994) Feature-linked syn-
chronization of thalamic relay cell firing induced by feedback from the
visual cortex. Nature 369:479–482.
AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamusJ.Neurosci.,November23,2011 • 31(47):17306–17316 • 17315
Taaseh N, Yaron A, Nelken I (2011) Stimulus-specific adaptation and devi- Download full-text
ance detection in the rat auditory cortex. PLoS One 6:e23369.
Ulanovsky N, Las L, Nelken I (2003) Processing of low-probability sounds
by cortical neurons. Nat Neurosci 6:391–398.
Ulanovsky N, Las L, Farkas D, Nelken I (2004) Multiple time scales of
adaptation in auditory cortex neurons. J Neurosci 24:10440–10453.
Villa AE, Abeles M (1990) Evidence for spatiotemporal firing patterns
within the auditory thalamus of the cat. Brain Res 509:325–327.
Villa AE, Rouiller EM, Simm GM, Zurita P, de Ribaupierre Y, de
Ribaupierre F (1991) Corticofugal modulation of the information
processing in the auditory thalamus of the cat. Exp Brain Res
Villa AE, Tetko IV, Dutoit P, De Ribaupierre Y, De Ribaupierre F (1999)
Corticofugal modulation of functional connectivity within the auditory
thalamus of rat, guinea pig and cat revealed by cooling deactivation.
J Neurosci Methods 86:161–178.
UT (2004) Probability of transmitter release at neocortical synapses at
different temperatures. J Neurophysiol 92:212–220.
von der Behrens W, Ba ¨uerle P, Ko ¨ssl M, Gaese BH (2009) Correlating
stimulus-specific adaptation of cortical neurons and local field potentials
in the awake rat. J Neurosci 29:13837–13849.
Winer JA, Kelly JB, Larue DT (1999) Neural architecture of the rat medial
geniculate body. Hear Res 130:19–41.
Winer JA, Diehl JJ, Larue DT (2001) Projections of auditory cortex to the
medial geniculate body of the cat. J Comp Neurol 430:27–55.
Winkler I, Denham SL, Nelken I (2009) Modeling the auditory scene: pre-
YangXF,KennedyBR,LomberSG,SchmidtRE,RothmanSM (2006) Cool-
ing produces minimal neuropathology in neocortex and hippocampus.
Neurobiol Dis 23:637–643.
Yu XJ, Xu XX, He S, He J (2009) Change detection by thalamic reticular
neurons. Nat Neurosci 12:1165–1170.
17316 • J.Neurosci.,November23,2011 • 31(47):17306–17316AntunesandMalmierca•CorticalInfluenceonSSAintheAuditoryThalamus