Interdependent Encoding of Pitch, Timbre, and Spatial Location in Auditory Cortex

Abstract

Because we can perceive the pitch, timbre, and spatial location of a sound source independently, it seems natural to suppose that cortical processing of sounds might separate out spatial from nonspatial attributes. Indeed, recent studies support the existence of anatomically segregated "what" and "where" cortical processing streams. However, few attempts have been made to measure the responses of individual neurons in different cortical fields to sounds that vary simultaneously across spatial and nonspatial dimensions. We recorded responses to artificial vowels presented in virtual acoustic space to investigate the representations of pitch, timbre, and sound source azimuth in both core and belt areas of ferret auditory cortex. A variance decomposition technique was used to quantify the way in which altering each parameter changed neural responses. Most units were sensitive to two or more of these stimulus attributes. Although indicating that neural encoding of pitch, location, and timbre cues is distributed across auditory cortex, significant differences in average neuronal sensitivity were observed across cortical areas and depths, which could form the basis for the segregation of spatial and nonspatial cues at higher cortical levels. Some units exhibited significant nonlinear interactions between particular combinations of pitch, timbre, and azimuth. These interactions were most pronounced for pitch and timbre and were less commonly observed between spatial and nonspatial attributes. Such nonlinearities were most prevalent in primary auditory cortex, although they tended to be small compared with stimulus main effects.

Full text

Behavioral/Systems/Cognitive
Interdependent Encoding of Pitch, Timbre, and Spatial
Location in Auditory Cortex
Jennifer K. Bizley,
1
Kerry M. M. Walker,
1
Bernard W. Silverman,
2
Andrew J. King,
1
and Jan W. H. Schnupp
1,3
1
Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom,
2
St Peter’s College, Oxford OX1 3DL, United
Kingdom, and
3
Robotics, Brain, and Cognitive Sciences Department, Italian Institute of Technology, 16163 Genova, Italy
Because we can perceive the pitch, timbre, and spatial location of a sound source independently, it seems natural to suppose that cortical
processing of sounds might separate out spatial from nonspatial attributes. Indeed, recent studies support the existence of anatomically
segregated “what” and “where” cortical processing streams. However, few attempts have been made to measure the responses of indi-
vidual neurons in different cortical fields to sounds that vary simultaneously across spatial and nonspatial dimensions. We recorded
responses to artificial vowels presented in virtual acoustic space to investigate the representations of pitch, timbre, and sound source
azimuth in both core and belt areas of ferret auditory cortex. A variance decomposition technique was used to quantify the way in which
altering each parameter changed neural responses. Most units were sensitive to two or more of these stimulus attributes. Although
indicating that neural encoding of pitch, location, and timbre cues is distributed across auditory cortex, significant differences in average
neuronal sensitivity were observed across cortical areas and depths, which could form the basis for the segregation of spatial and
nonspatial cues at higher cortical levels. Some units exhibited significant nonlinear interactions between particular combinations of
pitch, timbre, and azimuth. These interactions were most pronounced for pitch and timbre and were less commonly observed between
spatial and nonspatial attributes. Such nonlinearities were most prevalent in primary auditory cortex, although they tended to be small
compared with stimulus main effects.
Key words: auditory cortex; tuning; sound; spike trains; vocalization; localization; parallel; hearing
Introduction
One of the most important functions of the auditory system is to
identify and discriminate vocal calls, such as speech sounds. This
task requires the listener to process several complex perceptual
properties of a single auditory object, and so is likely to engage a
number of functionally distinct cortical areas in parallel. To iden-
tify a spoken vowel, for example, the auditory system must deter-
mine the positions of formant peaks in the spectral envelope of
the vowel sound (Peterson and Barney, 1952). Vowel discrimina-
tion is therefore a timbre discrimination task. Meanwhile, in ad-
dition to its timbre, the pitch of a spoken vowel can convey in-
formation about the speaker’s identity (Gelfer and Mikos, 2005)
and emotional state (Fuller and Lloyd, 1992; Reissland et al.,
2003), and so the periodicity of the vowel must also be analyzed.
Finally, localization of the speaker requires processing binaural
disparity cues and monaural spectral cues that are independent of
the timbre and pitch of the vowel. Because many other species
generate vocalizations in an entirely analogous manner, process-
ing the pitch, timbre, and location of vowel-like sounds is an
important task for the mammalian auditory system in general.
Building on earlier studies in the visual system (Mishkin and
Ungerleider, 1982; Goodale and Milner, 1992), it is widely
thought that a separation of function exists within higher-order
auditory processing streams, such that more posterior, or dorsal,
cortical areas mediate sound localization, whereas more anterior,
or ventral, areas are responsible for object identification (Raus-
checker et al., 1997; Romanski et al., 1999; Kaas and Hackett,
2000; Alain et al., 2001; Maeder et al., 2001; Tian et al., 2001;
Warren and Griffiths, 2003; Barrett and Hall, 2006; Lomber and
Malhotra, 2008). Consequently, we might expect the pitch and
the timbre of a complex auditory stimulus to be represented in a
separate region from its spatial location. However, it is not un-
common for listeners to find themselves in cluttered acoustic
environments, where the pitch, timbre, and spatial location of
several sound sources may have to be tracked simultaneously.
Separating the neural processing of different perceptual at-
tributes could make this task harder, by creating a sort of “bind-
ing problem.”
Previous work has focused almost exclusively on differences
across cortical areas in the representation of just one parameter,
such as sound-source location (Recanzone, 2000; Stecker et al.,
2005; Harrington et al., 2008) or pitch (Bendor and Wang, 2005).
The extent to which these attributes are encoded independently
has not previously been investigated. Here we used “artificial
vowel” sounds to investigate how pitch (as determined by the
Received Oct. 2, 2008; revised Jan. 9, 2009; accepted Jan. 13, 2009.
This work was supported by the Biotechnology and Biological Sciences Research Council (Grants BB/D009758/1
to J.W.H.S., A.J.K., and J.K.B.), the Engineering and Physical Sciences Research Council (Grant EP/C010841/1 to
J.W.H.S.), a Rothermere Fellowship and Hector Pilling Scholarship to K.M.M.W., and a Wellcome Trust Principal
Research Fellowship to A.J.K. We are grateful to Israel Nelken for valuable discussion and comments on this
manuscript.
CorrespondenceshouldbeaddressedtoDr.JenniferK.Bizley,DepartmentofPhysiology,Anatomy,andGenetics,
Sherrington Building, University of Oxford, Parks Road, Oxford OX1 3PT, UK. E-mail: jennifer.bizley@dpag.ox.ac.uk.
DOI:10.1523/JNEUROSCI.4755-08.2009
Copyright © 2009 Society for Neuroscience 0270-6474/09/292064-12$15.00/0
2064 •The Journal of Neuroscience, February 18, 2009 •29(7):2064 –2075

pulse rate), timbre (as determined by formant filter frequencies),
and location are encoded within and across five identified areas of
the auditory cortex of the ferret. Our aim was to determine the
degree to which these perceptual attributes are represented in a
mutually independent manner in both primary and secondary
cortical fields, and to look for evidence for feature specialization
across these fields.
Materials and Methods
Animal preparation. All animal procedures were approved by the local
ethical review committee and performed under license from the UK
Home Office in accordance with the Animal (Scientific Procedures) Act
1986. Five adult, female, pigmented ferrets (Mustela putorius) were used
in this study. All animals received regular otoscopic examinations before
the experiment, to ensure that both ears were clean and disease free.
Anesthesia was induced by a single dose of a mixture of medetomidine
(Domitor; 0.022 mg/kg/h; Pfizer) and ketamine (Ketaset; 5 mg/kg/h; Fort
Dodge Animal Health). The left radial vein was cannulated and a contin-
uous infusion (5 ml/h) of a mixture of medetomidine and ketamine in
physiological saline containing 5% glucose was provided throughout the
experiment. The ferrets also received a single, subcutaneous, dose of 0.06
mg/kg/h atropine sulfate (C-Vet Veterinary Products) and, every 12 h,
subcutaneous doses of 0.5 mg/kg dexamethasone (Dexadreson; Intervet
UK) to reduce bronchial secretions and cerebral edema, respectively. The
ferret was intubated, placed on a ventilator (7025 respirator; Ugo Basile)
and supplemented with oxygen. Body temperature, end-tidal CO
2
, and
the electrocardiogram (ECG) were monitored throughout the experi-
ment. Experiments typically lasted between 36 and 60 h.
The animal was placed in a stereotaxic frame and the temporal muscles
on both sides were retracted to expose the dorsal and lateral parts of the
skull. A metal bar was cemented and screwed into the right side of the
skull, holding the head without further need of a stereotaxic frame. On
the left side, the temporal muscle was largely removed, and the suprasyl-
vian and pseudosylvian sulci were exposed by a craniotomy, exposing
auditory cortex (Fig. 1) (Kelly et al., 1986). The dura was removed and
the cortex covered with silicon oil. The animal was then transferred to a
small table in an anechoic chamber (IAC).
Stimuli. Sounds were generated using TDT system 3 hardware
(Tucker-Davis Technologies) and MATLAB (MathWorks), and pre-
sented through customized Panasonic RPHV297 headphone drivers.
Closed-field calibrations were performed using a one-eighth inch con-
denser microphone (Bru¨el and Kjær), placed at the end of a model ferret
ear canal, to create an inverse filter that ensured the driver produced a flat
(⬍⫾5 dB) output.
Pure tone stimuli were used to obtain frequency response areas
(FRAs), both to characterize individual units and to determine tonotopic
gradients, so as to confirm the cortical field in which any given recording
was made. The tones used ranged, in 1/3-octave steps, from 200 Hz to 24
kHz, and were 100 ms in duration (5 ms cosine ramped). Intensities
ranged from 10 to 80 dB SPL in 10 dB increments. Each frequency-level
combination was presented pseudorandomly at least 3 times, at a rate of
one per second. Artificial vowel stimuli were created in MATLAB, using
an algorithm adapted from Malcolm Slaney’s Auditory Toolbox (http://
cobweb.ecn.purdue.edu/⬃malcolm/interval/1998-010/). Click trains
with a duration of 150 ms and a repetition rate corresponding to the
desired fundamental frequency were passed through a cascade of four
bandpass filters to impart spectral peaks at the desired formant frequen-
cies. The vowel sounds were normalized to have equal root-mean-square
amplitudes, and calibrations were performed using a one-eighth inch
condenser microphone (Bru¨el and Kjær) 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 to the artificial vowel sounds. A series of measurements
including head size, sex, body weight, and pinna size, were taken from
each ferret to select the best match from our extensive library of ferret
head-related transfer function recordings. We have shown previously
that ferret spectral localization cue values scale with the size of the head
and external ears (Schnupp et al., 2003). Sound-source direction cues
were generated by convolving the artificial vowel sounds with minimum
phase filters that imparted the appropriate interaural level differences
and spectral cues corresponding to a particular direction in the horizon-
tal plane, and which at the same time equalized out any differences in the
headphone transfer functions that had been revealed during headphone
calibration. Small delays were then introduced in the sound waveforms
to generate appropriate interaural time differences.
We presented sounds from four virtual sound-source directions
(⫺45°, ⫺15°, 15°, and 45° azimuth, at 0° elevation) and used four sound
pitches with F0 equal to 200, 336, 565, and 951 Hz. Four timbres were
chosen: /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 permutation of
these 4 pitches by 4 timbres by 4 source directions gave us a stimulus set
of 64 sounds.
Data acquisition. Recordings were made with silicon probe electrodes
(Neuronexus Technologies). In two animals, we used electrodes with an
8⫻4 configuration (8 active sites on 4 parallel probes, with a vertical
spacing of 150
␮
m). In a small number of recordings in one of these
animals, and in another animal, we used electrodes with a 16 ⫻2 config-
uration (16 active sites spaced at 100
␮
m intervals on each of two probes).
In the final two animals, electrodes with 4 ⫻4 and 16 ⫻1 configurations
were used (100 –150
␮
m spacing of active sites on each probe). The
electrodes were positioned so that they entered the cortex approximately
orthogonal to the surface of the ectosylvian gyrus. A photographic record
was made of each electrode penetration to allow later reconstruction of
the location of each recording site relative to anatomical landmarks (sur-
face blood vessels, sulcal patterns), to allow us to construct functional
maps of the auditory cortex.
The neuronal recordings were bandpass filtered (500 Hz to 5 kHz),
amplified (up to 20,000 times), and digitized at 25 kHz. Data acquisition
and stimulus generation were performed using BrainWare (Tucker-
Davis Technologies).
Data analysis. Spike sorting was performed off-line. Single units were
isolated from the digitized signal either by manually clustering data ac-
cording to spike features such as amplitude, width, and area, or by using
an automated k-means clustering algorithm, in which the voltage poten-
tial at 7 points across the duration of the spike window served as vari-
ables. We also inspected auto-correlation histograms, and only cases in
which the interspike-interval histograms revealed a clear refractory pe-
riod were classed as single units.
Composite map generation. Analysis of responses to vowel stimuli was
performed blind to animal number or the position of the electrode pen-
etration relative to anatomical landmarks. Before examining how the
responses to vowel stimuli varied as a result of their cortical location, each
penetration was first assigned to a cortical field on the basis of the re-
sponses of units to simple stimuli. This was done by measuring pure-tone
FRAs at all recording sites, and comparing these to previously docu-
mented physiological criteria for each of the fields that have been char-
acterized in ferret auditory cortex (Bizley et al., 2005). Penetrations were
assigned to a given cortical field according to the characteristic frequency
(CF) and tuning properties derived from the FRA and the latency and
duration of the response, together with photographs recording the loca-
tion of the electrode penetrations on the cortical surface and the overall
frequency organization obtained for each animal.
Having established the recording locations within each individual an-
imal and noted that there were consistent trends in the responses to
vowel sounds between cortical fields across animals, we established a
“composite” auditory cortex map. Field boundaries were determined on
the basis of the responses to pure tones and noise bursts, but blind to the
responses to vowel sounds. This approach has previously been used to
investigate the representation of multisensory responses in ferret audi-
tory cortex (Bizley and King, 2008). To create the composite map, the
penetration locations and cortical field boundaries for each individual
animal were projected onto a single animal frequency map derived using
optical imaging of intrinsic signals. This map was a representative exam-
ple taken from Nelken et al. (2004). This procedure was performed sep-
arately for each animal. Morphing each animal’s cortical map onto a
Bizley et al. •Pitch, Timbre, and Location Coding in Cortex J. Neurosci., February 18, 2009 •29(7):2064 –2075 • 2065

Figure 1. Stimuli and example responses. A, Frequency spectra for 16 of the artificial vowel stimuli used in this experiment. The four timbres used (corresponding to the vowels /a/, /␧/, /u/, and
/i/) are shown in different columns, with the four pitches (F0 of 200, 336, 565, and 951 Hz) shown in different rows. Each of these stimuli was also presented at one of four different virtual sound
directions(⫺45°, ⫺15°, 15°, and 45° azimuth).B,C, Spike rasterplots from twodifferent cortical neurons inresponse to all64 stimulus combinations.In each case,the same dataare replotted three
times,organized either according to stimulus azimuth(Az), pitch (F0),or timbre (vowel,vID). Both ofthese units producedresponses that wereclearly dependent onat least twoof the threestimulus
dimensions.
2066 •J. Neurosci., February 18, 2009 •29(7):2064 –2075 Bizley et al. •Pitch, Timbre, and Location Coding in Cortex

single example in this way allowed the data from each animal to be
superimposed in a bias-free manner.
Results
Responses to stimulus pitch, timbre, and location
We used a set of 64 artificial vowel sounds, comprising all possible
combinations of four spatial locations, four pitches, and four
timbres. The parameter values were chosen to be quite widely
spaced along each of the three dimensions. VAS stimuli were
presented at ⫺45°, ⫺15°, 15°, and 45° azimuth at 0° elevation,
with negative azimuths denoting locations to the animal’s right,
contralateral to the recording sites. Fundamental frequencies of
200, 336, 565, and 951 Hz were used, and the four timbres corre-
sponded to the vowels: /a/, /␧/, /u/, and /i/. These parameter
ranges were chosen to make the stimuli easily discriminable along
each perceptual dimension, both for human listeners, and, as far
as we know from available psychoacoustical data, also for our
animal model species, the ferret. The azimuth spacing of 30°
corresponds approximately to two to three ferret behavioral just
noticeable difference (JND) limens (Parsons et al., 1999). The
perceptual distance of the pitch steps used here (0.75 octaves) is
similarly approximately twice as wide as the ferrets’ JNDs
(Walker et al., 2009). The ferrets’ ability to discriminate the spec-
tral envelopes associated with the four different vowels has not
yet been formally investigated, but preliminary experiments have
demonstrated that ferrets rapidly learn to discriminate the iden-
tity of these vowel sounds and do so across at least a two octave
range of pitches (J. K. Bizley, K. M Walker, A. J. King, and J. W. H.
Schnupp, unpublished observations).
Each artificial vowel stimulus was 150 ms long. Figure 1A
shows the frequency spectra for the 16 possible combinations of
pitch and timbre. This illustrates that both timbre and pitch
changes affect the spectral envelope of the sounds, and presenting
these sounds from different virtual directions can introduce fur-
ther changes in the spectral envelope. Yet although changes in
location, pitch, and timbre all affect the sound spectrum, the
perceptual consequences of these changes are quite distinct. If the
perceptual distinction between pitch, timbre, and location is re-
flected at the level of neuronal discharges in auditory cortex, then
this stimulus set ought to reveal this separation according to per-
ceptual categories.
Extracellular recordings were performed using multisite sili-
con electrodes in anesthetized ferrets. We sampled ⬎900 acous-
tically sensitive recording sites. At 615 of these, we were able to
obtain stable recordings of neural responses to 30 – 40 presenta-
tions of each of the 64 artificial vowel stimuli, which were pre-
sented in a randomly interleaved order. Three hundred twenty-
four recordings were from single units and 292 were small
clusters of units. Because we were unable to find any systematic
difference in the response properties of single units and small unit
clusters, the term “unit” will be used to refer to both groups.
We observed a rich variety of response types across units, and
responses were frequently clearly modulated by more than one,
and often all three, stimulus dimensions. Figure 1, Band C, shows
the responses from two different units. In each case, the three
panels show the same data plotted three times, with the 64 stimuli
ordered into groups of 16 with a common azimuth, pitch, or
timbre (first, second, and third panels, respectively). These exam-
ples illustrate a common finding: tuning to stimulus pitch, tim-
bre, or azimuth alone did not adequately describe the responses
of these units and their responses could not be captured satisfac-
torily as a single spike count value. Rather, neurons often showed
a degree of sensitivity to each parameter (e.g., Fig. 1B), and/or to
particular combinations of parameters (Fig. 1C) in a time-
dependent manner. Further examples are illustrated in supple-
mental Figure 1, available at www.jneurosci.org as supplemental
material.
To examine these stimulus effects, we constructed post stim-
ulus time histogram (PSTH) matrices, in which data were sorted
according to two of the three stimulus parameters and pooled
across the third. Figure 2, Aand C, show such PSTH matrices for
the unit illustrated in Figure 1B. The 16 panels to the top left of
Figure 2Ashow the data arranged according to all 16 timbre ⫻
pitch combinations. The first four columns show the PSTH for
the responses to timbres corresponding to /i/, /u/, /␧/, and /a/,
whereas the top four rows show the data for pitches at 200, 336,
565, and 951 Hz, respectively. The rightmost column in Figure
2Ashows the mean response for each pitch, averaged across all
timbres and azimuths, whereas the bottom row shows the mean
response for each timbre, and the bottom right PSTH illustrates
the grand average response across all stimuli.
Displaying the data in this manner makes it easier to appreci-
ate the effect of varying either stimulus pitch or timbre. To de-
scribe these effects, we shall adopt the terminology used in
ANOVA-type linear statistical models, with mean spike rate dur-
ing some small time interval as our “response variable,” whereas
pitch, timbre, location, and poststimulus time serve as “explana-
tory variables.” We treat these as categorical variates, as we can-
not assume the relationship between stimulus parameter value
and spike rate to be linear or even monotonic. Within that con-
ceptual framework, comparing the top four panels of the right-
most column with the bottom right panel therefore reveals the
“main effect” of varying pitch on the discharge pattern on this
unit. To make it easier to visualize the main effects of each stim-
ulus parameter, we plot the individual PSTHs in the rightmost
column and bottom row on top of a color scale that shows how
each particular PSTH differs from the grand average at each time
bin. Red means the neural firing rate is, at that time point, larger
than average, blue indicates that it is below average, and the sat-
uration of the color encodes the size of the difference.
The grand average PSTH in the bottom right panel in Figure
2Ashows that the unit responded to artificial vowel sounds with
an initial increase in firing rate, which peaked at a rate of ⬃50 Hz
at ⬃50 ms post stimulus onset, followed by a smaller second peak
at ⬃180 ms. The main effect of presenting a relatively low pitch
(200 Hz, top panel of the last column) was to decrease the size of
the first peak in the PSTH and to increase that of the second.
Conversely, the main effect of high pitches (951 Hz, fourth panel
in the last column) was to increase the size of the first response
peak and to decrease the second. Similarly, the main effect of
varying timbre can be appreciated by comparing the panels in the
bottom row of the PSTH matrix in Figure 2A. A timbre corre-
sponding to the vowel /u/ strongly enhanced the initial response
peak (bottom row, second panel), whereas the timbre for /i/ sup-
pressed it (bottom row, first panel), but timbre changes did not
affect the later part of the response. This unit was therefore sen-
sitive to both pitch and timbre, and the effects of changing pitch
or timbre were manifest at different latencies after stimulus onset.
In the conceptual framework of an ANOVA-style analysis, the
simplest assumption for responses to a particular pitch/timbre
combination would be that the main effects of pitch and timbre
might be additive. To look for nonlinear interactions between the
stimulus dimensions, we compare the PSTHs in the main body of
the matrix against the values that would be predicted from the
linear sum of the main effects, which are shown by the color scales
in the rightmost column and bottom row of Figure 2A–D. The
Bizley et al. •Pitch, Timbre, and Location Coding in Cortex J. Neurosci., February 18, 2009 •29(7):2064 –2075 • 2067

color scales in the main body (first four rows and columns) of the
PSTH matrix show these “two-way interactions.” Therefore any
deviation from white shows that the response observed was non-
linear, with red colors indicating a supra-additive response, and
blue colors indicating a subadditive one. For example, in the unit
shown in Figure 2A, the combination of /i/ and a 200 Hz F0
elicited a supralinear response, whereas the combination of /i/
and 951 Hz F0 resulted in a response that was smaller than the
linear prediction. However, examination of the absolute values
for the interactions and main effects shows that the size of the
two-stimulus interactions were small relative to the “main ef-
fects” of any one stimulus parameter; the interaction coefficients
did not exceed ⫾33 spikes/s compared with ⫾86 spikes/s for the
largest main effect.
Figure 2Cshows the azimuth-by-pitch main effects and inter-
actions for the same unit, whereas Figure 2, Band D, shows the
pitch-by-timbre and pitch-by-azimuth main effects and interac-
tions, respectively, for a second sample unit (the same as that
illustrated in Fig. 1C). Although this second sample unit exhib-
ited rather different temporal discharge patterns, like the first, it
was clearly influenced by more than one stimulus dimension. As
we shall see further below, the data shown in Figure 2 were fairly
typical of many of the units recorded throughout all cortical areas
characterized. Thus, most units were sensitive to more than one
stimulus dimension, their firing patterns could change at various
times post-stimulus onset, and nonadditive interactions between
stimuli were not uncommon.
To quantify the strength and significance of these main effects
and interactions, we performed a 4-way ANOVA on the spike
counts, averaged across the 30 – 40 repeat presentations for each
Figure 2. Main effects and interactions of pitch, timbre, and azimuth. A–D, PSTH matrices illustrating the main effects and 2-way interactions of timbre, pitch, and azimuth on the responses of
two cortical neurons. Data are sorted according to two of the three stimulus dimensions, as indicated on the left and top margins of each panel, and averaged across the third. Each PSTH shows the
mean firing rate post stimulus onset in Hz. The effects of stimulus pitch and timbre (vowel identity) are plotted in Aand Band the effects of stimulus azimuth and pitch are shown in Cand D.A,C,
Data from the neuron whose responses are shown in Figure 1 B.B,D, Data from the neuron shown in Figure 1C. The first four rows and columns of each PSTH matrix represent the responses to
combinations of the two stimulus parameters indicated. The cells at the end of each row and column show the average response to the stimulus parameter indicated by the corresponding row and
column headers. For example, the cell at the end of the first row in Aand Bshows the mean response to a pitch of 200 Hz, regardless of timbre or location. The bottom right-hand PSTH in each matrix
showsthe overall grand average PSTH, constructedacross all 64stimulus conditions. Thecolor scale underlayin each PSTHhighlights the differencebetween it andthe grand averagePSTH, with blue
indicating a decrease in firing relative to the average and red showing an increase. The color scales for the first four rows and columns in each of the four panels saturate at ⫾33 spikes/s in A,⫾32
in B,⫾11 in C, and ⫾8inD. The color scales in the panels in the last row and bottom column saturate at ⫾60 spikes/s in Aand C, and ⫾32 in Band D.
2068 •J. Neurosci., February 18, 2009 •29(7):2064 –2075 Bizley et al. •Pitch, Timbre, and Location Coding in Cortex

of the 16 stimuli, in each 20 ms bin for the first 300 ms after
stimulus onset. In this manner, each response was represented as
a vector of 15 sequential spike counts. Our choice of 20 ms bin
widths was based on previous studies of ferret auditory cortex,
which indicate that this is likely to be a suitable temporal resolu-
tion for decoding neural responses (Schnupp et al., 2006; Walker
et al., 2008). In this ANOVA, the 3 stimulus parameters (azimuth,
pitch, and timbre) plus the time bin served as factors. To quantify
the relative strength with which one of the three stimulus dimen-
sions influenced the firing of a particular unit, we calculated the
proportion of variance explained by each of azimuth, pitch, and
timbre, Var
stim
, as:
Varstim ⫽
SSstim 䡠bin ⫺SSerror 䡠dfstim 䡠bin
SStotal ⫺SSbin
, (1)
where “stim” refers to the stimulus parameter of interest (pitch,
timbre, or azimuth), SS
stim 䡠bin
is the sum of squares for the
interaction of the stimulus parameter and time bin, SS
error
is the
sum of squares of the error term, df
stim 䡠bin
refers to the degrees of
freedom for the stimulus ⫻time bin interaction, SS
total
is the total
sum of squares, and SS
bin
is the sum of squares for the time bin
factor. A significant SS
bin
reflects the fact that the response rate
was not flat over the duration of the 300 ms response window.
This is in itself unsurprising, but by examining the stimulus-by-
time-bin interactions, we were able to test the statistical signifi-
cance of the influence a given stimulus parameter had, not just on
the overall spike rate, but also on the temporal discharge pattern
of the response. Stimulus-by-time-bin interactions were com-
mon, and revealed how a particular stimulus parameter influ-
enced the shape of the PSTH. Subtracting the SS
error
䡠df
stim 䡠bin
from the SS
stim 䡠bin
term allows us to calculate the proportion of
response variance attributable to each of the stimuli, taking into
account the additional variance explained simply by adding extra
parameters to the model. For the responses shown in Figure 2, A
and C, the percentage of variance explained by the stimulus main
effects was 5% for azimuth, 17% for pitch and 56% for timbre,
whereas for the unit shown in Figure 2, Band D, the main effects
of azimuth, pitch, and timbre accounted for 3%, 19%, and 7%,
respectively, of the variance in the neural discharge patterns.
Thus, although both units were significantly influenced by all 3
stimulus dimensions, one might justifiably describe the first as
being “predominantly” sensitive to timbre and the second to
pitch. Only 23% of units were significantly ( p⬍0.001) modu-
lated by azimuth, pitch, or timbre alone. In contrast, 36% of
neural responses were dependent on two of the three stimulus
dimensions and 29% of units were influenced by all three. The
responses of the remaining 12% of units were not significantly
modulated by any of the stimuli.
As mentioned above, we combined data from single units and
from small multiunit clusters for most analyses. To verify that any
joint sensitivity was not a result of recording from more than one
neuron, we determined the proportion of units sensitive to com-
binations of two parameters for single units alone. Sensitivity to
both pitch and timbre was observed in 56% of all recording sites
and in 52% (169 of 324) of the single units. Thirty percent of all
recordings and 33% of all single units were sensitive to both pitch
and azimuth, whereas 34% of all recordings and 36% of single
units were sensitive to timbre and azimuth. Thus, we were equally
likely to observe combination sensitivity in both multiunits and
well separated single units.
Spike count measures do not fully capture
response complexity
To examine the importance of the temporal discharge pattern in
the neural response, we also performed an ANOVA that was
restricted to the overall spike counts calculated over the first 75
ms after stimulus onset. This ANOVA was performed again using
pitch, azimuth, and timbre as independent variables, but this
time excluded poststimulus time. It resulted in far fewer units
exhibiting significant response modulation as a function of these
stimulus attributes. For example, using a single spike count mea-
sure, only 11% of all units exhibited significant ( p⬍0.05) sen-
sitivity to sound azimuth, although 36% of units had shown a
significant ( p⬍0.001) time-bin ⫻azimuth interaction. The
same comparison for pitch and timbre yielded values of 18%
compared with 66% for pitch, and 29% compared with 73% for
timbre. Over this single time window of 75 ms, the discharge rates
of 60% of units no longer exhibited any significant stimulus main
effects, and only 13% of units were modulated by more than one
parameter. Analyzed in this manner, the unit shown in Figure
2A, for example, was found to be sensitive only to timbre,
whereas that depicted in Figure 2Bwas no longer sensitive to any
of the three stimulus dimensions.
When we performed the analysis using a longer response win-
dow (300 ms, data not shown) there were even fewer neurons
whose responses were significantly modulated by these stimuli,
and neither unit shown in Figure 1 was found to be sensitive to
pitch, azimuth, or timbre. Figure 2Ashows that, for one of these
units, the early and the late part of the response varied in opposite
ways with stimulus pitch. The resulting “cancellation” of stimu-
lus effects is the likely to be the main reason why using an inap-
propriately wide analysis window failed to give a significant result
despite clear stimulus dependence. These results clearly demon-
strate that highly significant but transient stimulus effects can
easily be missed in an analysis that is temporally too coarse-
grained. We therefore adopted the response variance explained
statistics described in Equation 1 as our preferred measure of
neural stimulus sensitivity for the further analyses described
below.
Cortical distribution of sensitivity to pitch, timbre,
and azimuth
To examine the distribution of stimulus sensitivity across audi-
tory cortex, recordings were made in 5 of the 7 previously iden-
tified acoustically responsive areas in the ferret ectosylvian gyrus:
the primary and anterior auditory fields (A1 and AAF), the tono-
topically organized posterior pseudosylvian and posterior supra-
sylvian fields (PPF and PSF), which are located on the posterior
bank of the ectosylvian gyrus, and the nontonotopic anterior
dorsal field (ADF) on the anterior bank (Fig. 3A, Table 1) (Ko-
walski et al., 1995; Nelken et al., 2004; Bizley et al., 2005). These
five areas make up the auditory “core” (A1 and AAF), and “belt”
(PPF, PSF, ADF) areas in this species. We have previously re-
ported that ⬃60% of neurons in the anterior ventral field (AVF)
are visually sensitive (Bizley et al., 2007). AVF and the ventropos-
terior (VP) area are likely to be “parabelt” areas and were not
included in the present study. In four of five animals, recordings
were made in all five cortical fields and, in the remaining animal,
responses were recorded in three fields.
The locations of each field were determined for individual
animals as described in the Materials and Methods. Because we
used multisite electrode arrays and commonly recorded several
units at different depths, the range of CFs obtained in these cases
was visualized by plotting one Voronoi tile for each unit re-
Bizley et al. •Pitch, Timbre, and Location Coding in Cortex J. Neurosci., February 18, 2009 •29(7):2064 –2075 • 2069

corded, arranged in a circular manner
around the penetration site, with the most
deeply recorded unit shown rightmost and
proceeding clockwise from deep to super-
ficial. The composite frequency map ob-
tained in this manner is shown in Figure
3B. The low frequency regions that mark
the boundaries of fields AAF and ADF and
of A1, PPF, and PSF are readily apparent.
To investigate the anatomical distribu-
tion of sensitivity to pitch, timbre, and lo-
cation, we generated composite feature
sensitivity maps using methods described
further below. Figure 3C–E maps the pro-
portion of variance explained (Eq. 1) by
the main effects of azimuth, pitch, and
timbre, respectively, for every penetration
made, onto the surface of the auditory cor-
tex. Each “tile” in the Voronoi tesselation
shows the average value obtained for all
units recorded at that site. The color scale
indicates the proportion of variance ex-
plained with darker, red colors indicating
low, and brighter, yellow colors indicating
high values. These plots suggest that there
are areas in which clusters of units have a
higher sensitivity to each stimulus param-
eter. Highly azimuth sensitive units were
particularly common in A1, as well as in an
area encircling the tip of the pseudosylvian
sulcus. Highly pitch sensitive units were
most commonly found in the middle of
auditory cortex, around the point at which
the low frequency edges of the tonotopic
core and belt areas converge, as shown in
Figure 3B. Timbre sensitivity was highest
in the primary fields and along the low fre-
quency ridge that separates the two poste-
rior fields, PPF and PSF. To illustrate the
range of percentage variance explained in
any one electrode penetration, Figure
3F–H plots the values for every unit re-
corded, with each tile representing a single
recorded unit, and multiple units from a
single penetration arranged in a circular
manner, as in Figure 3B, around the site of
the penetration. Overall, Figure 3 suggests
that there are “clusters” of recording sites
that are relatively more sensitive to stimu-
lus azimuth, pitch, or timbre, but these are
not obviously restricted to a particular
subset of the five tonotopic cortical fields
investigated here, and, in each field, we observed considerable
unit-to-unit variability in the sensitivity to pitch, timbre, and
location.
The distribution of parameter sensitivity for each cortical area
was visualized in box-plot format (Fig. 3I–K). Despite the very
wide unit to unit variation, some degree of specialization is nev-
ertheless apparent, as, for all three stimulus dimensions, there
were significant differences in the proportion of variance ex-
plained by cortical field (Kruskal–Wallis test,
␹
2
⫽27, 68, and 77
respectively, p⬍0.0001). Tukey–Kramer post hoc comparisons
(p⬍0.05) revealed that azimuth sensitivity was, on average,
significantly higher in A1 and PPF than in AAF, PSF, and ADF,
pitch sensitivity tended to be more pronounced in A1 and the
posterior fields PSF and PPF than in AAF and ADF, whereas AAF
showed the highest average level of timbre sensitivity. These
Figure3. Distributionofrelativesensitivitytolocation,pitch,andtimbreacrosstheauditorycortex.A,Locationofferretauditorycortex
on the middle, anterior, and posterior ectosylvian gyri (MEG, AEG, and PEG, respectively). The inset shows the location of seven auditory
cortical fields. The color scale shows the tonotopic organization as visualized using optical imaging of intrinsic signals (from Nelken et al.,
2004). B, Voronoi tessellation map showing the characteristic frequencies (CFs) of all unit recordings made (n⫽811). These data were
collected from a total of five animals and have been compiled onto one auditory cortex map. Each tile of the tessellation shows the CF
obtained from each recording site, using the same color scale as in A.C–E, Voronoi tessellation maps plotting the proportion of variance
explainedbyeachofthestimulusdimensions:azimuth(C),pitch (D), and timbre (E). Each tile represents the average value obtained at that
penetration. All units included in the variance decomposition are shown (n⫽619). F–H,asC–E, but here each individual unit is plotted,
with tiles representing units from a single penetration arranged counterclockwise by depth around the penetration site. I–K, Box-plots
showing the proportion of variance explained by azimuth (I), pitch (J), and timbre (K) for each of the five cortical areas examined. The
boxes show the upper and lower quartile values, and the horizontal lines at their “waist” indicate the median. In all cases, there was a
significanteffectofcorticalfieldonthedistributionof variance values (Kruskal–Wallis test, p⬍0.001), and significant pairwise differences
are indicated by the horizontal lines above the plots (Tukey–Kramer post hoc test, p⬍0.05).
Table 1. Total number of recordings (probe placements and units) in each cortical
field for the 5 ferrets used in this study
A1 AAF PPF PSF ADF
Probe placements (n)10 7 117 5
Units (n) 189 101 152 96 77
2070 •J. Neurosci., February 18, 2009 •29(7):2064 –2075 Bizley et al. •Pitch, Timbre, and Location Coding in Cortex

trends were seen consistently in each of the animals, and not just
in the pooled data, and a jack-knife test was used to establish that
no one animal contributed disproportionately to the small but
significant differences reported here. The same statistical tests
(i.e., Kruskal–Wallis and Tukey–Kramer post hoc tests) were im-
plemented 5 times, excluding each of the five animals in turn. In
all cases, the same significant trends across cortical areas were
preserved.
Linear and nonlinear interactions in the cortical sensitivity to
pitch, timbre, and azimuth
Our analysis method quantified the linear contribution of each of
the three stimulus dimensions to the units’ responses, as well as
the nonlinear effects of presenting particular stimulus combina-
tions. This nonlinear effect is quantified in our ANOVA by the
three-way interaction of the time bin factor with two different
stimulus parameters, and can be thought of as a measure of com-
bination sensitivity. The interaction coefficients measure nonad-
ditive (“multiplicative”) interactions between categorical vari-
ates, and are analogous to a logical AND operation (by how much
does the response differ from the purely linear, additive expecta-
tion for specific parameter combinations e.g., when timbre ⫽/i/
AND azimuth ⫽45°?). Over two-thirds of units tested were sen-
sitive to more than one stimulus dimension and 41% of all units
showed significant ( p⬍0.001) nonlinear interactions for at least
one combination of pitch ⫻timbre, azimuth ⫻pitch, or azi-
muth ⫻timbre. We also investigated whether it would be possi-
ble to describe the nature of these nonlinear interactions either as
“predominantly expansive” or facilitatory, or as “predominantly
compressive” or saturating. Expansive nonlinearity would make
the response of a neuron more selective for a particular stimulus
parameter combination, whereas saturat-
ing nonlinearities would have the opposite
effect. Expansive nonlinearities are supra-
additive, and would result in positive in-
teraction coefficients which grow system-
atically as main effect coefficients grow
larger. Compressive, subadditive nonlin-
earities, would, in contrast, result in large,
positive main effect coefficients being as-
sociated with negative interaction coeffi-
cients. We therefore compared the sums of
main effect coefficients (the “predicted ad-
ditive response”) with their corresponding
interaction coefficients, but we found no
significant systematic trends or relation-
ships. The interaction effects thus appear
to be too variable to allow them to be char-
acterized generally as overall predomi-
nantly expansive or compressive.
The distribution across the cortical sur-
face of sensitivity to combinations of stim-
ulus parameters is shown in Figure 4A–C.
Nonlinear interactions were most com-
monly observed in A1 and AAF, where
54% and 56% of units exhibited significant
interactions between two or more stimuli,
respectively. The most common interac-
tions were between pitch and timbre, al-
though azimuth ⫻timbre sensitivity and
azimuth ⫻pitch sensitivity were also ob-
served (Fig. 4D). The proportion of units
exhibiting significant interactions fell to
36%, 30%, and 15% in fields PPF, PSF, and ADF, respectively.
Although the number of units showing interactions varied be-
tween cortical fields, the overall magnitude of the interaction
term did not vary between cortical areas for either the azimuth ⫻
pitch or azimuth ⫻timbre conditions. This is shown in Figure 4 E
by plotting the proportion of variance explained by the sum of the
spatial and nonspatial interaction terms (azimuth ⫻pitch and
azimuth ⫻timbre) for each of the five cortical areas (Kruskal–
Wallis test,
␹
2
⫽5.5, p⫽0.24). In contrast, the proportion of re-
sponse variance explained by the pitch ⫻timbre interaction term
did show a significant variation across cortical fields (Fig. 4F,
Kruskal–Wallis test,
␹
2
⫽48.2, p⬍0.001), with combination sensi-
tivity for nonspatial parameters accounting for more of the variance
in the primary areas A1 and AAF than in the posterior fields PSF and
PPF, which in turn, had higher values than ADF. This distribution is
very similar to that observed for the timbre main effects.
Both the number and magnitude of the pitch ⫻timbre inter-
actions were greater than for either the timbre ⫻azimuth or
pitch ⫻azimuth interaction terms, supporting a separation of
spatial and nonspatial attributes throughout auditory cortex.
However, this separation is far from complete: of the 254 units in
which there was a significant interaction, 225 were sensitive to
pitch–timbre combinations and 60 of these (i.e., 26%) were also
sensitive to pitch–azimuth or timbre–azimuth combinations. In
summary, whereas interactions in the “what” domain were more
common, a substantial minority of units were sensitive to com-
binations of both spatial and nonspatial stimulus features.
Stimulus sensitivity differs with cortical depth
Our multisite recording electrodes allowed us to make simulta-
neous recordings at 8 –16 different depths throughout the cortex.
Figure 4. Nonlinear sensitivity to stimulus combinations. A–C, Maps showing the distributions of neural sensitivity attribut-
able to (proportion of response variance explained by) timbre ⫻azimuth (A), pitch ⫻azimuth (B), or timbre ⫻pitch (C)
nonlinear two-way interactions. D, Histogram showing the number of units in each field in which there were significant two-
stimulus interactions for each of these stimulus parameter combinations. The total number of units recorded in each cortical field
are listed above. E,F, Box-plots summarizing the statistical distributions of the summed azimuth ⫻timbre and azimuth ⫻pitch
interactions (E), and the pitch ⫻timbre interactions (F). There was no significant difference in the distribution between fields for
the interactions between spatial and nonspatial parameters shown in E(p⫽0.24). In contrast, the magnitude of the pitch ⫻
timbre interactions did vary with cortical field ( p⬍0.001). Horizontal lines above the box-plots show which distributions had
pairwise significantly different means (Tukey–Kramer post hoc test, p⬍0.05).
Bizley et al. •Pitch, Timbre, and Location Coding in Cortex J. Neurosci., February 18, 2009 •29(7):2064 –2075 • 2071

We grouped recordings coarsely into “su-
perficial” or “deep” simply based on
whether or not the recording site was
within or ⬎800
␮
m below the cortical sur-
face. These divisions coarsely divide units
into supragranular and infragranular cor-
tical layers. The distribution of proportion
of variance explained values are plotted in
Figure 5. Azimuth sensitivity was found to
be greatest in the deeper cortical layers (2-
sampled ttest, p⫽0.01), whereas pitch
and timbre sensitivity were greater in the
superficial layers ( p⫽0.004 and p⬍
0.001, respectively). Pitch–timbre interac-
tions were also more common in the su-
perficial layers ( p⫽0.002) whereas pitch–
azimuth ( p⫽0.33) and timbre–azimuth
(p⫽0.35) interactions were found to be
equally distributed in depth. Data were
pooled across all cortical areas for these
analyses, but similar trends were observed in each of the five
cortical fields individually.
Azimuth, pitch, and timbre sensitivities do not depend on
unit characteristic frequency
In Figure 3Ewe showed that clusters of units with high timbre
sensitivity were commonly found in the low CF border region
between A1, PPF, and PSF. This raises the question of whether
sensitivity to timbre, or indeed to other stimulus parameters,
varies systematically with unit CF. However, scatter plots of unit
CF against the proportion of variance explained (supplemental
Fig. 2A–C, available at www.jneurosci.org as supplemental ma-
terial) showed no systematic relationship between unit CF and
azimuth, pitch, or timbre sensitivity. Furthermore, pitch, timbre,
or azimuth sensitivity was just as common among units that were
unresponsive or untuned to pure tones as in units with clearly
defined CFs (supplemental Fig. 2D–F, available at www.
jneurosci.org as supplemental material), and there were no CFs at
which it was particularly easy or difficult to obtain vowel re-
sponses (supplemental Fig. 1G, available at www.jneurosci.org as
supplemental material). We also found no significant correla-
tions between CF and best pitch (defined as the pitch that elicited
the most spikes per presentation) for pitch-sensitive units with a
CF ⬍1 kHz.
Discussion
It has been proposed that a division of labor exists across auditory
cortical areas whereby the ability of humans and animals to rec-
ognize or localize auditory objects can be attributed to anatomi-
cally separate processing streams. This concept is inspired by
earlier studies that postulated distinct hierarchies for processing
different visual features, such as color or motion, in extrastriate
visual cortex (Ungerleider and Haxby, 1994). Parallel processing
in the auditory system is supported by behavioral-deactivation
(Lomber and Malhotra, 2008), functional imaging (Alain et al.,
2001; Maeder et al., 2001; Warren and Griffiths, 2003; Barrett and
Hall, 2006), and electrophysiological studies (Recanzone, 2000;
Tian et al., 2001; Cohen et al., 2004), as well as by anatomical
evidence showing differences in the connectivity of these regions
(Hackett et al., 1999; Romanski et al., 2000; Bizley et al., 2007).
Most of the physiological studies compared the sensitivity of dif-
ferent auditory cortical areas to only one stimulus parameter,
such as spatial location or pitch. Here we adopted a different
approach, based around a stimulus set in which several stimulus
dimensions were systematically varied, to explore the relative
sensitivity of neurons in different cortical fields to azimuth, pitch,
and timbre.
Choice of stimulus parameter values
Because we examined neural responses to three stimulus at-
tributes, the range of values selected in each case was necessarily
limited, but nonetheless covered behaviorally pertinent and
broadly comparable ranges. Ferrets can accurately localize low-
frequency narrowband noise bursts (Kacelnik et al., 2006) and
lateralize the same synthetic vowels that were used in the present
experiment. Although the range of azimuths used covered only
the frontal quadrant of auditory space, behavioral and electro-
physiological studies have shown particularly high sensitivities
within this region to changes in sound-source direction (Mrsic-
Flogel et al., 2005; Nodal et al., 2008). The separation of our
stimuli in both azimuth (Parsons et al., 1999) and pitch (Walker
et al., 2009) was large compared with psychoacoustic difference
limens. There have been few studies of timbre discrimination in
animals, but chinchillas can discriminate the vowels /i/ and /a/
across a range of speakers and pitches (Burdick and Miller, 1975),
and our own data demonstrate that ferrets can accurately dis-
criminate the vowel timbres used in this study (Bizley, Walker,
King, and Schnupp, unpublished observations).
Distribution of azimuth, pitch, and timbre sensitivity
The bulk of behavioral and neurophysiological studies of audi-
tory cortex have been performed in cats. A1 is well conserved
across species, and AAF appears to be equivalent in cat and ferret
(Kowalski et al., 1995; Imaizumi et al., 2004; Bizley et al., 2005).
The posterior fields, PPF and PSF, in the ferret share certain
similarities with the posterior auditory field (PAF) in the cat, both
being tonotopically organized and containing neurons with dif-
ferent temporal response properties from those in the primary
fields (Phillips and Orman, 1984; Stecker et al., 2003; Bizley et al.,
2005). Like the cat’s secondary auditory field (Schreiner and
Cynader, 1984), ferret ADF lacks tonotopic organization and
contains neurons with broad frequency response areas (Bizley et
al., 2005). Although strict homologies have yet to be established,
auditory cortex appears to be organized in a similar manner in
these species.
We found that, as in cats (Harrington et al., 2008), sensitivity
Figure 5. Parameter sensitivity in superficial and deep cortical layers. A–C, Distribution of parameter sensitivity (response
variance attributable) to azimuth (A), pitch (B), and timbre (C) for responses recorded at superficial (⬍800
␮
m) or deep (⬎800
␮
m) cortical locations. Higher sensitivities to pitch and timbre were relatively more common in the superficial layers.
2072 •J. Neurosci., February 18, 2009 •29(7):2064 –2075 Bizley et al. •Pitch, Timbre, and Location Coding in Cortex

to changes in sound azimuth varies across auditory cortex, but is
nonetheless a property of all areas examined. This contrasts with
recent behavioral data in cats (Lomber et al., 2007), which suggest
that certain cortical fields, such as A1 and PAF, are required for
normal sound localization, whereas others, such as AAF, are not.
However, complex behaviors, like remembering to approach a
sound source for a reward, are bound to require cognitive control
from high-order cortical areas, most likely in frontal cortex. The
profound differences observed in response to cooling different
cortical fields may therefore have less to do with the physiological
properties of neurons in those areas than with their projections to
higher-order brain regions (Hackett et al., 1998; Romanski et al.,
1999).
Our findings are also in broad agreement with many previous
studies of cortical pitch sensitivity. Our “pitch sensitivity” test
was less stringent than that used by Bendor and Wang (2005),
who reported a pitch-selective area in marmoset auditory cortex.
However, in agreement with their observations, we found pitch-
sensitive neurons to be more common in the superficial cortical
layers and to occur frequently (although not exclusively) near the
low-frequency border of A1. Nevertheless, overall, we did not
find a correlation between unit CF and pitch sensitivity or a ten-
dency for low CF neurons to be more sensitive to pitch. The lack
of a single pitch center in ferret auditory cortex is supported by
the results of imaging studies in ferrets (Nelken et al., 2008) and
humans (Hall and Plack, 2008).
The neural basis for timbre processing has been much less
widely studied. Previous studies have demonstrated that the re-
sponse properties of neurons in A1 are well suited to detect spec-
tral envelope cues (Calhoun and Schreiner, 1998; Versnel and
Shamma, 1998). Moreover, the spectral integration properties of
A1 neurons have been reported to be topographically organized
in A1 in a manner that might support vowel discrimination based
on the frequency relationship of the first and second formants
(Ohl and Scheich, 1997). Consistent with these studies, we found
that sensitivity to vowel timbre was greatest in the primary audi-
tory cortical areas of the ferret. Nevertheless, as with azimuth and
pitch sensitivity, the responses of neurons recorded in all five
cortical fields were modulated by timbre. Lesions of the dorsal/
rostral auditory association cortex, but not A1, in rats impaired
performance on a multiformant vowel discrimination task (Ku-
doh et al., 2006). Using fMRI, timbre sensitivity has been dem-
onstrated in both posterior Heschl’s gyrus and the superior tem-
poral sulcus in humans (Menon et al., 2002; Kumar et al., 2007).
Previous studies have reported systematic differences in neu-
ral tuning properties along isofrequency laminae in A1. Changes
in the representation of properties such as tone threshold and
tuning bandwidth (Schreiner and Mendelson, 1990; Cheung et
al., 2001; Read et al., 2001) and binaural response characteristics
(Middlebrooks et al., 1980; Rutkowski et al., 2000) have been
observed. However, the sampling density of recording sites in
individual animals was insufficiently fine to investigate whether
this was the case for any of the parameters investigated in the
present study.
Interdependent coding of pitch, timbre, and azimuth
Our analysis revealed that the neural encoding of pitch, location,
and timbre cues is interwoven and distributed across auditory
cortex. These methods were sensitive to changes in neural firing
that occurred over time and were able to capture the effects ap-
parent in the raw data in a way that a simple spike-count measure
failed to. Previous studies have also shown that the timing of
spikes in auditory cortex carries information useful for discrim-
inating natural sounds (Schnupp et al., 2006; Goure´ vitch and
Eggermont, 2007), as well as about a sound’s pitch (Steinschnei-
der et al., 1998) and location (Furukawa and Middlebrooks, 2002;
Nelken et al., 2005).
Our stimuli spanned three independent perceptual dimen-
sions, but very few neurons in any of the cortical fields examined
were sensitive to changes in azimuth, pitch, or timbre only. Nev-
ertheless, small but significant differences in average neuronal
sensitivity were observed across cortical areas and depths. These
subtle regional differences could provide the basis for the subse-
quent anatomical segregation of spatial and nonspatial informa-
tion in higher-order cortical areas. Although it is possible that
cortical areas other than those sampled here exhibit greater func-
tional specialization, or that a clearer distinction might be appar-
ent with other stimulus features, such as those that are temporally
modulated, it is important to remember that spatial and nonspa-
tial aspects of sounds often have to be considered together. For
instance, to operate effectively in the presence of multiple sound
sources, it is necessary to be able to track specific pitch, timbre,
and sound-source location combinations over time. Spatial sen-
sitivity has been reported within auditory cortical and prefrontal
areas thought to be concerned with sound identification (Cohen
et al., 2004; Gifford and Cohen, 2005; Lewald et al., 2008). More-
over, a recent study (Recanzone, 2008) documenting sensitivity
to monkey calls found that neurons throughout auditory cortex
were equally selective in their responses. Interactions between
spatial and nonspatial processing streams are known to occur in
the visual cortex (Tolias et al., 2005). Such effects are likely to be
particularly important in audition, where multiple sounds can be
perceived simultaneously at several locations.
We found that cortical neurons often responded nonlinearly
to feature combinations. This was particularly the case for pitch
and timbre in the primary fields, A1 and AAF. This apparent
combination sensitivity could simply reflect intermixing of rela-
tively low level sensitivity to multiple sound features. However, it
has been argued that A1 might represent auditory objects by
grouping together physical stimulus attributes from a common
source, with higher-order cortical areas extracting perceptual fea-
tures, such as the object’s location, from this object-based repre-
sentation (Nelken and Bar-Yosef, 2008). Grouping stimulus at-
tributes is essential for tracking a sound source through a
potentially cluttered acoustic environment (Bregman, 1990), and
the nonlinear sensitivity that we observed in A1 may be ideally
suited to achieving this. We observed a decrease in nonlinear
feature interactions away from A1, suggesting an increasingly
independent representation of these perceptual dimensions in
higher auditory cortex. Although still sensitive to different sound
features, this sensitivity was well described by linear interactions,
which help to preserve information for subsequent processing.
Ultimately, the question of interest is how this distributed
network of neurons contributes to perception. In humans, selec-
tive attention has been shown to modulate putative localization
and identification pathways independently (Ahveninen et al.,
2006). The true extent to which a division of labor exists within
auditory cortex may therefore become apparent only when ani-
mals use the activity in these areas to listen to different attributes
of sound.
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