Multiplexed and Robust Representations of Sound Features in Auditory Cortex

Abstract

We can recognize the melody of a familiar song when it is played on different musical instruments. Similarly, an animal must be able to recognize a warning call whether the caller has a high-pitched female or a lower-pitched male voice, and whether they are sitting in a tree to the left or right. This type of perceptual invariance to "nuisance" parameters comes easily to listeners, but it is unknown whether or how such robust representations of sounds are formed at the level of sensory cortex. In this study, we investigate whether neurons in both core and belt areas of ferret auditory cortex can robustly represent the pitch, formant frequencies, or azimuthal location of artificial vowel sounds while the other two attributes vary. We found that the spike rates of the majority of cortical neurons that are driven by artificial vowels carry robust representations of these features, but the most informative temporal response windows differ from neuron to neuron and across five auditory cortical fields. Furthermore, individual neurons can represent multiple features of sounds unambiguously by independently modulating their spike rates within distinct time windows. Such multiplexing may be critical to identifying sounds that vary along more than one perceptual dimension. Finally, we observed that formant information is encoded in cortex earlier than pitch information, and we show that this time course matches ferrets' behavioral reaction time differences on a change detection task.

Full text

Behavioral/Systems/Cognitive
Multiplexed and Robust Representations of Sound Features
in Auditory Cortex
Kerry M. M. Walker, Jennifer K. Bizley, Andrew J. King, and Jan W. H. Schnupp
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom
We can recognize the melody of a familiar song when it is played on different musical instruments. Similarly, an animal must be able to
recognize a warning call whether the caller has a high-pitched female or a lower-pitched male voice, and whether they are sitting in a tree
to the left or right. This type of perceptual invariance to “nuisance” parameters comes easily to listeners, but it is unknown whether or how
such robust representations of sounds are formed at the level of sensory cortex. In this study, we investigate whether neurons in both core
and belt areas of ferret auditory cortex can robustly represent the pitch, formant frequencies, or azimuthal location of artificial vowel
sounds while the other two attributes vary. We found that the spike rates of the majority of cortical neurons that are driven by artificial
vowels carry robust representations of these features, but the most informative temporal response windows differ from neuron to neuron
and across five auditory cortical fields. Furthermore, individual neurons can represent multiple features of sounds unambiguously by
independently modulating their spike rates within distinct time windows. Such multiplexing may be critical to identifying sounds that
vary along more than one perceptual dimension. Finally, we observed that formant information is encoded in cortex earlier than pitch
information, and we show that this time course matches ferrets’ behavioral reaction time differences on a change detection task.
Introduction
The encoding properties of auditory neurons have typically been
examined by expressing their spike rates within a fixed time window
as a function of a single sound parameter. Rate–intensity curves, for
example, describe firing rates as a function of sound intensity (Phil-
lips and Irvine, 1981). This approach has revealed much about how
neuronal responses are modulated by sounds but overlooks the inherent
complexities of natural sound statistics and spike firing patterns.
Outside of the laboratory, sounds typically vary over more
than one acoustic dimension. Thus, an animal must be able to
recognize the calls of predators, prey, and potential mates regard-
less of their pitch or the direction from which they originate.
Neuronal sensitivity to timbre (Langner et al., 1981; Ohl and
Scheich, 1997), pitch (Bendor and Wang, 2010; Bizley et al.,
2010), and spatial location (Benson et al., 1981; Middlebrooks et
al., 1994; Mrsic-Flogel et al., 2005) has been described in auditory
cortex. However, it is not known whether or how the activity of
cortical neurons can support perceptual invariance to “nuisance”
parameters when these features vary simultaneously.
One way to achieve an explicit, invariant representation of mul-
tiple sound properties is to represent each feature by a separate group
of neurons. However, our recent results suggest that this is not the
way auditory cortical response properties are organized. Rather, the
vast majority of neurons in ferret auditory cortex are sensitive to
combinations of the periodicity, timbre, and location of sounds (Bi-
zley et al., 2009). An alternative solution might be to represent mul-
tiple cues within the spiking response of individual neurons by
tuning different aspects of the response to different perceptual fea-
tures, resulting in a “multiplexed” spike code. Indeed, the onset,
sustained, and offset responses of cortical neurons can represent
different stimulus values for a range of sound features, including
pure tone frequency and intensity (Takahashi et al., 2004; Wang et
al., 2005), spatial location (Campbell et al., 2010), and vocalization
identity (Qin et al., 2008). These findings emphasize the importance
of choosing an appropriate response window for determining the
sensitivity of a neuron to a given sound feature (Panzeri et al., 2010).
They are also consistent with the possibility that single neurons hold
multiplexed representations of sound features, but this has yet to be
confirmed experimentally.
Here, we investigate whether neurons carry information
about the periodicity, spectral timbre (formants), or azimuthal
location of artificial vowel sounds in ways that are “robust” to
simultaneous variance in the other two attributes. We estimated
the information carried about each attribute within discrete time
windows of the neural response and show that the neural code for
timbre, pitch, and spatial location differs systematically across the
auditory cortex. Furthermore, we demonstrate that single neu-
rons can multiplex pitch and timbre information in separate re-
sponse windows. Robust information about sound timbre was
found to peak consistently earlier in the response than pitch in-
formation. We show that this neural signature has a behavioral
correlate: ferrets respond to timbre changes faster than pitch
changes in artificial vowels in a go/no-go task.
Received April 26, 2011; revised July 14, 2011; accepted Aug. 9, 2011.
Author contributions: K.M.M.W., J.K.B., A.J.K., and J.W.H.S. designed research; K.M.M.W. and J.K.B. performed
research; K.M.M.W. and J.W.H.S. contributed unpublished reagents/analytic tools; K.M.M.W. analyzed data;
K.M.M.W. wrote the paper.
This work was supported by a grant from the Biotechnology and Biological Sciences Research Council (J.W.H.S.,
J.K.B., A.J.K.), a Royal Society Dorothy Hodgkin Fellowship (J.K.B.), and a Wellcome Trust Principal Research Fellow-
ship(A.J.K.). Weare gratefulto IsraelNelken forhis helpfulcomments ona draftof thismanuscript, andCesare Magri
for providing MATLAB code for entropy calculations at http://www.ibtb.org (Magri et al., 2009).
Correspondence should be addressed to Kerry M. M. Walker, Department of Physiology, Anatomy and Genetics,
University of Oxford, Sherrington Building, Parks Road, Oxford OX1 3PT, UK. E-mail: kerry.walker@dpag.ox.ac.uk.
DOI:10.1523/JNEUROSCI.2074-11.2011
Copyright © 2011 the authors 0270-6474/11/3114565-12$15.00/0
The Journal of Neuroscience, October 12, 2011 •31(41):14565–14576 • 14565

Materials and Methods
Animals. A total of 10 adult, female, pigmented ferrets (Mustela putorius)
were used in this study. All experiments were approved by the local
ethical review committee and performed under license from the United
Kingdom Home Office in accordance with the Animal (Scientific Proce-
dures) Act (1986).
Extracellular recordings. Details of the surgical procedures performed
here on six ferrets can be found in the study by Bizley et al. (2009). Briefly,
general anesthesia was induced with a single intramuscular dose of me-
detomidine (Domitor; 0.022 mg 䡠kg
⫺1
䡠h
⫺1
; Pfizer) and ketamine (Ke-
taset; 5 mg 䡠kg
⫺1
䡠h
⫺1
; Fort Dodge Animal Health) and was maintained
with a continuous intravenous infusion of medetomidine and ketamine
in saline throughout the experiment. Oxygen was supplemented with a
ventilator, and vital signs (body temperature, end-tidal CO
2
, and the
electrocardiogram) were monitored while the temporal muscles were
retracted, a head holder was secured to the skull surface, and a craniot-
omy and durotomy were made over the left auditory cortex.
Spike activity was recorded using silicon array Michigan probes (1–2
M⍀impedances; Neuronexus). Electrode penetrations were made
throughout five identified areas of ferret auditory cortex: the primary
auditory cortex (A1), the anterior auditory field (AAF), the posterior
pseudosylvian and suprasylvian fields (PPF and PSF), and the anterior
dorsal field (ADF). The electrode recording sites used were configured as
8⫻4 (eight active sites on four parallel probes, with a vertical spacing of
150
␮
m), 4 ⫻4 (100 –150
␮
m spacing), 16 ⫻2 (spaced at 100
␮
m
intervals), or 16 ⫻1 (100–150
␮
m spacing on a single probe). Photo-
graphic records of each electrode penetration were used to reconstruct
the recording locations relative to anatomical landmarks (surface blood
vessels and sulcal patterns).
Voltage signals recorded were bandpass filtered (500 Hz to 5 kHz),
amplified up to 20,000 times, and digitized at 25 kHz. Data acquisition
was performed using Tucker-Davis Technologies System 3 multichannel
recording systems, together with desktop computers running BrainWare
software (Tucker-Davis Technologies) and custom scripts written in
MATLAB (MathWorks).
Neuronal spiking responses were isolated from the digitized signal
off-line. Spikes from a common neural source were classified either by
manually clustering spike shapes according to features such as ampli-
tude, width, and area, or by using an automated k-means algorithm to
cluster voltage potentials. A unit was only classified as a single neuron if
the autocorrelation spike histogram revealed a clear refractory period
and the spike shape was stereotyped. All other clusters of spikes were
classified as multiunit activity, representing the firing of a small cluster of
neurons. Poststimulus time histograms (PSTHs) of spikes from each unit
were visually inspected in BrainWare, and all single units (n⫽619) and
multiunit clusters (n⫽464) that appeared to be driven by auditory
stimuli were exported to MATLAB for further analysis.
Stimuli. Sounds were generated using Tucker-Davis Technologies
System 3 hardware and MATLAB. Sounds were presented to anesthe-
tized animals in an anechoic room through Panasonic earphone driv-
ers (RPHV297), which were mounted on plastic otoscope speculae
inserted into each ear canal. The earphones were closed-field cali-
brated using a 1/8 inch condenser microphone (Bru¨el and Kjær type
4138) placed at the end of a model ferret ear canal, and inverse filters
were used to ensure that the devices produced flat (less than ⫾5 dB)
outputs across 100 –24,000 Hz.
Following each cortical penetration, noise bursts (100 ms duration;
900 ms interstimulus interval; 10 –80 dB SPL) were presented to identify
acoustically responsive neural activity. Next, pure tones (100 ms dura-
tion, with 5 ms cosine onset and offset ramps) were presented, and the
best frequency (BF) of each unit was derived from the frequency response
area, as described by Bizley et al. (2005). All units with a bandwidth of ⬍2
octaves at 10 dB above threshold were assigned a BF, and those with wider
bandwidths were classified as “untuned.”
Artificial vowel stimuli were created in MATLAB, using an algorithm
adapted from Malcolm Slaney’s Auditory Toolbox. A click train was first
produced with a repetition rate corresponding to the desired fundamen-
tal frequency (F0) and a duration of 150 ms. This signal was then passed
through a four-pass-band filter to impart spectral peaks at the desired
formant frequencies. The artificial vowels were root-mean-square nor-
malized to ensure that changes in pitch or timbre did not influence the
overall sound pressure level. Virtual acoustic space (VAS) techniques
were then used to add sound source direction cues (interaural timing
differences, interaural level differences, and spectral cues) to the artificial
vowel sounds, as described by Mrsic-Flogel et al. (2005).
The sounds were presented in VAS at ⫺45, ⫺15, 15, and 45° azimuth,
at 0° elevation. Negative azimuths denote locations to the animal’s right,
contralateral to the recording hemisphere. F0 values of 200, 336, 565, and
951 Hz were used. The four timbres presented corresponded to the fol-
lowing vowels: /a/ with formant frequencies F1–F4 at 936, 1551, 2815,
and 4290 Hz; /␧/ with formant frequencies at 730, 2058, 2979, and 4294
Hz; /u/ with formant frequencies at 460, 1105, 2735, and 4115 Hz; and /i/
with formant frequencies at 437, 2761, 3372, and 4352 Hz. The combi-
nations of these four pitches, four timbres, and four sound source direc-
tions resulted in a stimulus set of 64 artificial vowels.
Mutual information calculations. Mutual information (MI) calcula-
tions have been used to evaluate how reliably the response of a neuron
distinguishes between sensory stimuli. They provide an estimate of the
reduction in uncertainty about a stimulus feature, X, provided by the
response, R, of a neuron. Therefore, when the MI is larger, one can more
accurately guess the identity of the stimulus presented based on the neu-
ral response. By convention, MI is measured in bits (Cover and Thomas,
1991), according to the following formula:
MI(X;R)⫽
冘
x僆X
冘
r僆Rp共x,r兲log2
冉
p共x,r兲
p共x兲p共r兲
冊
,(1)
The artificial vowel sounds used in the present experiment were varied
over three perceptual attributes: timbre, pitch, and sound source azi-
muth. We used MI analysis to examine how well a neuron can encode one
of these stimulus dimensions (X), despite variance in the other two di-
mensions (Yand Z). To calculate MI for stimulus feature X(timbre, for
example), responses to presentations of the 64 vowel sounds were classi-
fied into the four Xgroups, collapsing across the 16 Yand Z(e.g., F0 and
azimuth) combinations. Therefore, in the case of MI calculations for
timbre, the value of xin Equation 1 would indicate only the timbre of the
sound, and the size of set Xwas four. We presented each of the 64 sounds
in our stimulus set at least 20 times for each unit, so this grouping of
stimuli meant that each MI calculation included at least 320 observations
of neural responses (i.e., 320 “trials”) for each value of x(N
x
⫽320).
The maximum MI for a stimulus set of size 兩X兩with uniform proba-
bility is equal to log
2
(兩X兩). We presented each of our stimulus classes with
equal probability, so an ideal neuron that responds perfectly reliably to
different values of sound feature X, and invariantly to features Yand Z,
would provide log
2
(4), or 2, bits of information.
The response, r, in Equation 1 was the sum of spikes within a single
response time window. The response window was defined by its duration
and onset time. MI was calculated for several window durations, includ-
ing 20, 40, and 320 ms. For each response window duration, MI calcula-
tions were repeated across a range of onset times with respect to sound
onset. The window onset was shifted across the first 320 ms of the neural
response, in steps of one-half of the window size. For a window width of
20 ms, this would give 33 estimates of MI, with onsets ranging from 0 to
320 ms. For a response window of 320 ms, the MI was calculated once,
summing spikes occurring on each trial from sound onset (t⫽0 ms) to
320 ms. For each MI calculation, the set Rcomprised all unique neural
responses, r, observed from the neuron, across all stimulus presentations.
The information content of codes other than spike rates was also ex-
amined. For a binary spiking code, trials with at least one spike in the 320
ms following sound onset were coded as “1,” while trials in which the unit
did not spike were coded as “0.” We also wanted to examine the infor-
mation available in the first-spike latency to millisecond accuracy, al-
though this value was often too variable (i.e., the entropy of first-spike
latency was too high) to adequately estimate the probability densities.
Therefore, we restricted the variability of this code by binning the laten-
cies into four first-spike latency quartiles for each unit. Here, trials on
which there were no spike within the 320 ms response window were
14566 •J. Neurosci., October 12, 2011 •31(41):14565–14576 Walker et al. •Feature Multiplexing in Auditory Cortex

coded as having a first-spike latency of 321 ms. Finally, we used Victor’s
“binless” algorithm to arrive at an optimized and low-dimensional rep-
resentation of the temporal spiking patterns of a unit (Victor, 2002).
Victor’s binless method has been previously applied to studies of audi-
tory cortex (Nelken et al., 2005), and details of this algorithm are avail-
able in the original source (Victor, 2002).
Bias estimation. In Equation 1, the true probabilities of neural re-
sponses, p(r) and p(x,r), are estimated from a limited sample of experi-
mental observations and are inherently biased to overestimate the
relations between a stimulus and response (Treves and Panzeri, 1995).
Several methods for estimating the bias of MI estimations have been
shown to be effective for neural data (Panzeri et al., 2007). We used a
method based on bootstrapping a randomly shuffled version of the da-
taset. In this approach, the set of all spike rate responses were randomly
reassigned with replacement to the set of stimulus conditions. The MI of
this shuffled dataset was then calculated using Equation 1. This proce-
dure was repeated 500 times, and the median of the resulting MI values
was taken as the bias estimate (bias). All MI values reported were bias-
corrected by subtracting the value of bias from the MI calculated for the
original, unshuffled dataset.
Out of interest, the bias for MI values was also estimated using a less
conservative, but more computationally efficient approach (Panzeri et
al., 2007) as follows:
bias ⫽
冘
x僆X兩Rx⫺1兩⫺兩兩R兩⫺1兩
2NIn(2) , (2)
where 兩R
x
兩is the cardinality of responses to stimulus x,兩R兩is the total
number of unique responses observed across all stimuli, and Nis the total
number of stimulus presentations. The bias values obtained using the
random shuffling procedure and from Equa-
tion 2 were very similar, and the type of bias
estimation used made no difference to the
overall pattern of results across fields and stim-
ulus features.
Behavioral testing. Four water-restricted fer-
rets were trained to detect a change in the pitch
or timbre of a repeating artificial vowel on a
go/no-go task. The animal initiated each trial
by inserting its nose in a poke hole situated at
the center of the sound-isolated testing cham-
ber, and a sequence of artificial vowels was then
delivered from a speaker positioned above the
nose poke hole. Vowels were two-formant ver-
sions of those described above, and the vowel
/a/ with an F0 of 200 Hz served as the reference
sound (80 dB SPL, 350 ms duration, 200 ms
interstimulus intervals). The reference sound
could change in identity or pitch at the third to
seventh vowel in the sequence, and if ferrets
withdrew from the nose poke hole during pre-
sentation of such a deviant, they were rewarded
with water. Failures to withdraw to a deviant
(within a 550 ms time window following devi-
ant onset) resulted in a 12 s time out. Spectral
timbre and pitch changes were tested in sepa-
rate sessions, and ferrets completed an average
of 93 trials per testing session. Ferrets 1, 2, 3,
and 4 completed 12, 22, 25, and 24 sessions on
the pitch change detection task and 18, 22, 31,
and 41 sessions on the timbre task, respectively.
Reaction times were derived from trials in
which the animal correctly identified a sound
change.
Results
We initially made extracellular recordings
from 1083 acoustically responsive units in
the left auditory cortex of five ferrets. We
characterized the responses of the units to pure tones as well as to
artificial vowel stimuli (Fig. 1A), which varied along three differ-
ent parameters: their formant composition (they could be /a/, /␧/,
/i/, or /u/-like), their fundamental frequency (F0, which is the key
determinant of the vowel pitch, could take values of 200, 336, 565,
or 951 Hz), and their azimuthal position (which could be ⫺45,
⫺15, 15, or 45°, in which negative values denote directions to the
animal’s right, contralateral to the recording sites). This therefore
produced 64 stimulus combinations. To test whether the units in
our dataset were driven by these artificial vowel sounds, we per-
formed paired ttests on the spike rates during vowel presentation
(150 ms duration, beginning at sound onset) and during the
proceeding silent period (150 ms duration, beginning ⱖ450 ms
after offset of the previous sound). We found that 320 single units
and 323 multiunit clusters in our sample were significantly driven
by artificial vowels (p⬍0.05). These units were found in five
different areas of auditory cortex (Fig. 1B): A1 (n⫽212 neural
units), AAF (n⫽111), PPF (n⫽155), PSF (n⫽110), and ADF
(n⫽55), as judged by their frequency selectivity and temporal
response properties. All further analyses were performed on these
643 driven units.
Mutual information throughout the response duration
Using the methodology described below, we calculated the MI
between neural spike rates and each of the three perceptual fea-
tures: timbre, pitch, and azimuth (Eq. 1; see Materials and Meth-
ods). In each case, responses were collapsed across the other two
A
B
Figure 1. Artificialvowelsounds andlocationsof fiveauditorycortical fieldsfromwhich responseswererecorded. A,Frequency
spectra of the artificial vowels used in the experiment, fully varied across four spectral timbres (columns) and four pitches (rows).
Each of these stimuli was presented at four different virtual locations along the azimuth (⫺45, ⫺15, 15, and 45° from midline). B,
Locations of five auditory cortical fields on the ectosylvian gyrus of the ferret: A1, AAF, PPF, PSF, and ADF. The fields are separated
with dotted lines, and the pseudosylvian sulcus (pss) and suprasylvian sulcus (sss) are drawn as solid lines.
Walker et al. •Feature Multiplexing in Auditory Cortex J. Neurosci., October 12, 2011 •31(41):14565–14576 • 14567

features. In this way, when we calculate the MI for timbre, we
accept the potential confound that arises from allowing pitch and
azimuth to vary, so the result reflects timbre information that is
robust across pitch and azimuth changes. When choosing a post-
stimulus time period over which to analyze spike trains, the
experimenter usually makes assumptions about the most appro-
priate temporal response window for the neurons under study.
Here, we explored a wide range of possible response windows,
repeating MI calculations for spike rates calculated in 20 ms time
windows that slide, in 10 ms steps, across the initial 320 ms after
stimulus onset. The results are shown in Figure 2A. Each line in
the MI plots shows the bias-corrected MI for one unit, as a func-
tion of poststimulus time. Data are grouped according to which
cortical field they were recorded in (rows). The MI estimated for
timbre, pitch, and azimuth is displayed in the three columns of
Figure 2A(left to right, respectively).
Figure 2Areveals trends in the distribution of MI across the
duration of the response. For instance, the most informative part
of the neural response for all three stimulus parameters was often
found in the onset component (i.e., during the first 40 ms follow-
ing stimulus onset), although the sustained and offset responses
were also informative in some cortical fields. These plots also
show that, while neurons that were most informative about tim-
bre and F0 were found in PPF, the most informative units about
azimuth were located in A1. The distributions of stimulus-related
information as a function of poststimulus time bear some resem-
blance to the population-averaged firing rate PSTHs shown in
Figure 2B. That is, periods of elevated firing rates in a given field
tended to be periods of high stimulus-related information. How-
ever, there was also considerable unit-to-unit variability in the
amount of stimulus-related information signaled and in how that
information was distributed over the duration of the response.
Informative units were found across a range of BFs in each field
(Fig. 3).
To compare the MI values across cortical fields and during
different response periods, we calculated the MI for timbre, F0,
and azimuth carried by spike rates within four separate 40-ms-
wide epochs of the neural response: the onset bin (10 –50 ms), a
sustained response bin (60 –100 ms), the offset bin (160 –200 ms),
and a late response bin well after stimulus offset (300 –340 ms).
For each epoch, the median MI for one of the stimulus attributes
was calculated across all units in each field. These values are
mapped onto their corresponding cortical fields in Figure 4 (gray
scale), allowing us to visualize the temporal evolution of infor-
mation about that attribute across the auditory cortex. This is
shown for timbre in the top row of Figure 4, where nearly all the
stimulus-related information was found in the onset response,
particularly in AAF. In contrast, information about pitch (Fig. 4,
middle row) was more distributed across A1, AAF, and the pos-
terior fields during the onset response, and remained high in the
posterior fields throughout the sustained response. Pitch infor-
mation in A1 rose again during the response to vowel offsets and
was relatively high in PPF even into the late response epoch.
Finally, azimuth information was more confined to the onset
responses in the auditory core (Fig. 4, bottom row) and was pres-
ent to a lesser extent in early PPF responses.
For each of the three stimulus parameters, a Kruskal–Wallis
test (
␣
⫽0.05), with pairwise, post hoc multiple comparisons
using Tukey’s honestly significant difference criterion (Tukey’s
HSD test) was conducted to examine whether the MI within each
response epoch differed across the cortex (Fig. 5). This analysis
showed that, for timbre and azimuth, the onset response was
generally more informative in the auditory core (A1 and AAF)
than in the belt regions studied (PPF, PSF, ADF). The sustained
Figure 2. Information for three sound features and average spike rates across the neural response. A, Each plot shows the MI between the relevant sound feature and the spike rate of a different
neuron. Information for vowel timbre, periodicity (F0), and azimuth is shown in three columns (left to right, respectively), and neurons from each of the five cortical fields are plotted separately
(rows).Eachtrace indicates,for a givenunit, thebias-correctedMI calculatedfrom the spikecount within20-ms-widetime windowspositioned inpoststimulustime asshown on thex-axis. Stimulus
presentation time is indicated by the black horizontal bar in the bottom panel. B, Corresponding poststimulus time histograms of responses to artificial vowels. The panels from top to bottom show
the average spike rates (mean ⫹SD; black and gray lines, respectively) of units recorded in the five fields (rows).
14568 •J. Neurosci., October 12, 2011 •31(41):14565–14576 Walker et al. •Feature Multiplexing in Auditory Cortex

response was most informative about azimuth in PPF, timbre in
AAF and PPF, and about F0 in PPF and PSF. The offset responses
of A1 neurons were more informative about timbre and, partic-
ularly, F0 than those of neurons in other fields, although offsets in
AAF and PPF carried more information about F0 than those in
PSF. Finally, the late spike rates of neurons measured 150 ms after
vowel offset were more informative for F0 in PPF than in most
other fields. This late response was also significantly more infor-
mative for timbre in PSF than in the rest of the cortex, although
the late MI values for both timbre and az-
imuth were low overall during this period
(note the y-axes).
These results show that, for many neu-
rons, the spike rate within one of the four
40 ms response windows described pro-
vides information about the pitch, timbre,
and azimuth of vowels, but this may not
be the most effective way of decoding the
spiking responses. We compared the in-
formation provided by the best of these
four 40 ms response windows for each
unit with four other spike codes: a binary
spike/no-spike code (Bin), the spike rate
calculated over the entire 320 ms duration
(R320), the relative first-spike latency
(Lat), and Victor’s binless algorithm for
classifying the temporal spiking patterns
(VB). Details of how each code was calculated
are provided in Materials and Methods.
Codes were compared using a Kruskal–
Wallis test with pairwise comparisons us-
ing Tukey’s HSD tests (Fig. 6). The results
showed that for all three stimulus features
the binary and full spike count codes pro-
vided less information than the other
three codes (Tukey’s HSD, p⬍0.05). For
pitch and azimuth, the first-spike latency
also provided less information than the 40
ms spike rate and Victor’s binless classifi-
cation of the temporal spiking patterns
(p⬍0.05). Therefore, the spike rate
within a single, well chosen, and relatively
short response window can provide a sub-
stantial portion of the information about
vowel timbre, pitch, and azimuth that is
available in the spike train.
Figure 2Ashows that, across all fields
and response bins, neural spike rates tended
to be more informative about the timbre
and F0 of vowels than their azimuthal posi-
tion. A Kruskal–Wallis analysis with pair-
wise Tukey’s HSD tests confirmed that, in
many cases, the median MI for F0 and
timbre was significantly greater than that
for azimuth, particularly for onset and
offset responses (data not shown). In con-
trast, in no cortical field or response win-
dow did the median azimuth information
exceed that of timbre or periodicity.
While the three stimulus parameter ranges
were not carefully matched in discrim-
inability, the stimuli presented in this ex-
periment were nevertheless very widely
spaced relative to ferret behavioral discrimination thresholds for
F0 (Walker et al., 2009) and timbre (our unpublished observa-
tions) of artificial vowels, and for the azimuth of noise bursts
(Parsons et al., 1999; Nodal et al., 2010). Ferrets can lateralize
these artificial vowels (Walker et al., 2009), but their acuity for
localizing these sounds in azimuth has not been tested. Given the
prevalence of monaural spectral cues and interaural level cues in
the high-frequency (⬎5 kHz) range (Schnupp et al., 2003), one
might expect localization accuracy to be better for noise than our
Figure 3. MI as a function of BF. In each scatter plot, the maximum MI value for each unit, across all tested response windows,
isplotted( y-axis) againsttheBF fortheneuron (x-axis).Peak MI valuesfor untuned neuronsare plotted tothe rightofeach scatter
plot. The 15 plots show data from each of the five cortical fields (rows) and for the three sound features (columns).
Figure 4. Population median information about timbre, pitch, and location across cortical fields and response periods. In each
map of auditory cortex, the gray scale of a cortical field indicates the median MI between a particular stimulus attribute (rows) and
spike rates during one of four different 40 ms response window (columns). MI medians were calculated across all units recorded in
the field. The gray scales were normalized separately for each stimulus attribute (far right), with darker shades indicating more
information.
Walker et al. •Feature Multiplexing in Auditory Cortex J. Neurosci., October 12, 2011 •31(41):14565–14576 • 14569

low-pass-filtered vowel sounds. We tested
this prediction in an additional ferret by
comparing the neural responses to our set
of vowels with the responses to broadband
noises (200 ms duration, 5 ms onset/offset
ramps) that varied across four spatial lo-
cations (⫺60, ⫺30, 0, and 30°) using the
same VAS filters. In the onset responses of
13 AAF neurons and the sustained re-
sponses of 20 neurons in PPF, the MI for
azimuth was up to 10 times larger for
noise bursts than for vowels (Fig. 7; paired
ttest, t⫽4.82, p⬍0.05).
The MI of neural spike rates for vowel
features were in the range of ⬃0.01– 0.6
bits, or ⬃1–30% of the total entropy for a
given feature, which falls within the range
of values reported in previous studies of
sensory coding by cortical cells (Panzeri et
al., 2001; Mrsic-Flogel et al., 2003; Nelken
et al., 2005; Goure´ vitch and Eggermont,
2010). Although the information pro-
vided by any one neuron is insufficient to
support highly accurate sensory discrimi-
nation, a small number of neurons pro-
viding 0.6 bits of independent timbre
information each would be sufficient to
discriminate the timbre of our sounds to a
high level of accuracy, despite the azimuth
and pitch confounds. While the onset
spike responses of 74% of simultaneously
recorded pairs of units in our dataset were
significantly correlated (Spearman’s cor-
relation; p⬍0.05), these correlations
were generally quite weak (mean r⫽0.12;
SD, 0.12), as previously reported for cor-
tical cells (Brosch and Schreiner, 1999; de
la Rocha et al., 2007). Thus, the units we
have recorded are largely, although not
entirely, independent in their responses,
and it is reasonable to expect that a small
population of these units could support
ferrets’ behavioral discrimination of the
pitch, timbre, and azimuth of vowels (see
behavioral results below).
Tukey’s HSD tests further showed that
onset responses in A1, AAF, and ADF
were, on average, more informative about
the timbre of vowels than vowel F0. In
contrast, there was more information
about sound F0 than about timbre or azi-
muth in the sustained responses of the
posterior fields (PPF and PSF), and in the
late responses of A1 and PPF. Thus, while
spectral timbre may modulate the onset of
cortical responses more strongly, modula-
tion of their spike rates by stimulus pitch
seems to persist for longer during the re-
sponse to a vowel.
These data therefore indicate that sensitivity to pitch, timbre,
and location is distributed differentially in the population re-
sponses across both time and cortical fields. But does this hold
true for individual units? That is, does the same neuron provide
information about different stimulus attributes at different times,
or do different neurons provide stimulus-related information in
each of these time windows? Figure 8 examines this question by
comparing MI values obtained from each unit during the onset
Figure5. Differencesin spike-rateinformation for timbre,pitch, andlocationacross corticalfields. Box-and-whisker plotsshow
the distribution within the auditory cortex of information about vowel timbre (top row), periodicity (middle row), and azimuth
(bottom row) carried by the spike rates of neurons during four different 40 ms response windows (columns). Within each plot,
results are grouped according to cortical field. MI values differed significantly across the auditory cortex for all comparisons
(Kruskal–Wallistests,p⬍0.05), exceptthoseof lateresponsesto azimuth(bottom right). Thelines at thetop ofeachplot indicate
significant pairwise differences in MI between individual fields (Tukey’s HSD tests, p⬍0.05). The ordinate axis is clipped at the
98th percentile for clarity.
Figure 6. Mutual information across five neural codes. Each panel shows the MI for a given stimulus feature as calculated using
each of five spike codes: a binary spike/no-spike (Bin), the overall spike rate (320 ms response period) (R320), the spike rate within
the most informative 40 ms response bin (R40), the first spike latency (Lat), and Victor’s binless classification of the temporal
dischargepatterns(VB). Linesabovethe box-and-whiskerplotsindicate significantdifferences(Kruskal–Wallis test,withpairwise
Tukey’s HSD tests, p⬍0.05). The ordinate axis is clipped at the 98th percentile for clarity.
14570 •J. Neurosci., October 12, 2011 •31(41):14565–14576 Walker et al. •Feature Multiplexing in Auditory Cortex

response with the MI in the sustained (Fig. 8A) or offset re-
sponses (Fig. 8B). Again, data are plotted separately for each
cortical field. This figure shows evidence for large-scale multi-
plexing of information about the three stimulus features across
different time periods within the population of neurons in a cor-
tical field. For example, Figure 8Ashows that all the units re-
corded in PPF transmitted more information about timbre (first
column) in their onset than in their sustained responses, whereas
the opposite was true for azimuth (third column). Because infor-
mation about a particular stimulus feature could be restricted to
particular response periods, this opens up the possibility of mul-
tiplexing within the responses of these neurons, with different
periods being used to encode different features.
But not all fields were so homogeneous. In some fields, two
types of unit were observed: one that primarily encodes the stim-
ulus parameter in the onset response, and another in which MI is
much higher during the offset response. An example of this is the
encoding of timbre in A1 (Fig. 8B, top row, left column). This
plot reveals that the most informative neurons fall into two sub-
populations: one encodes timbre during onset spike rates, but not
during offsets, whereas the second shows the opposite pattern. By
contrast, there are very few data points in the top right corner of
the scatter plots (i.e., neurons are rarely equally highly informa-
tive during onset and offset).
When we examined the time profile of pitch and timbre MI
within individual units, we found several examples of neurons
that represent these parameters of vowels during separate re-
sponse windows. This provides more direct support for multi-
plexing within individual neurons. Examples of multiplexing
within single neurons are shown in the seven panels of Figure 9.
These examples were chosen to illustrate the variety of ways in
which neurons can encode robust information about the timbre
and pitch of a vowel by modulating their spike rates in different
time windows. The examples shown come from A1, PPF, and
PSF, and all were classified as single units.
To estimate what proportion of units exhibit this coding
property, we developed an algorithm to identify those that mul-
tiplex F0 and timbre in this manner. In this algorithm, we exam-
ined the MI for each parameter in individual 20 ms windows
across the initial 320 ms of the response (as in Fig. 2A). First, we
required that the unit in question carried a minimum amount of
information about both pitch and timbre. Specifically, the unit
had to carry at least 0.01 bits of information about sound timbre
in at least one time window examined, and at least 0.01 bits of
pitch information in any other window (a criterion of 0.01 bits
was based on bootstrapping analyses, described below). Next, we
required that, in at least one 20 ms response window, the MI for
timbre was ⬎65% of the maximum timbre MI for that neuron,
while the pitch MI was ⬍35% of the maximum pitch MI (and
thus essentially “invariant” to pitch changes). Finally, we re-
quired that the reverse trend was found in another temporal win-
dow: pitch MI was ⬎65% of the maximum of the neuron, while
timbre MI was ⬍35% of maximum. A unit meeting all of these
criteria was classified as “multiplexing.” In this manner, a total of
71 units (11% of all units driven by artificial vowels) was classified
as multiplexers: 31 in A1 (25 single units and 6 multiunit clus-
ters), 7 in AAF (1 single), 22 in PPF (17 single), 10 in PSF (8
single), and 1 in ADF (0 single).
The highest MI value that an ideal neural response could pro-
vide about each of our stimulus features (the “stimulus entropy”)
is 2 bits, since there were four, equally probable stimulus values
for each stimulus parameter (see Materials and Methods). In our
dataset, the highest MI value obtained was near 0.5 bits (from a
PPF neuron), but for some cortical fields the maximum MI values
we estimated were much smaller than this. For example, the most
informative unit in PSF provided ⬃0.1 bits of information about
vowel timbre. To determine whether the estimated MI values for
any one neuron were greater than might be expected by chance,
we used a bootstrapping approach similar to that used in our bias
estimation method (see Materials and Methods) to derive a test of
the significance of the MI values. If the MI value calculated for a
given response window was larger than the 95th percentile of the
MI values calculated for scrambled versions of these data (boot-
strapped 500 times), the former was considered statistically
significant (p⬍0.05) and we concluded that this unit may con-
structively contribute to categorizing sounds along the feature
dimension in question. This procedure was repeated for 20-ms-
wide windows that were positioned from 0 to 700 ms poststimu-
lus offset, in 10 ms steps.
The proportions of significantly informative neurons in each
20 ms poststimulus time window for each stimulus attribute
within each cortical field are shown in Figure 10 A. Both the over-
all proportion of informative units and the time course of the
informative spiking windows varied substantially between fields
and stimulus attributes. Common to all fields and stimulus fea-
tures, however, is that the highest proportions of neurons were
informative during the onset response (i.e., during the initial 50
ms). All five fields also share the property that the percentage of
timbre-informative units peaked earlier than the percentage of
pitch-informative units. In the auditory core (A1 and AAF), the
proportion of azimuth-informative units also peaked before
pitch-related information. Finally, there was a general tendency
for pitch information to persist longer than timbre or azimuth
information, particularly in A1 and the posterior fields.
Figure 10 Aemphasizes that the majority of driven units in our
sample carried robust information about pitch and timbre at
some point in the neural response. Across the five fields, 80% of
units carried robust information about vowel timbre in the pres-
ence of pitch and azimuth changes (i.e., these units contained
significant information in ⬎5% of the time bins examined). By
the same metric, 83% of units were robustly informative about
pitch, while 61% of units provided robust information about
azimuthal location.
Figure 10Aalso reveals differences between cortical fields in
the manner in which an individual sound attribute is encoded.
Figure 7. Spike-rate information for the azimuth of broadband noises and artificial vowels.
The scatter plot shows the MI of auditory cortical responses for the azimuth of artificial vowels
(x-axis) and broadband noises ( y-axis). Units were recorded in AAF (circles; n⫽13) and PPF
(triangles; n⫽20). The dotted line indicates equality.
Walker et al. •Feature Multiplexing in Auditory Cortex J. Neurosci., October 12, 2011 •31(41):14565–14576 • 14571

For instance, although timbre information was found predomi-
nantly in the onset response in all cortical areas, the sustained
portion of the neural response was also informative for timbre in
A1, AAF, and PPF, but not in PSF or ADF. Similarly, sustained
responses often carried F0 information, but overwhelmingly so in
A1 and PPF.
Informative spike count windows tended to correspond to
portions of the PSTH where average spike rates were well above
the spontaneous level (compare Figs. 2B,10A). However, the
spike rates of a subset of neurons were informative even at times
in the response where the overall spike rate of the population of
neurons within the cortical field was near spontaneous firing
levels. For example, the average spike rate from 200 to 250 ms
poststimulus onset was near the spontaneous rate in all fields, yet
a substantial number of units in A1 and PPF encoded significant
information about the periodicity of the sound during this time
window. This occurs because some units here reliably produced
sustained responses to a small subset of the stimulus conditions.
Conversely, although we observed a large onset response in ADF,
these onset responses were often produced in response to most
stimulus conditions and so carried little information about either
sound pitch or azimuth.
We next examined the buildup of information about each
stimulus feature across the five cortical fields. For each unit, MI
was calculated for the spike rate within a time window beginning
at sound onset, for durations of 5–150 ms, sampled in 5 ms steps.
For the majority of neurons in most fields, the MI increased over
the first 40 ms, and then plateaued at some maximum value. The
exception was A1, where a sharp peak within the initial 40 ms was
typical. To quantify the speed of the buildup of stimulus-related
information, we determined the latency at which the MI reached
75% of its maximum for each unit and stimulus attribute. The
distributions of this information latency for each of the five cor-
tical fields investigated are shown in Figure 10B. Note that infor-
mation about timbre built up ⬃10 ms faster than information
about pitch in A1, and ⬃30 ms faster in PPF. Furthermore, the
MI for azimuth built up earlier than timbre and pitch MI in AAF
neurons, but later than timbre MI in A1 and PPF.
These observations lead to the prediction that an ideal ob-
server should be able to judge the spectral formant structure (and
hence the identity) of a vowel slightly faster than the pitch of a
vowel. We therefore set out to test whether ferrets make vowel
timbre judgments faster than pitch judgments. Four ferrets were
trained on a go/no-go change detection task in which they were
required to withdraw from a nose poke hole for a water reward
when either the pitch or spectral timbre of a repeating artificial
vowel changed. The stimuli were two-formant versions of those
used in our extracellular recordings with a 200 Hz /a/ as the
repeating reference. A two-way ANOVA was used to examine the
effects of stimulus attribute (pitch or timbre) and difficulty level
(three target pitches or three target vowels) on ferrets’ percentage
correct scores. Mean percentage correct scores across ferrets are
shown by the histograms in Figure 11A, along with the perfor-
mance of each animal. There was no difference in ferrets’ per-
centage correct scores on the pitch and timbre task (two-way
ANOVA, p⫽0.210), or in their performance across different
levels of difficulty (p⫽0.170). Reaction times (i.e., withdrawal
times from target onset) were calculated over trials in which fer-
Figure 8. Spike-rate information for timbre, pitch, and location during onset, sustained, and offset responses. A, Scatter plots of the MI based on spike rates for a 40 ms response window during
the onset (x-axis) and sustained ( y-axis) period of the neural response. Each circle represents a different unit, with the data plotted separately for units located in the five auditory cortical fields
(rows),andfor informationabout the timbre,F0, andazimuthof thesound (columns). Thedotted linesaredrawn atequality. B,Scatterplots, asinA, comparingthe MIinthe onset(x-axis) and offset
(y-axis) response windows.
14572 •J. Neurosci., October 12, 2011 •31(41):14565–14576 Walker et al. •Feature Multiplexing in Auditory Cortex

rets made a correct detection. As shown in
Figure 11B, the ferrets reacted, on aver-
age, faster to timbre changes than to pitch
changes (two-way ANOVA, p⫽0.007),
with no overall difference across the diffi-
culty levels (p⫽0.120). Reaction times on
the timbre task were ⬃30 ms faster
(mean ⫾SD, 235.3 ⫾26.6 ms) than on
the pitch task (264.7 ⫾25.8 ms). The re-
action time difference observed on these
tasks is therefore consistent with our find-
ing that the incidence of significant units
(Fig. 10A) and MI buildup over the re-
sponse (Fig. 10B) both peak earlier for
timbre than for pitch cues in auditory
cortex.
Discussion
Our previous work demonstrated that
most neurons in ferret auditory cortex are
sensitive to multiple perceptual attributes
of sound, including the pitch, timbre, and
spatial location of the sound (Bizley et al.,
2009). This form of “feature combination
sensitivity” may help with perceptual
binding, whereby several acoustical fea-
tures must be attributed to a single sound
source. However, it raises the problem of
perceptual invariance. The melody of a fa-
miliar song can be recognized even when
it is played on different musical instru-
ments placed at different locations. If cor-
tical neurons are tuned to combinations
of features, how can a listener attend to
one attribute only, and isolate it from
other, irrelevant features? We have shown
that, despite the abundance of feature
combination sensitivity in auditory cor-
tex, many neurons remain informative
about the timbre, pitch, or azimuth of
vowels despite large changes in the other
two features.
Differences across cortical fields
Although neural responses in AAF car-
ried, on average, more information about
the spectral identity of vowels than in
other fields, particularly during the onset
response, a small group of units in PPF
were the most informative about timbre.
Pitch information was highest in posterior
fields, whereas spatial information was
highest in A1, in terms of both the median
MI across neurons in the cortical fields
and the maximum MI in individual units.
The increase in information about spec-
tral timbre and pitch in the posterior
bank, relative to A1 and AAF, is analogous
to the invariance increase documented
within the visual ventral stream (Ison and
Quiroga, 2008). Thus, projections from
auditory core to the posterior bank of the
ectosylvian gyrus in the ferret could mark
Figure9. Individualneurons multiplexrepresentations ofspectraltimbre andF0 intheirresponses toartificial vowels. Eachplot
showsMItime tracesfrom asingleneuron thatwas classifiedas“multiplexing” F0and timbreinformation.The robustMI fortimbre
(dotted line) and periodicity (solid line) were calculated separately in a sliding, 20 ms window. The field from which a given neuron
was recorded is indicated at the top of each plot.
AB
Figure10. Timeprofiles ofinformativeneural populations.A, The proportionof units thatcarried significantinformationabout
voweltimbre,F0, andazimuthare plottedacross20 mstimebins (x-axis)andthe fivecorticalfields (rows).Foreach field,thenoise
in MI values was estimated as the average MI of units during a 20 ms time bin positioned 550 ms after the offset of the sound. The
MI for each time bin during the 320 ms response period was thus considered statistically significant if it was larger than the MI
calculated for all units during this noise bin. The gray dashed line at 5% indicates the proportion of units that would pass our
significance test by chance. B, MI was calculated in time windows positioned at stimulus onset and extending from 5 to 320 ms in
duration (tested in 5 ms steps). The response window width at which the MI for a particular unit and stimulus feature reached 75%
of its maximum MI value was determined. Histograms of these window widths are shown, across five cortical fields (rows) and for
the three stimulus parameters (line styles).
Walker et al. •Feature Multiplexing in Auditory Cortex J. Neurosci., October 12, 2011 •31(41):14565–14576 • 14573

the beginning of an auditory stream for object recognition (Raus-
checker, 1998; Tian et al., 2001).
We found that cortical fields also differed in the time signa-
tures of their responses. Neurons in the auditory core tended to
respond phasically to sound onsets and offsets, while in the belt
regions tonic responses were common. Moreover, each of the
response windows analyzed could be independently informative
about the location, periodicity, and timbre of vowels. Other au-
thors have reported sustained responses in A1, which sometimes
lasted well beyond stimulus offset (Moshitch et al., 2006; Camp-
bell et al., 2010). We found that many PPF units carried pitch
information up to 200 ms after vowel offset. Therefore, while
sustained responses to steady-state sounds may be more com-
mon in the auditory cortex of alert animals (Wang et al., 2005;
Walker et al., 2008), sustained and informative spiking patterns
can also be observed under anesthesia, especially in the higher
cortical fields. The medetomidine/ketamine anesthesia used here
does not obviously alter the ability of auditory cortical neurons to
distinguish natural sounds (Walker et al., 2008), undergo stimu-
lus timing-dependent plasticity (Dahmen et al., 2008), or adapt
to sound contrast (Rabinowitz et al., 2011). However, auditory
cortical representations can change when animals engage in be-
havioral tasks (Fritz et al., 2005; Otazu et al., 2009; Lee and
Middlebrooks, 2011). Whether task demands affect the manner
in which neurons “multiplex” information remains to be charac-
terized in future experiments.
The spiking response of individual units accounted for ⬃2–
30% of the total entropy for any one vowel feature, which is in
line with previous findings (Panzeri et al., 2001; Mrsic-Flogel et
al., 2003; Nelken et al., 2005; Goure´ vitch and Eggermont, 2010)
and suggests that ferrets’ discrimination decisions are likely to be
based on small populations of units. Studies that have compared
behavioral judgments to neural responses in the auditory
(Stecker et al., 2005; Walker et al., 2008; Bizley et al., 2010), visual
(Britten et al., 1996), and somatosensory (Herna´ ndez et al., 2000)
cortices indicate that perceptual decisions likely reflect the activ-
ity of populations of neurons, which can carry more stimulus-
related information than single neurons alone. Chechik et al.
(2006) have shown the information content of auditory cortical
neurons to be highly independent, so feature information should
improve substantially when the largely uncorrelated responses of
cortical neurons are pooled.
Multiplexing neural representations of stimulus features
A novel finding of the present work is the demonstration of fea-
ture multiplexing in the auditory cortex. By transmitting multi-
ple signals simultaneously but unambiguously over a shared
channel, multiplexing potentially enables individual neurons to
encode features along several perceptual dimensions, which may
help solve the binding problem while still allowing for perceptual
invariance. Other forms of multiplexing in neural responses have
been described. For example, in primary visual cortex, stimulus
contrast is represented by spike times on a fine timescale (10 –30
ms), whereas other visual attributes, such as orientation, spatial
frequency, and texture, are represented on coarser (up to ⬃100
ms) timescales (Victor and Purpura, 1996). There is evidence for
this form of multiplexing in our data (Fig. 10A), where represen-
tations of sound periodicity often persisted over longer durations
than timbre responses in A1, PPF, and PSF. In the visual and
auditory cortices, multiplexing can occur across even wider time-
scales, as local field potential oscillations have been shown to
carry information that complements the spike-based codes of
single neurons (Eggermont and Smith, 1995; Montemurro et al.,
2008; Kayser et al., 2009).
Populations of neurons have additionally been shown to rep-
resent multiple sound features by using a combination of neural
codes. Chase and Young (2005, 2006, 2008) showed that the spike
rates of neurons in the inferior colliculus often carried informa-
tion about more than one type of localization cue, with consid-
erable confounded information between cue combinations
(Chase and Young, 2005). By incorporating first-spike latency
and spike pattern codes with this spike rate code, these neurons
could represent different localization cues more independently
(Chase and Young, 2006, 2008).
We provide evidence for an additional type of multiplexing in
the responses of auditory cortical neurons. Here, neurons multi-
plex representations of different sound features by independently
modulating their firing rate within discrete time windows
throughout their response to a sound. We found that single neu-
rons can thereby provide mutually invariant representations of
sound pitch and timbre. Previous studies have shown that spe-
cific temporal windows can be informative about the basic prop-
erties of sound. Thus, the frequency of pure tones can be encoded
in the onset, sustained, and offset responses of cortical neurons
(Takahashi et al., 2004; Moshitch et al., 2006; Qin et al., 2007;
Fishman and Steinschneider, 2009), and the spectral content of
time-varying sounds is represented by spikes evoked throughout
the stimulus duration (Qin et al., 2008; Brown and Harrison,
2009). The present finding expands on these results to show that
such temporally delimited representations of multiple features
can coexist within a single neuron, while remaining essentially
invariant to one another.
We found that the neuronal time signature that comprises this
form of pitch and timbre multiplexing varies across single neu-
rons and is often temporally complex (Fig. 9). The responses of
neurons that multiplex timbre and pitch representations also
often contained time windows in which the spike rate was mod-
A
B
Figure 11. Ability of ferrets to detect changes in the pitch or timbre of artificial vowels. A,
Average performance on a go/no-go change detection task across four ferrets (mean bars ⫹
SEM), along with the mean score for each ferret (symbols, averaged across sessions). Data are
grouped according to the target value, which could change in pitch (gray bars) or timbre (white
bars) from a 200 Hz /a/ reference. B, Average withdrawal time following target onset for four
ferrets (mean bars ⫹SEM), along with the average withdrawal time for each ferret (symbols).
Reaction times were calculated only for correct trials.
14574 •J. Neurosci., October 12, 2011 •31(41):14565–14576 Walker et al. •Feature Multiplexing in Auditory Cortex

ulated by both these sound attributes. Therefore, upstream neu-
rons could extract either confounded or independent stimulus
attributes from this temporally defined spike rate code, depend-
ing on their window of integration. The biophysical mechanisms
required to read out temporal input sequences have recently been
demonstrated within cortical dendrites (Branco et al., 2010). The
multiplexed responses described here would predict that up-
stream cortical neurons should exhibit a range of temporal inte-
gration windows. This remains a testable hypothesis for future
studies of cortical physiology, particularly with intracellular re-
cording techniques.
Timbre information precedes pitch
At the population level, the time windows in which each feature
was represented varied substantially across the five cortical fields
examined, but timbre was generally encoded earlier in the onset
response than pitch. An analysis of the buildup of information
across the response confirmed that, for most neurons, timbre-
related information peaked earlier than information about sound
pitch. Importantly, this result agrees with our behavioral data, in
which ferrets’ reaction times in change detection tasks were faster
for timbre than for pitch.
Despite this temporal congruence of the behavioral and phys-
iological responses, reaction times on the timbre task were ⬃30
ms faster than on the pitch task, whereas the latency of timbre
information in our recordings was usually ⬃10 –30 ms ahead of
pitch information, depending on the cortical field. We also found
that pitch information often persisted for longer than timbre
information, extending into the sustained and late periods of the
response, particularly in the posterior bank. This could delay
pitch judgments so that information is consolidated across a lon-
ger time window.
Human psychophysical studies have shown that listeners can
identify vowels based on only a fraction of a cycle of the F0 of the
vowel, whereas reliable pitch judgments require the presentation
of approximately four cycles (Gray, 1942; McKeown and Patter-
son, 1995). This is consistent with the idea that vowel identity can
be determined by detecting formant peaks across tonotopically
organized frequency channels, and then comparing this activa-
tion pattern against memorized spectral templates (Conley and
Keilson, 1995). This computation lends itself to parallel spectral
processing in the ascending auditory pathway, and one might
expect the brain to be able to perform this very quickly. In con-
trast, the periodicity pitch of a complex sound is likely to be
encoded as the autocorrelation of spike times across neurons,
which must be calculated over several periods of the waveform
(Cariani, 1999). Our data confirm that formant recognition is
rapid and precedes pitch recognition in cortical responses as well
as behaviorally.
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