Processing of complex stimuli and natural scenes in the
Neuronal responses in auditory cortex show a fascinating
mixture of characteristics that span the range from almost
perfect copies of physical aspects of the stimuli to extremely
complex context-dependent responses. Fast, highly stimulus-
specific adaptation and slower plastic mechanisms work
together to constantly adjust neuronal response properties to
the statistics of the auditory scene. Evidence with converging
implications suggests that the neuronal activity in primary
auditory cortex represents sounds in terms of auditory objects
rather than in terms of invariant acoustic features.
Department of Neurobiology, The Silberman Institute of Life Sciences,
and the Interdisciplinary Center for Neural Computations, Givat Ram
Current Opinion in Neurobiology 2004, 14:474–480
This review comes from a themed issue on
Edited by Catherine Dulac and Benedikt Grothe
Available online 17th July 2004
0959-4388/$ – see front matter
? 2004 Elsevier Ltd. All rights reserved.
primary auditory cortex
spectro-temporal receptive field
Research into signal coding in primary auditory cortex
(A1) has enjoyed renewed popularity in recent years. All
modern methodologies, including new slice preparations
for studying thalamo–cortical interactions , intracellu-
lar and extracellular single neuron recordings [2,3??,4??],
evoked electrical and magnetic fields (EEG and MEG)
animals, including rodents, bats, cats, primates, and
Despite this accumulation of information, the nature of
the representation of complex sounds in A1 remains the
subject of heated debate. This is not due to a lack of data,
but rather because of the fact that the data are often
contradictory. Whereas some studies emphasize a rela-
tively simple cortical representation, other studies show a
large degree of complexity in the neuronal responses.
Here, I review evidence that indicates that simplicity and
complexity co-exist in A1. Evidence with converging
implications suggests that the co-existence of simplicity
and complexity in A1 is due to its participation in pro-
cesses that are often implicitly assigned to higher brain
areas. In particular, I review evidence that suggests the
involvement of auditory cortex in processes such as the
on-line extraction of statistical regularities from the audi-
tory scene and the organization of the auditory scene in
terms of auditory objects.
Paradoxical response properties in
Precise and imprecise temporal coding
One of the complexities in auditory cortex is the interplay
among multiple time scales that determine the neural
responses. For example, cortical neurons respond to some
auditory events with stereotypical response bursts at a
fixed latency (‘locking’). The variance of the latency of
such bursts might be similar to that of peripheral neurons.
However, the same neurons may show sluggish responses
to other features of the sounds.
Temporal coding is usually tested using repetitive sti-
muli, such as amplitude-modulated best-frequency tones
or click trains. The ability of cortical neurons to phase-
lock to repetitive stimuli is usually limited to sub-pitch
rates (20-30 Hz)  in both anesthetized and awake 
animals, although in many studies, a minority of the
cortical neurons can follow fast repetitive stimuli up to
much higher rates. Lu et al.  demonstrated the pre-
sence of a separate population of neurons that was sensi-
tive to the rate of faster click trains without locking to the
The most recent evidence for the sluggishness of cortical
neurons is based on the spectro-temporal receptive field
(STRF) [12–15]. The STRF can be interpreted as the
time-frequency distributionof the most efficient stimulus
for the neuron, but also as the average response following
a short tone burst as a function of the burst frequency and
would uncover relatively complex response properties
. However, several recent analyses of large sets of
mammalian STRFs concluded that for the majority of
Current Opinion in Neurobiology 2004, 14:474–480 www.sciencedirect.com
neurons, STRF shapes are rather simple [13,14]. Further-
more, STRFs are often sluggish: the temporal component
of the STRF (the average of the STRF at all frequencies,
as a function of the delay) is often relatively slow, and the
modulation transfer function of cortical neurons, derived
as the Fourier transform of the temporal component of
the STRF, peaks at 16 Hz in the cat . Most STRFs
have the form of differentiators in time and frequency,
therefore representing a relatively simple operation, with
time constants in the order of 30 ms.
Such slow dynamics of firing would seem to imply rela-
tively slow dynamics for the membrane potential, and
therefore, large variance in spike timings [17?]. However,
First spike latencies of cortical neurons show as little
variability as they do in the auditory nerve .The same
precision of spike timing is seen in the responses to
frequency-modulated (FM) sounds covering a large fre-
quency band. In most A1 neurons, FM sounds produce a
short burst of spikes when the frequency trajectory
crosses the edge of the tuning curve of the neuron. This
burst is locked with millisecond precision to the time at
which the frequency trajectory reaches the trigger fre-
quency, independent of the velocity of the sweep
It could be argued that in the above examples, the precise
However, Elhilali et al. [21??] demonstrated such preci-
sion in the steady-state responses to dynamic ripple
stimuli. These are stimuli that are composed of a sum
of a large number of sine waves densely distributed over a
large frequency band that covers the whole response area
of the neuron. The amplitudes of these sine waves are
modulated byenvelopes that vary slowly in both time and
frequency. Cortical neurons sometimes locked to the fine
structure of the carrier even at rates that were substan-
tially higher (up to 200 Hz) than the 20–30 Hz above
which locking to click trains and other repetitive stimuli
was essentially absent in cortex . The precise locking
to the carrier structure_was present although the locking
to the slow envelope often disappeared at much slower
The conservation of highly precise timing in cortical
responses is apparent even in EEG and its magnetic
components of the MEG responses to short FM sounds.
These components are believed to represent the onset
responses in primary auditory cortex. The measured mid-
latency potentials were modeled as sums of unit
responses, presumably evoked by each individual fre-
quency as it occurs in the FM sound, whose temporal
sequence was determined by the cochlear activation due
to the FM sounds. The cochlear activation depends on
the direction of the FM sound and on the directionality of
the cochlear traveling wave, which always travels from
high to low frequencies . The MEG responses had a
directional sensitivity that was reproduced by this simple
model. Thus, some of the temporal structure of the
cochlear traveling wave is represented all the way to
the auditory cortex.
Fishbach et al. [17?,24] presented a simple model that
accounts quantitatively for tone responses, 2-tone
responses, and responses to FM sounds in the auditory
cortex. Their model is essentially a differentiator of the
temporal envelopeindifferentfrequency bands,followed
by integration over frequency with frequency-dependent
delays. To fit the data, the model required short time
precise and the sluggish responses of cortical neurons can
be described as differentiations, they operate on the
incoming sound at different time scales : the precise
responses are sensitive to fast changes (1–10 ms), whereas
the sluggish responses are sensitive to slow changes (10–
The use of intracellular recordings in vivo in auditory
cortexuncoveredsome ofthe mechanismsunderlyingthe
highly precise cortical responses. Wehr and Zador [3??]
balanced excitatory and inhibitory inputs to cortical neu-
rons. The inhibition turns out to be slightly delayed with
respect to the excitation, which opens a short window
during which neurons can respond, thus enhancing the
precision of their firing. Such results are consistent with
the Fishbach model [17?]. Elhilali et al. [21??] suggested
the possible involvement of synaptic depression for
achieving the seemingly contradictory goals of sluggish
yet precise firing.
Linear and non-linear responses in auditory cortex
Several studies have recently suggested a rather linear
representation of sounds by neurons in auditory cortex.
Most of these studies are based on characterization of
auditory neurons by STRFs. Under the assumption that
the system integrates signal energy linearly, STRFs can
be used to predict the responses of neurons to general
stimuli by convolving them with the spectrogram of the
stimulus [26,27?]. For example, using random chord
stimuli, first introduced by deCharms et al. , Schnupp
et al.  estimated STRFs in the ferret auditory cortex
and successfully used them to predict the responses to
virtual space stimuli. These findings demonstrated that
most of the spatial sensitivity of cortical neurons could be
attributed to their linear spectral sensitivity. Mrsic-Flogel
et al.  further strengthened these results by demon-
strating that at least some of the maturation of the spatial
responses in A1 during development of ferrets can be
attributed to the growth of their heads and ears, which
transforms juvenile into adult acoustics.
Processing of complex stimuli and natural scenes in the auditory cortex Nelken475
Current Opinion in Neurobiology 2004, 14:474–480
However, under many other conditions, linear models,
and in particular STRFs, cannot easily explain cortical
responses to complex sounds. Barbour and Wang [30?]
described a class of neurons that respond maximally for
low-contrast stimuli in the auditory cortex of awake
marmosets. As the responses of linear systems should
increase monotonically with the contrast, such non-
monotonic responses cannot be accounted for by linear
Bar-Yosef et al.  presented relatively simple natural
sounds, tonal bird songs over natural backgrounds, to
that almost any modification of these sounds, such as
extracting them from the temporal context or cleaning
them from their noise background, resulted in large
changes in the neural responses. Such effects are qualita-
tively inconsistent with linear, STRF-based descriptions
of cortical neurons, although Bar-Yosef et al.  did not
perform a quantitative comparison with predictions based
Going a step beyond the study of Bar-Yosef et al. ,
Machens et al. [27?] fitted STRFs to the membrane
potential trajectories of cortical neurons responding to
natural sounds. They found that for some sounds, such
models could satisfactorily account for the responses, but
generally their predictive power was low.
Feature detection or something else?
It seems that depending upon the circumstances, a cor-
tical neuron can choose to be sluggish or precise, linear or
non-linear. Thus, the feature sensitivity of a neuron, as
determined, for example, by its STRF, cannot be used as
an invariant essential characterization of its responses.
The multiple time scales at which cortical neurons pro-
cess sounds provide another argument against a pure role
in feature-detection for auditory cortex neurons .
Feature detectors are expected to be sensitive to the
time scale of the features they represent. However,
cortical neurons show sensitivity to sounds on multiple
Taking this argument to the extreme, it can be hypothe-
sized that cortical neurons are not feature detectors, but
something else. In fact, most features whose extraction
has been assigned to cortical neurons are already repre-
sented subcortically. Thus, using intracellular recordings
of the responses to FM sounds, Zhang et al. [4??] demon-
strated that the excitatory inputs to cortical neurons,
presumably arriving from the auditory thalamus, are
already selective to the direction of the frequency mod-
ulation. Similarly, mechanisms underlying pitch sensitiv-
ity can already be demonstrated at the level of the
cochlear nucleus , and the extraction of periodicity,
which could play a part in encoding pitch, has been
hypothesized to be complete as early in the pathway as
the inferior colliculus (IC), although definitive evidence
is still lacking [9,33,34]. The substantial slowing in the
phase-locking capability of auditory cortex neurons, that
has been argued to be a specialization for analyzing
invariant features of animal vocalizations , is already
found in the auditory thalamus . It is possible to add
other important features to this list, such as interaural
time and level differences (ITDs and ILDs), which are
important for binaural hearing. For example, Shackleton
et al.  demonstrated that just noticeable differences
for interaural time disparities, computed for single
neurons in IC, are already consistent with behavioral
So what can auditory cortex neurons do, beyond feature
detection? Clues to their putative role in auditory percep-
tion may arise from studies of adaptation and plasticity,
and from the use of complex, natural, and naturalistic
auditory scenes containing multiple sound objects.
Adaptation and plasticity
The plastic capabilities of auditory cortex have been
studied in several preparations on many time scales.
Significant changes in electrical and magnetic brain
potentials (EEG and MEG) occur during training for
the performance of tasks such as the perception of virtual
pitch  and fine pitch discrimination . Even simple
exposure to different auditory environments can substan-
tially change auditory cortical organization and responses:
thus, raising rats in an enriched environment increases
many measures of cortical responsiveness , whereas
raising them in constant noise disrupts the tonotopic
structure of auditory cortex . In humans, comparisons
of musicians and non-musicians show significant changes
in brain structure and in responses to sounds, although
such studies are weakened by the fact that correlation
does not imply causation. Musicians have greater volume
of gray matter in auditory areas, motor areas, and in areas
related to visuo-spatial processing relative to non-musi-
cians, and these increases were correlated with practice
intensity . Schneider et al. [6?] have reported some of
the most impressive results of this kind. They demon-
strated correlations among the amplitude of mid-latency
components of the MEG, the gray matter volume of the
presumed primary auditory cortex on Heschl’s gyrus, and
musical aptitude in groups of professional musicians,
amateur musicians, and non-musicians.
Plasticity can be evoked in controlled experimental con-
ditions by classical conditioning and can be mimicked in
anesthetized animals by nucleus basalis stimulation .
Good reviews of these results by Weinberger and by Suga
and Ma have recently appeared [41,42].
Fritz et al. [43??] have recently demonstrated some of the
most rapid plastic changes in auditory cortex observed
in the literature so far. They showed changes in the
Current Opinion in Neurobiology 2004, 14:474–480 www.sciencedirect.com
receptivefieldsof ferret auditorycortexwithinminutes of
the beginning of a tone detection task. Responsiveness of
neurons to the frequency of the target tone increased,
sometimes transiently and sometimes for many hours
after the end of the task. Such changes were smaller or
absent when the animal was listening passively to the
At even shorter time scales, tens of seconds and even
seconds, another type of plastic change, adaptation, is
ubiquitous in auditory cortex. Adaptation in brain poten-
tials, in which the N1 component and its magnetic analog,
N1m, tend to decrease with repetitive stimulation, has
been known for a long time. When such a repetitive
stimulus sequence is interrupted by a rare sound, a novel
(MMN), appears in the evoked responses . This
process has been linked to sensory memory and to
‘primitive intelligence’  because it can be evoked
when an abstract rule is violated by a rare sound .
MMN generators have been localized in or at the
vicinity of auditory cortex .
At the single neuron level, Malone et al.  demon-
strated strong adaptation that resulted in context-sensi-
tivity of the responses to time-varying interaural phase
differences in the auditory cortex of awake macaques.
Such results, already observed in the IC , were sub-
stantially more pronounced in auditory cortex. Ulanovsky
et al. [49??] demonstrated stimulus-specific adaptation in
an oddball paradigm, similar to the one used to evoke
MMN, in which a stimulus sequence consists of a com-
mon stimulus (‘standard’) and a rare stimulus (‘deviant’).
Stimulus-specific responses developed within 2–3 pre-
sentations of the standard, which represents highly sen-
sitive adaptation with a time scale of a few seconds. Such
stimulus-specific adaptation in auditory cortex could
underlie the generation of MMN.
Thus, the context-dependence of auditory cortex res-
ponses has been observed at time scales of years, months,
days, hours, minutes, and seconds, which is approaching
the time scale of responses to individual stimulus
Auditory scene analysis in auditory cortex
Several recent studies, using a variety of techniques,
suggest a role for auditory cortex in segregation and
grouping of sound components. For example, at the brain
potential level, Dyson and Alain  reported that the
amplitude of the mid-latency potentials increased when a
harmonic was mistuned, potentially creating two auditory
objects instead of one. Furthermore, the enhanced ampli-
tude was correlated with an increased likelihood of
reporting two concurrent auditory objects. Krumbholz
et al.  reported that when a noise burst suddenly
acquired pitch without energy transition, a pitch onset
potential occurred with a latency that was monotonically
related to the pitch period. The source of this potential
was localized to primary auditory cortex. Such a potential
could also be interpreted as indicating the onset of a new
A different aspect of auditory scene analysis was studied
byFishman etal. using multi-unit responsesand local
field potentials. They suggested the involvement of
auditory cortex in stream segregation. They recorded res-
ponses to an alternative sequence of tones, in which the A
tone was at best frequency and the B tone was away from
responses, but at higher repetition rates, the responses to
sequence of responses at half the stimulation rate. This
result is similar to the perceptual segregation of such an
alternating sequence into two ‘streams’ .
At the single neuron level, Fishbach et al. [17?,24] argued
that onset responses in cortex represent ‘auditory edges’,
and as such represent the onset of new objects. They
demonstrated that a simple model, essentially a differen-
tiator in time and frequency, can account for a large
If phasic responses in auditory cortex represent the onset
of new objects, then the lower limit of pitch, about 40 Hz
, could be related to the disappearance of phase
locking to periodic sounds by the majority of the neurons
in auditory cortex at about that rate, as discussed above.
Indeed, the disappearance of such time-locked responses
above about 40 Hz would create a perceptual continuity,
consistent with the perceptual transition from individual
events at each period to the sensation of pitch . Under
this interpretation, the inability of cortical neurons to
follow fast modulation rates is a feature, rather than a bug.
Using a different approach, Nelken et al.  showed that
cortical neuronssometimes respondtothecombinationof
a bird chirp and the weak natural background as if they
heard only the weak background. In the same vein,
Nelken et al. [56,57] found that cortical neurons respond
to the combination of a weak tone and strong fluctuating
noise with patterns that are similar to their responses to
weak tones presented alone. Both examples are inter-
preted as the results of auditory scene analysis, in which
by segregating the two components in the mixture and
responding to the weak component.
Speculative synthesis and conclusions
Most of the interesting auditory features might already be
extracted from the incoming sounds by the level of the
IC, which should therefore be considered as the auditory
analog of the primary visual cortex (V1). The role of
auditory cortex is to organize these features into auditory
Processing of complex stimuli and natural scenes in the auditory cortex Nelken477
Current Opinion in Neurobiology 2004, 14:474–480
objects (Figure 1). To do that, auditory cortex has to use
temporal and spectral context at several time scales. The
large adaptive and plastic capacity of auditory cortex is
used to tune the neural circuits to the statistical regula-
rities of the environment. In fact, it might be difficult or
impossible to separate the time scales at which context
sensitivity becomes adaptation and plasticity, as also
demonstrated in other neural systems [58,59].
As a result, neurons in auditory cortex respond to auditory
objects, representing the relevant distinctive features of
the objects to which they respond. When only single
objects are present, as is the case in most current experi-
mental paradigms, the neurons encode the sounds, often
in a simple way, by representing changes in the sounds at
multiple time scales. However,when the same soundsare
determine the features to which the neurons respond.
This context sensitivity can account for some of the
contradictions in the descriptions of the responses of
cortical neurons to complex sounds.
Although this review considered responses in primary
auditory cortex almost exclusively, the speculations for-
mulated here are consistent with the recent suggestion
that there are two streams of processing in the auditory
system beyond primary auditory cortex, a ‘what’ pathway
and a ‘where’ pathway [60,61]. It is conceivable that
primary auditory cortex constructs auditory objects, to
which higher auditory areas assign properties such as
phonemic identity (in the ‘what’ pathway) [62,63].
To test these speculations it will be necessary to test
auditory cortex with stimuli that will engage mechanisms
of auditory scene analysis. In particular, it is the use of
mixtures of sounds, in which more than one object is
present, which will probably prove crucial to further our
understanding of the role of auditory cortex in hearing.
Supported by grants from the Israeli Science Foundation (ISF), the
German-Israeli Foundation (GIF) and the Volkswagenstiftung.
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