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The Journal of Neuroscience, May 15, 1998, 18(10):3786-3802
Temporal and Spectral Sensitivity of Complex Auditory Neurons in
the Nucleus HVc of Male Zebra Finches
Frédéric E.
Theunissen and
Allison J.
Doupe
Sloan Center for Theoretical Neuroscience and Keck Center for
Integrative Neuroscience, Departments of Physiology and Psychiatry,
University of California, San Francisco, San Francisco, California
94143-0444
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ABSTRACT |
Complex vocalizations, such as human speech and birdsong, are
characterized by their elaborate spectral and temporal structure. Because auditory neurons of the zebra finch forebrain nucleus HVc
respond extremely selectively to a particular complex sound, the
bird's own song (BOS), we analyzed the spectral and temporal requirements of these neurons by measuring their responses to systematically degraded versions of the BOS. These synthetic songs were
based exclusively on the set of amplitude envelopes obtained from a
decomposition of the original sound into frequency bands and preserved
the acoustical structure present in the original song with varying
degrees of spectral versus temporal resolution, which depended on the
width of the frequency bands. Although both excessive temporal or
spectral degradation eliminated responses, HVc neurons responded well
to degraded synthetic songs with time-frequency resolutions of
~5 msec or 200 Hz. By comparing this neuronal time-frequency tuning
with the time-frequency scales that best represented the acoustical
structure in zebra finch song, we concluded that HVc neurons are more
sensitive to temporal than to spectral cues. Furthermore, neuronal
responses to synthetic songs were indistinguishable from those to the
original BOS only when the amplitude envelopes of these songs were
represented with 98% accuracy. That level of precision was equivalent
to preserving the relative time-varying phase across frequency bands
with resolutions finer than 2 msec. Spectral and temporal information
are well known to be extracted by the peripheral auditory system, but
this study demonstrates how precisely these cues must be preserved for
the full response of high-level auditory neurons sensitive to learned
vocalizations.
Key words:
birdsong; song system; Zebra finch; HVc; complex sound; natural sound; time-frequency; temporal-spectral; modulation transfer
function; auditory cortex; speech
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INTRODUCTION |
Temporal and spectral cues are
critical for the identification of complex vocalizations such as
speech, as shown in psychophysical experiments that use systematic
degradations of the speech signal along these parameters (Liberman et
al., 1967 ; Drullman, 1995; Drullman et al., 1995; Shannon et al.,
1995). Moreover, temporal processing is thought to be critically
involved in disorders of speech and language learning (Merzenich et
al., 1996; Tallal et al., 1996). Very little is known, however, about
the spectral and temporal sensitivity of the high-level central neurons
that must mediate complex sound processing. In recent studies,
researchers have described the response properties of neurons in the
auditory cortex of cats and primates that are tuned to certain
characteristics of natural sounds (Ohlemiller et al., 1994; Schreiner
and Calhoun, 1994; Rauschecker et al., 1995; Wang et al., 1995).
Birdsong provides a particularly useful model for studying the neural
basis of complex vocalizations, however, because, like speech, song is
a learned behavior and depends on auditory experience (Marler, 1970;
Konishi, 1985). Moreover, song acquisition and production are mediated by a specialized set of forebrain sensorimotor areas unique to species
that learn their vocalizations (Nottebohm et al., 1976; Kroodsma and
Konishi, 1991). Electrophysiological experiments have shown that the
brain areas for song contain some of the most complex auditory neurons
known. These "song-selective" neurons respond more strongly to the
sound of the bird's own song (BOS) than to almost any other sounds,
including simple stimuli such as pure tone or broadband noise bursts,
and complex stimuli such as closely related songs of other individuals
of the same species (conspecifics) (Margoliash, 1983, 1986; Margoliash
and Fortune, 1992; Margoliash et al., 1994; Lewicki, 1996; Volman,
1996). These neurons are also sensitive to the temporal context of the
sounds within the BOS, because both the BOS played in reverse and
isolated sections of the BOS, which elicit strong responses in their
natural context, are ineffective stimuli (Margoliash, 1983; Margoliash and Fortune, 1992; Lewicki and Arthur, 1996). Moreover, systematic modification of some of the parameters of white-crowned sparrow songs
demonstrated the dependence of HVc neural responses on both spectral
and temporal features of song (Margoliash, 1983, 1986). The highly
selective auditory properties of these neurons and the fact that these
features emerge during song learning suggest that these neurons play an
important role in vocal learning and in the discrimination of adult
vocalizations (Margoliash, 1983; Margoliash and Fortune, 1992; Volman,
1993; Doupe, 1997).
Although the general importance of spectral and temporal context for
the response of HVc neurons was clear, in this study we developed a
systematic and broadly applicable methodology, based on a
time-frequency decomposition that is commonly used in speech analysis
(Flanagan, 1980), to describe any song completely with a relatively
simple set of parameters. This parametrization allowed us to define
explicitly the spectral and temporal structure of these complex natural
sounds. We then systematically modified the parameters in the
decomposition to generate a series of synthetic versions of the BOS
that preserved varying degrees of the temporal and spectral structure
present in the original song. By comparing the response of HVc
song-selective neurons to these synthetic songs with their response to
the original BOS, we were able to characterize features of the temporal
and spectral structure in the BOS that were essential for HVc neurons,
and to quantify the sensitivity of the neuronal responses to the exact
preservation of these features. This characterization also revealed the
striking precision with which the temporal and spectral structure
present in these learned vocalizations needs to be preserved from the auditory periphery to higher order auditory centers.
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MATERIALS AND METHODS |
Song selection and recording
Two to three days before the experiment, an adult male zebra
finch was placed in a sound-attenuated chamber (Acoustic Systems, Austin, TX) to obtain clear audio recordings of its mature,
crystallized song (this species usually sings only one song type as
adults). An automatically triggered audio system was used to record
~90 min of bird sounds, containing many samples of the song of the bird. The tape was scanned, and 10 loud, clear songs were digitized at
32 kHz and stored on a computer. Those songs were assessed further by
calculating their spectrograms and by examining them visually. A
representative version was then chosen from those 10 renditions and
analyzed by a custom-made computer program to obtain a parametric
representation based on the spectral and temporal components of the
song (see below).
Zebra finch songs are organized into simple elements often called
syllables. These syllables are in turn organized into a set sequence
that is called a song phrase or motif. The motif is repeated multiple
times in a song (Zann, 1996, pp 214-215). We chose songs that varied
in length between 1.1 and 2.3 sec and consisted of two or three motifs.
The length of the song is important because it has been reported that
HVc neurons integrate over long periods of time and that the maximal
responses are not necessarily found in the first motif (Margoliash and
Fortune, 1992; Sutter and Margoliash, 1994).
Parametric representation of song
The analysis consisted of decomposing the original song into a
set of narrowband signals by filtering the song through a bank of
overlapping filters (Fig.
1A). The narrowband
signals could then be represented by two parameters, one that describes
the amplitude envelope and one that describes the time-varying phase of
the carrier frequency. The set of time-varying amplitude envelopes characterizes the time-varying power in each frequency band and therefore represents both the spectral and temporal structure of the
song. The time-varying phase carries additional spectral and temporal
information for each band, but as we will describe in detail in
Synthetic songs, this information can become redundant with the
information embedded in the joint consideration of the amplitude
envelopes. In this section, we describe the mathematics involved in the
original decomposition. The next section describes what aspects of the
spectral and temporal structure are actually represented in the
amplitude and phase components, how the two are related, and how we
used variations of these parameters to generate songs with specific
spectral and temporal properties.
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Figure 1.
A, Schematic showing the
decomposition of a complex sound into a set of narrowband signals, each
described by an amplitude envelope and a frequency-modulated carrier.
The complex sound is the input to a filter bank composed of a set of
adjoining, and in this case overlapping, filters that cover the
frequency range of interest. The narrowband output signals of two of
the filters in the bank is shown. The envelope that was obtained with
the analytical signal is drawn. The carrier frequency is centered at
the frequency corresponding to the peak of the filter and has slow
frequency modulations that are not easily discernible in this figure.
B, Overall filter transform (thick line)
obtained from a set of overlapping Gaussian filters (thin
lines), the center frequencies of which are separated by one
bandwidth (1 SD). The overall filter transform is almost perfectly flat
for a large frequency range. In this example, we used 15 Gaussian
filters with a bandwidth of 500 Hz and center frequencies between 500 and 4000 Hz.
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The decomposition of each narrowband signal into its amplitude and
phase constituents was obtained using the analytical signal (Cohen,
1995). As will be emphasized below, this particular decomposition generates an amplitude envelope function that is identical to the one
obtained by calculating the short time Fourier transform of the signal,
just as is done when a spectrogram is generated. In addition, this
operation generates the phase of the short time-window Fourier
transform in a form that is continuous with time and that can be
interpreted as an instantaneous frequency modulation. A detailed
mathematical description of this parametric representation can be found
in Flanagan (1980). The decomposition is briefly summarized here.
The original signal s(t) is first divided into
n bandpassed component signals
sn(t). To be able to
resynthesize the original signal from the bandpassed components, we
must choose the filters in the filter banks so that the overall filter
transform (obtained by summing the transforms from each filter) is flat
over all frequencies occupied by s(t). In
addition, the phase distortion of each filter must be insignificant. If
these requirements are satisfied, we can recreate the original sound by
summing all of the bandpassed signals:
In our decomposition, we used Gaussian filters that were separated
along the frequency axis by exactly 1 SD. It can be shown analytically
(and verified numerically) that the deviations from a flat amplitude
transform in that case are of the order of 1 in 109
(Fig. 1B). Enough filters were used to cover the
frequency range from 500 to 8000 Hz. The filtering was performed
digitally in the frequency domain, resulting in no phase
distortions.
In the next step, to extract the instantaneous amplitude envelope
An(t) and the instantaneous
phase  (t) of each narrowband signal, we calculated the
analytical signal of each
sn(t):
The analytical signal decomposition of
sn(t) guarantees that the
frequency components of An(t)
are all below those of cos[(t)] (Cohen, 1995). In
particular, it can be shown that for a bandpassed signal of bandwidth
 w, all of the frequency components of
An(t) 2 are
below w (Flanagan, 1980). In general,
A(t) is what one would intuitively call the
amplitude envelope of the signal. For example, the
A(t) calculated for a beat signal made of two pure tones with amplitudes A1 and
A2, frequencies w1
and w2, and absolute phases
1 and 2 is given by:
A(t) 2 = A12 + A22 + 2 A1A2
cos[(w1  w2)t + (1 2)]. The calculation of A(t) for
a complex signal is just the extension of this simple vector sum to
include all frequency components of the signal. Finally, it can be
shown that An(t) corresponds to
the amplitude at the center frequency wn
of the Fourier transform of s(t) as seen by a
window centered around t and with shape given by the inverse Fourier transform of the filter transform function expressed in the
frequency domain. In other words,
An(t) 2 is the
running power at frequency wn calculated
with a window centered around t. The width and shape of the
window is related to the shape and width of the frequency filter. This
is exactly the value achieved when one calculates a spectrogram of a
signal.
The part of the signal that is not described by the amplitude envelope
(and therefore not shown explicitly in a spectrogram) is often called
the fine structure of the signal and is given in the analytical signal
by an instantaneous phase, (t). The instantaneous phase
can in turn be expressed in terms of its derivative and an absolute
phase. The derivative of the instantaneous phase is taken as the
instantaneous frequency:
We further expressed the instantaneous frequency as a modulation
around the center frequency of the band
wn:
In this final form,
An(t) will be referred to as
the amplitude modulation or AM component of the signal,
wFMn(t) is the frequency
modulation or FM around the center frequency wn, and n is the
absolute phase.
Synthetic songs
Four synthetic song families were generated using systematic
degradations of the parametric representation described above. Each
family of songs preserved some aspect of the original signal. In the
following description, the song families are organized approximately in
terms of increasing similarity with the original signal. The first set
of songs was generated by preserving only the AM components in the
decomposition. This resulted in synthetic songs with amplitude
envelopes similar to that in the original song. The second set of songs
progressively restored the relative instantaneous phase across
frequency bands, improving both the FM and AM quality of the synthetic
song. The third and fourth set distorted the FM component by additive
FM noise. This distortion was done in two ways, one that randomized
(third set) and one that preserved (fourth set) the original relative
phase. Finally, as a control, we also created the single synthetic song
that preserved all of the original parameters. This song is referred to
as Syn in Results. The Syn song is identical to the original song
filtered by the combined filter transform function obtained from our
filter bank.
Synthetic AM songs and the time-frequency scale. The first
set of songs was generated by preserving the AM components obtained in
the decomposition but by generating a new and random instantaneous phase for each bandpassed signal. The instantaneous phase was chosen to
be random so that the new component bandpassed signals of song become
effectively noise, band-limited to the same frequency band as the
original bandpassed signal and modulated by the same amplitude
envelope. The full degraded synthetic song is the sum of these
narrowband signals. A family of such AM songs can be generated by
increasing or decreasing the width of the filters in the filter bank
used to extract the AM waveforms of the original song.
When the filter bandwidth is very wide, the entire song will fit in the
band of a single filter, and the resulting AM song will be similar to
white noise modulated by the overall amplitude envelope of the signal
(see Figs. 2, 6, AM-1 panel). As one narrows the
bandwidths of the filters, more filters are needed to cover the entire
song, and the amplitude envelopes from each filter characterize the
spectral structure more precisely. However, because of the
time-frequency resolution trade-off, the amplitude envelopes in each
band will now be limited to coarser time resolutions
[An(t) is band-limited by the
width of the filter]. Normally, when the full song is resynthesized by
summing the signals in each band and by preserving all parameters, the
fine temporal aspect of the overall song envelope is recovered because
the phase in each band interacts with that in the other bands in a
specific manner to recreate the overall temporal structure of the
signal. However, by randomizing the phase, we eliminated this
particular relationship between the phases in each band and affected
the overall temporal structure. Because our phase is random, the
overall time resolution is effectively the time resolution of the
amplitude envelopes in each band. This time resolution is given by the
inverse of the bandwidth of the filters. Just as the songs modified
after filtering through wideband filters have good temporal but poor spectral resolution, songs created at the very narrowband filter extreme characterize the frequency content of the song well but have
poor temporal resolution; the amplitude envelopes in each band are
effectively flat, and the resulting song is a colored-noise signal with
a flat amplitude and overall frequency spectrum identical to that of
the original signal (see Fig. 6, AM-256 panel). At intermediate time-frequency resolutions, the synthetic AM signals can
capture both the spectral and temporal structure of the original signal
but always with a particular trade-off between time and frequency (see
Fig. 6, intermediate panels). We subsequently denote the width of the filters used in generating an AM song as the time-frequency scale of the synthesized signal. The AM songs are labeled with "AM scale," in which the scale is a number specifying the time scale in milliseconds.
The time-frequency scale trade-off of the AM songs is illustrated (see
both Figs. 2, 6). These figures show spectrograms for synthetic AM
songs generated with progressively narrower frequency filters. In
Figure 2, we also show the spectrograms
of the original signal calculated with the same windows that were used
to obtain the AM songs. This allows for direct comparison between the
amplitude envelope of the AM songs and those of the original song. In
Figure 6, the full range of AM songs is displayed in spectrograms all calculated with the time-frequency scale that was best at representing the original song. These spectrograms illustrate how, as one goes from
AM-1 to AM-256, spectral resolution is gained at the cost of temporal
resolution.
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Figure 2.
Wideband (W-1), middleband
(W-16), and narrowband
(W-256) spectrograms generated with different
time windows for a representative section of a zebra finch song motif
(BOS) and three synthetic AM songs derived from that
particular song (AM-1, AM-16, and
AM-256). The time windows used to generate the
spectrograms had a Gaussian shape and a width of 1, 16, or 256 msec,
respectively. The three AM songs were generated by preserving the AM
waveforms of the frequency decomposition of the original BOS obtained
with a bank of Gaussian-shaped frequency filters, as explained in
Materials and Methods. The filters also had widths of 1, 16, or 256 msec expressed in the time domain (1 kHz, 62.5 Hz, or 3.9 Hz,
respectively, in the frequency domain). Therefore, the
W-1 (W-16 and
W-256) spectrogram for the AM-1
(AM-16 and AM-256) song
approximately matches the W-1 (W-16 and
W-256, respectively) spectrogram for the
BOS. At other time-frequency scales, the spectrograms
of the AM songs do not match that of the BOS,
illustrating the information that is lost in the AM songs. The
AM-1 song preserves the fine temporal modulations but
does not have the frequency resolution of the BOS. The
AM-256 has good frequency discrimination calculated at
longer time scales (notice the finer frequency bands for the last
harmonic stack in the song) but has smeared the temporal structure
present in the BOS. The AM-16 shows good
time-frequency compromise.
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In our experiments, we used a range of frequency filters by varying
their width from 2 kHz to 2 Hz in logarithmic steps. The corresponding
width of these filters in the time domain ranged from 0.5 to 512 msec.
To cover the frequency range from 500 to 8000 Hz, the number of filters
ranged from 4 for the 2-kHz-wide filters to 3840 for the 2-Hz-wide
filters. For each time-frequency value describing the filter width, we
generated a synthetic AM song.
Mathematically, the synthesis went as follows. The
An(t) in the synthesis was
calculated from the original song, but the
wFMn(t) and
n were random. The random
wFMn(t) was generated so
that wFM had a Gaussian distribution of zero
mean and SD equal to the bandwidth of the filters in the filter bank,
w. In addition, we required that
wFMn(t) be band-limited to
frequencies below w. These two requirements
guarantee that the function:
is the analytical representation of a bandpassed signal centered
at wn, with bandwidth
w and unit amplitude (i.e., flat bandpassed
noise). Finally, these unit amplitude signals from each frequency band
were multiplied by the original
An(t) and were summed together.
The result was a synthetic song with an amplitude envelope in each band
similar to that in the original song but with significantly different
fine structure.
The resulting synthetic songs have an amplitude envelope in each of
their component bands similar to but not exactly the same as that in
the original signal because, in the AM songs, the phase relationship
between each band and its neighboring "overlapping" frequency bands
was altered. Just as randomizing the phase altered the overall
amplitude envelope in the AM songs, it will also alter the amplitude
envelopes in each band when all the bands are summed together in the
synthesis. In other words, in fully parameterized song, there exists
redundant information in the time-varying amplitude envelopes and in
the relative phase across overlapping frequency bands. One cannot
therefore be scrambled without affecting the other. Under certain
conditions (of enough overlap between the frequency bands), the
amplitude envelopes can completely determine the value of the relative
phase across frequency bands. In those cases, one can say that the
spectrogram (i.e., the set of amplitude envelopes) is invertible in the
sense that the original signal (except for an absolute phase) can be
recovered solely from the set of amplitude envelopes. The relative
instantaneous phase and therefore the exact representation of the
amplitude envelopes will be restored in the family of synthetic songs
described in the next section.
To estimate the degree of distortion of the
An components of the AM synthetic songs,
we calculated the normalized cross-correlation between the
An of the original song and the
An of the synthetic songs (see below for
the definition of cross-correlation). We found that the average
cross-correlation (± SEM) was 0.737 ± 0.003 (range,
0.634-0.798) for all 74 AM songs used in these experiments. Significantly for the interpretation of our results, this value was
independent of the width of the filters used in generating the songs.
Our AM synthetic songs can therefore be thought of as the typical
signal that would be estimated in a inverting operation (done, e.g., by
the high-level auditory areas) from a noisy representation of the
complex sound by its amplitude envelopes (e.g., noisy neural encoding
of these envelopes at the auditory periphery). The amount of noise is
equal to ~26% of the signal. The noise in the representation is more
detrimental to temporal information when many frequency bands are used,
because in those cases the temporal information is present in the fine
differences in amplitude across bands. Similarly, the noise is more
detrimental to spectral resolution when few frequency bands are used.
To eliminate completely the noise in the amplitude envelopes, we had to
restore the relative phase across bands perfectly. Therefore in our
particular decomposition using overlapping Gaussian frequency bands,
the amplitude envelopes can fully characterize the signal (except for
an absolute phase that can shift the phase by the same amount in each
band).
Note that any other mathematical representation of a signal in terms of
sums of amplitude envelopes [including those used in Shannon et al.
(1995)] is also affected by the fact that one cannot independently
change time-varying spectral and temporal information. For example,
decreasing the overlap between the filters would reduce the
contamination in the amplitude envelope attributable to the interaction
with the neighboring bands but would result in an increase in spectral
fluctuations caused by a nonuniform sampling of the frequency range
covered by the overall filter transform of the filter bank (as shown in
Fig. 1B). Both errors in the synthesis could
apparently be eliminated by using nonoverlapping boxcar filters, but in
reality the amplitude envelope of a synthetic song made from such
boxcar filters would only match the amplitude envelopes of the original
song extracted with the exact same set of filters that was used to
obtain the An(t) waveforms for
the synthesis. The amplitude envelopes of the synthetic and the
original song extracted with differently shaped filters or with filters of the same shape but shifted along the frequency axis would be different, again because of different interference terms. For example,
for boxcar filters, the error would be the greatest for amplitudes
extracted when the filters were shifted by exactly one-half the
bandwidth. On the other hand, our formulation, using Gaussian
overlapping filters, would result in similar errors for amplitude
envelopes extracted with filters (of equivalent bandwidth) of any shape
and centered at any arbitrary point along the frequency axis. This
uniformity of representation of the amplitude envelopes is
physiologically more realistic, just as the shape and the overlap of
our overlapping Gaussian filters constitute a better model of the
auditory periphery than does a set of nonoverlapping boxcar filters.
These were important factors, because we wanted to analyze our results
in light of the encoding occurring at the different stages of the
auditory system. Finally, we wanted to use a formulation that was
completely symmetric along the time and frequency dimensions, so that
we could interchangeably quantify the scale of our AM synthetic song
(given by the width of the filters) in the time domain or in the
frequency domain. The choice of Gaussian filters separated by 1 SD was
the result of all of these considerations.
Songs that preserve the relative instantaneous phase. In the
second and third set of synthetic songs, we progressively restored the
fine structure components of the signal that had been eliminated from
the AM songs. Our starting point was the AM synthetic song generated
for the time-frequency scale of 16 msec or 62.5 Hz (AM-16). This
particular time-frequency scale was chosen both because AM songs
generated at this scale elicited good responses from HVc neurons and
because the amplitude waveforms calculated at this scale were the most
informative for discriminating among zebra finch songs from different
birds (see Results).
In the second set of synthetic songs, we progressively restored the
instantaneous relative phase across adjoining frequency bands. In
practice, we generated a set of songs with Gaussian noise added to the
values of the instantaneous phase waveforms n(t), obtained from the original song. This
is different than the situation for the AM songs in which the
instantaneous phase waveforms from the original song were ignored and
new random instantaneous phase waveforms were generated. The amount of
noise was specified to preserve the relative phase across adjoining
frequency bands to within a given temporal resolution. The temporal
resolution was implemented by allowing Gaussian deviations from the
original relative phase at each time point. The value of the temporal
resolution was varied by changing the width of the Gaussian noise. The
width of the Gaussian was expressed in radians, which were translated into time units by dividing by (2 )62.5. The value of 62.5 Hz corresponds to the interval between the center of two adjoining frequency bands for the time-frequency scale of 16 msec.
Songs with temporal resolutions in the relative phase ranging from 10 to 0 msec were generated. Gradually restoring the relative instantaneous phase had two effects; it progressively restored the FM
in each band and improved the quality of the AM component. The FM
component is the derivative of the instantaneous phase, which is
independent of the absolute phase and will therefore be preserved when
the relative phase is preserved. The quality of the AM component also
depends on the relative phase in order to obtain the same interference
terms as those of the original song (see synthetic AM songs and the
time-frequency scale). To indicate the accuracy of the representation
of the AM component, we also calculated for each temporal resolution
the cross-correlation value between the AM component of the synthetic
songs and the one of the original song. The 0 msec temporal resolution
resulted in a synthetic song that was similar to the original song
except for an identical shift in absolute phase in all frequency bands. That particular synthetic song was called RAP for random absolute phase. The RAP song has the same AM and the same FM that the BOS has.
Songs that preserve various amounts of the FM component. For
the third and fourth sets of songs, we added noise to the original FM
component in each band. In addition, the fourth set preserved the
relative phase exactly across all bands. To preserve the relative phase, we added the same FM noise to every frequency band. For the
third set, independent frequency noise was added to the FM component in
each band. We generated a set of songs by varying the SD of the FM
Gaussian noise from 0 to 30 Hz. As in the previous set of songs, the FM
noise was band-limited to frequencies below 62.5 Hz. Recall that an
AM-16 song is generated with random Gaussian FM with 62.5 Hz SD; thus
30 Hz corresponds to approximately half that amount of noise. For the
synthetic songs designed to preserve various amounts of FM, however,
the noise was added to the FM components of the original song. The
absolute phase was also random, one random shift for all absolute
phases for the set that preserved the relative phase and an independent
phase shift in each band for the set that did not preserve the relative
phase. The randomness of the absolute phase is only significant in the
0 Hz case. The 0 Hz noise value corresponds to synthetic songs with the
original FM. For the cases in which the relative phase was preserved
(fourth set), the 0 Hz song was identical to the RAP song. For the
cases in which we generated a different absolute phase in each band (third set), the 0 Hz song will be called RP for random phase.
Measure of song similarity based on the amplitude envelope
We estimated the degree of similarity between zebra finch songs
from different birds or between our synthetic songs and the original
song by calculating the normalized cross-correlation coefficient of
their respective AM components:
The   indicate averages over the length of the
song. CA was calculated for a range of
time delays between the two signals, and the largest correlation was
taken as the measure of song similarity. Because different songs could
vary in duration, the time averages were performed only for the
duration of the shortest song.
The correlation measures were repeated for a range of time-frequency
scales to allow the study of the effect of time-frequency scale on the
discriminability of songs based on their amplitude waveforms. We also
performed this calculation on a syllable-by-syllable basis by comparing
syllables from one song with syllables from the other songs. By looking
only at syllables, we could separate the effect of the temporal scale
given by the rhythmic succession of silences and syllables from the
temporal scale of the sounds within a syllable. The syllable
cross-correlation reported in this work was limited to the pair of
syllables that were the most similar between the two songs. These
particular pairs are presumably the most difficult to
differentiate.
The syllable decomposition was done by a computer program that
automatically divided the song into sections of sounds and silences
based on the waveform profile of the overall power envelope calculated
with an 8 msec hanning window. The peaks and troughs of this amplitude
envelope that were a factor of 10 apart were used to define sections of
silence and sections of sound in the song. The sections of sound could
be separated by very short silences (one point or 4 msec) and
vice-versa. The temporally discrete sections of sounds obtained with
this algorithm are a particular implementation of what is usually
defined somewhat subjectively as a syllable in the zebra finch song by
human experts (Sutter and Margoliash, 1994; Zann, 1996, pp 214-215).
Figure 3 shows the syllable decomposition
obtained for one of the songs used in this work. Our algorithm
efficiently divides the song into syllables, with the limitation that
for syllables separated by longer periods of silence, the boundaries
between sound and silence are not necessarily at the same threshold
levels of sound intensity that would be used by a human expert. Because
the measurement of the length of the syllables or of the interval
between syllables was not part of our work, these differences are not
important.
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Figure 3.
Spectrogram (top) and overall power
envelope (bottom) of one of the representative songs
used in these experiments. The vertical lines are the
divisions obtained from a computer program that automatically divides
the song into syllable-like elements based on the peaks and troughs of
the overall power (see Materials and Methods). Syllables 9-14 were
chosen for the color spectrograms (see Figs. 2, 6).
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The cross-correlation analysis was performed for 16 songs from our
zebra finch colony, including the songs from the seven birds used in
this experiment. The songs belonged to birds from different families
and had different temporal and spectral structure. This ensemble of
songs was not necessarily representative of all song sounds that zebra
finches can produce but was more than sufficient to characterize the
time-frequency scale of zebra finch sounds, as evidenced by the small
error bars that we obtained for the CA
measure.
Electrophysiology
All physiological recordings were done in anesthetized adult
male zebra finches in acute experiments. Two days before the recording
session, a small surgical procedure was performed to prepare the bird
for the recording session. The bird was anesthetized with 20-30 µl
of Equithesin intramuscularly (0.85 gm of chloral hydrate, 0.21 gm of
pentobarbital, 0.42 gm of MgS04, 2.2 ml of 100%
ethanol, and 8.6 ml of propylene glycol to a total volume of 20 ml with
H20), and a small patch of skin on the head was removed to
expose the skull. The top bony layer of the skull was removed around
the dorsal part of the midsagittal sinus and in an area a few
millimeters lateral of the sinus. A ink mark was made 2.4 mm lateral
from the dorsal bifurcation point of the sinus to be used as a
reference point for electrode penetration. Finally, a metallic
stereotaxic pin was glued to the skull with dental cement.
For the recordings, the bird was slowly anesthetized with urethane (75 µl of a 20% solution administered in three doses over a 1.5 hr
period) and immobilized with the stereotaxic pin. A very small patch of
the lower layer of the skull and the dura was removed at the marked
location exposing the brain. Extracellular electrodes were inserted
through this opening at and around the location originally marked by
the ink dot. The stimuli were presented inside a sound-attenuated
chamber (Acoustic Systems) with a calibrated speaker 20 cm away from
the bird. The volume of the speaker was adjusted to deliver peak levels
of ~85 dB. The rate-intensity function of HVc neurons quickly
plateaus above threshold values. The 85 dB value was chosen so that the
sound level of the song was in the range at which the rate-intensity
function of the neurons is flat (Margoliash and Fortune, 1992). We did
not investigate what effect low sound levels would have on our results.
Data were collected when the base line activity and auditory responses
were characteristic of the nucleus HVc. As reported in other studies both for zebra finches (Margoliash and Fortune, 1992) and for white-crowned sparrows (Margoliash, 1986), HVc responses, in both the
urethane-anesthetized and the awake-restrained animal, are characterized by bursting spontaneous activity and by auditory responses that show a strong preference for the BOS in comparison with
the responses to other complex auditory stimuli, such as the BOS played
in reverse or the song of conspecifics. These characteristic properties
can be used to distinguish the neural responses of HVc neurons proper
from those of the neighboring neostriatal areas. Our experience is in
complete agreement with this phenomenology. The exact location of the
recording sites was also verified postmortem by finding the electrode
tracks and lesion reference points in Nissl-stained sections of the
brain of the bird [for detailed histological methods, see Doupe
(1997)]. The data from this paper consist solely of recordings from
within the nucleus HVc [for a detailed anatomical description of the
HVc, see Fortune and Margoliash (1995)].
The data consisted of neural responses obtained in 77 distinct
recording sites in seven birds. In any particular bird, the recording
sites were at least 75 µm apart. This distance was sufficient to
guarantee that the neural activity recorded from two successive sites
originated from different units. A window discriminator was used to
translate the neural activity at each recording site into spike arrival
times of small clusters of one to five neurons. The single-cell spike
arrival times were obtained when a stereotyped spike shape was easily
selected with a window discriminator. The multiunit recordings
consisted of spikes of various shapes that could easily be
discriminated from the background activity with a window discriminator
but not from each other. We assessed that responses from small clusters
of two to five neurons were obtained in such recordings. Additional
single units were isolated from the clusters of neurons using the
spike-sorting algorithm of Lewicki (1994) and showed very similar
results to the small clusters of neurons and to the single units
isolated with a window discriminator but were not used in the analysis
presented here.
Stimulus repertoire and presentation
Stimulus repertoire consisted of the BOS, all of the synthetic
versions of the BOS, the BOS played in reverse, the BOS played in
reverse order, two conspecific songs, and broadband noise bursts. In
addition, in some experiments, we used pieces of songs to test for
temporal combination-sensitive neurons. The BOS, the BOS played in
reverse, the broadband noise bursts, and the conspecific songs were
used both as search stimuli and to initially characterize the
selectivity of the recording sites for the BOS. The synthetic stimuli
were then presented in subgroups that consisted of most of the
synthetic songs from one of the four families and the BOS. Ten
interleaved trials were collected for all of the stimuli in the
subgroup. The stimulus presentation order was randomized for each trial
number. The interstimulus interval was between 7 and 8 sec. Two seconds
of background activity was recorded before each stimulus, and between 4 and 5 sec was recorded after the stimulus. An additional time interval
between 1 and 3 sec (uniform random distribution) was added between
collections. When a single stimulus such as the BOS was presented with
this interstimulus interval, no measurable adaptation in the responses
was found.
In the analysis, the response to the synthetic songs was compared with
the response to the BOS obtained during the same collection trials.
This was a precaution used in case the response properties were not
stationary during the long period of time that was required for the
presentation of all synthetic stimuli. Because the set of collection
trials was repeated for a subgroup of stimuli to assess stationarity,
we obtained between 10 and 40 trials for each stimulus. However, 10 trials were used most of the time to be able to record the responses to
the largest ensemble of synthetic stimuli at each recording site. This
small number of trials was sufficient to characterize single recording
sites in terms of their classic selectivity properties (i.e., BOS vs
conspecific song) because the magnitude of the response is clearly
different in those cases. For stimuli that gave similar responses (such as AM-16 vs AM-8), more trials would be required in particular cases to
obtain significant differences for single recording sites (although
some neurons showed statistically significant differences). Our
conclusions for those stimuli are based on the population study.
Not all synthetic stimuli were presented at each recording site. The
total number of recording sites for each stimulus is specified in each
of the figure legends when the results are presented. Table
1 summarizes the number of recording
sites per bird and the number of sites where data were obtained for
each of the four synthetic stimuli ensembles.
Neural response characterization
The neural response to any given stimulus was expressed as a
Z score. The Z score is given by the difference
between the firing rate during the stimulus and that during the
background divided by the SD of this difference quantity:
where µS is the mean response during the stimulus
(S) and µBG is the mean response during the background
(BG). The denominator is the equation for the SD of S BG. The
background was estimated by averaging the firing rate during the 2 sec
period before the stimulus. For each unit, the Z score for
the response to any stimulus was then compared with the Z
score for the response to the BOS by calculating the ratio of these two
values. The Z score was in most cases larger for the BOS
than for any other stimuli, so that the fraction of the BOS
Z score is also an estimate of the response relative to the
maximal response. The fractions for different units were then averaged
to generate a result for the entire data set.
We also used the psychophysical measure d' (Green and Swets,
1966) to estimate the strength of the selectivity of the recorded neurons. The selectivity of HVc neurons is determined by their response
to the BOS in comparison with their response to conspecific songs or to
the BOS played in reverse. We calculated the d' to estimate
the difference between such responses. The d' value for the
discriminability between stimuli i and j is
calculated as:
where R is the response to a given stimulus.
 is the mean value of R, and is its
SD. We took R to be S BG. The d' value for neuronal responses can be compared with psychophysical or behavioral responses in a forced-choice paradigm [see, for example, Delgutte (1996)]. For our purposes, d' is the simplest
measure of selectivity that takes into account not only the estimate of mean responses but also their variance.
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RESULTS |
Song selectivity
In this paper, we were interested in quantifying the selectivity
seen in HVc neurons. Our goal was to find what aspects of the
acoustical structure inherent to all songs are essential to obtain
neural responses and to measure the sensitivity of the neurons to
systematic degradation of the necessary structure. The first step in
our analysis involved measuring the classic song selectivity of the
auditory neurons recorded in the experiments. A rigorous quantification
of the selectivity was needed to compare our responses with those found
in previous work and to select a group of neurons from our data set
that we determined to be highly song selective. The song selectivity in
HVc neurons has been characterized by a much stronger mean response to
the BOS than to songs from conspecifics or to the BOS played in reverse (Margoliash, 1986; Margoliash et al., 1994; Lewicki and Arthur, 1996;
Volman, 1996). To also take into account the variance seen in the
responses, we chose to quantify the degree of selectivity by
calculating the psychophysical measure of discrimination d' (see Materials and Methods).
Figure 4A shows the
cumulative probability distribution of the d' measure for
BOS versus conspecific song for all the recording sites in our data
set. The mean d' value is 2.3 with 86% of the recording
sites showing a selectivity greater than d' = 1.0. These values are not necessarily representative of all auditory neurons in
HVc because we did not attempt to map the nucleus in a systematic manner. For certain recording sites, the responses to conspecific songs
were missing, but we had data to characterize the selectivity of BOS
versus BOS played in reverse. In either case, all neural recordings for
which d' was >1 were classified as song selective.
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Figure 4.
A, Cumulative probability
distribution of the measure d' from signal detection
theory for the discriminability between the BOS and conspecific songs
(Con), calculated from the neural responses obtained at
54 recording sites. B, Response, measured as a percent
of the response to the BOS, for the synthetic song that preserved all
of the parameters obtained in our decomposition (Syn),
for the song played in reverse (Rev), and for
conspecific songs (Con). The data are obtained from
n = 30 for Syn,
n = 39 for Rev, and
n = 47 for Con (n
refers to the number of recording sites). The error bars show 1 SEM.
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Figure 4B shows the mean relative Z score
of all song-selective responses to conspecific songs and to the BOS
played in reverse. The response to these stimuli was close to zero.
Figure 4B also shows the mean response to the
synthesized BOS in which all parameters in the decomposition have been
preserved (Syn). As expected, the response to Syn is
statistically indistinguishable from the response to the BOS because
the two stimuli are identical except for the overall bandpass filtering
from 500 to 8000 Hz. Figure 5 shows the
peristimulus spike time histogram (PSTH) and the single-spike train
records from a single-unit recording for these four stimuli.
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Figure 5.
Individual spike rasters and peristimulus time
histograms (top) for the response of a particular single
unit in the HVc to the BOS, Syn,
Rev, and Con stimuli (see Fig. 4).
Oscillograms (waveform representations of the sound pressure) of the
stimuli are shown below each histogram. The
d' for this particular single unit was 1.5. As shown in
Figure 4, ~75% of the recording sites showed greater selectivity
than did this particular neuron, and this neuron, despite its evident
selectivity, is among the less selective members of the population that
was used for the studies involving the synthetic stimuli
(d' > 1).
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Time-frequency scale tuning
To investigate the spectral and temporal requirements of the
song-selective neurons, we first generated synthetic songs that, when
decomposed into defined frequency bands, had amplitude envelopes similar to that of the BOS in each frequency band. However, the time-varying phase of the signal in each band (which can also be
expressed as a frequency modulation and absolute phase) was set to be
different from the one in the original BOS and was randomized. By
varying the width (and correspondingly the number) of the frequency bands, we generated a set of AM songs with systematically varying degradations of temporal versus spectral resolution (see Materials and
Methods for more details). Figure 6 shows
the spectrograms for a set of such AM songs, illustrating the trade-off
between synthetic songs that preserve the time structure of the
original song (AM-1 to AM-16) and
synthetic songs that preserve the spectral structure of the original
song (AM-16 to AM-256). Based on visual inspection, the synthetic songs in the middle values of the
time-frequency scale (AM-16 or AM-32) show a
good compromise, achieving seemingly minimal temporal and spectral
degradation. We will come back to this issue in the next section.
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Figure 6.
Spectrograms of a representative section of an
original song and its corresponding degraded AM synthetic songs. The
spectrograms of the AM-1 to AM-256 songs
are shown. The songs generated with small time windows (1-4 msec)
preserve the temporal modulations seen in the original song but have
poor frequency resolution. For long time windows (such as 256 msec),
the spectral resolution calculated at longer time scales is good, but
the temporal structure present in the original signal is smeared. The
symbols (*,  ) indicate the time-frequency scale that
gave the best neural response (*) and the best discrimination among
songs () (see Fig. 10 and the corresponding text). The same
symbols are also used below (see Figs. 7, 8). All
spectrograms displayed in this figure were generated with 16 msec
Gaussian windows.
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Figure 7 shows the PSTHs for a
representative single unit obtained in response to eight AM songs
generated with time windows ranging from 0.5 to 64 msec. The PSTHs in
this figure can be compared with the ones obtained from the same unit
in response to the original BOS and other songs shown in Figure 5. As
the time window was increased, the responses of this particular neuron
to the AM songs increased, peaked at ~16 msec, and then decreased.
The response to AM songs synthesized with time windows of <2 or >32
msec was indistinguishable from background activity. The maximal
response obtained at 16 msec was slightly less than was the response to the BOS. Thus, this neuron showed good responses to synthetic songs
based only on the amplitude envelopes, as long as these were obtained
in a particular time-frequency range. This time-frequency scale
included songs that, by visual inspection of the spectrograms of
Figure 6, were good at representing both the spectral and temporal structure in song (AM-16 in Figs. 6, 7) but also included
songs that showed substantial spectral degradation (AM-4 and
AM-8 in Figs. 6, 7).
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Figure 7.
Peristimulus histograms for a single-unit
recording in response to the set of AM songs spanning the range of
time-frequency scales between 0.5 and 64 msec. The responses to AM
songs generated with time windows of >64 msec were similar to those
obtained at 64 msec. The stimuli started at t = 2 sec and lasted ~1 sec. This single unit and song were from bird
zfa_18. This neuron is the same as that of Figure 5. The
symbols (*, ) indicate the time-frequency scale that
gave the best neural response (*) and the best discrimination among
songs () (see Figs. 6, 8, 10).
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The mean relative Z score of all song-selective neuronal
responses in our data set for the entire range of AM songs is shown in
Figure 8A. All
individual song-selective recording sites exhibited similar tuning,
with responses that peaked at time-frequency scales between 2 and 16 msec. The exact location of the peak as well as the width of the tuning
curve varied slightly across units, as exemplified in the three
single-unit response traces shown in Figure 8B and in
the example shown in Figure 7. This variability was present both in
neuronal responses from the same bird (as shown here) or across
birds.
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Figure 8.
A, Time-frequency tuning curve of
HVc in response to AM song stimuli. The x-axis shows the
time (bottom) or frequency (top) scale
that was used to generate the AM song stimuli. The response is measured
as a percent of the response to the BOS. The error bars show 1 SEM. The
number of recording sites for each stimulus was n = 31 for t = 0.5 msec, n = 31 for
t = 1.0 msec, n = 42 for
t = 2.0 msec, n = 35 for
t = 4.0 msec, n = 41 for
t = 8.0 msec, n = 37 for
t = 16 msec, n = 42 for
t = 32 msec, n = 33 for
t = 64 msec, n = 40 for
t = 128 msec, and n = 25 for
t = 256 msec. The symbols (*, )
indicate the time-frequency scale that gave the best neural response
(*) and the best discrimination among songs () (see Figs. 6, 7, 10).
B, Time-frequency tuning curves for three different
single units from an individual bird. The x- and
y-axes are identical to those in A.
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In all cases, the response at the extreme time-frequency scales was
similar to or below background. The stimulus at the extreme time scale
of 0.5 msec is similar to a broadband white noise stimulus modulated by
the overall amplitude envelope of the BOS calculated with a 0.5 msec
window. This stimulus is analogous to the noise stimulus defined in
Margoliash and Fortune (1992). For that particular stimulus, our
results are similar to theirs; they reported a weak response to noise
syllables, and we found a weak or inhibitory response for the AM-0.5
synthetic song. At the other end of time-frequency scale, the
synthetic song preserves the overall spectrum of the BOS, but
because we randomized the phase of its frequency components, it has
lost almost all of the original temporal modulations (Figs. 2, 6); such a stimulus is often referred to as colored noise, as
opposed to white noise that is characterized by a flat spectrum. As
seen in Figure 8, this stimulus also elicited a weak or inhibitory response. The song played in reverse is another example of a synthetic stimulus that preserves the spectral quality of the song on the time
scale of the song duration but distorts the temporal structure. In the
song played in reverse, the temporal distortion is a very particular
one, whereas the distortion from the random phase in the AM songs
generates a systematically degraded version of the original temporal
envelope (see Materials and Methods).
Neither AM song with a very precise temporal profile nor AM song with a
highly precise spectral profile elicited a positive response, and in
some cases these stimuli even inhibited the cells. However, as we moved
from one time-frequency extreme to the other, the response to the AM
synthetic songs traced a smooth tuning curve, reflecting a graded
sensitivity to the temporal-spectral precision trade-off inherent in
these synthetic AM songs. The responses were the largest for
time-frequency scales between 4 and 16 msec. The mean response at the
peak time-frequency scale of 4 msec was on average 77 ± 13% (± SEM) of the response to the BOS. Most individual neuronal responses
also showed a response at the peak of their tuning that was high but
still below that of the BOS; 83% of the recording sites had
Z scores below the value of their Z score to the
BOS. A one-tail paired t test comparing the mean
Z score obtained for the AM-4 songs and the mean
Z score for the BOS shows that the mean for AM-4 is clearly
below the mean for BOS (n = 34; t = 4.264; p = 0.0001).
In summary, HVc neurons show a strong response to synthetic songs that
preserve only the amplitude envelopes of a filter bank decomposition of
the original song, but only do so for a range of time-frequency scales
between 4 and 16 msec (250-62 Hz). On visual inspection, it appears
that some of the synthetic songs that gave the best neural responses
were also the ones that were in some sense most like the original
signal. On the other hand, we also found large responses to synthetic
songs that apparently had large amounts of spectral degradation. In the
next section, we will attempt to quantify these observations about how
the different AM songs characterize the acoustical structure in the
song and how this compares with neural responses. It is also true that even the responses for the optimal time-frequency scales were still
below those of the original song, reflecting the fact that HVc
song-selective neurons are sensitive to additional temporal and
spectral structure of the original song that was not reflected in these
AM songs (see Materials and Methods). The nature of the missing
structure and the sensitivity of the neurons to the gradual restoration
of this structure will be addressed in a subsequent section.
Relative preference for temporal cues
The next goal in our analysis was therefore to compare the
time-frequency scale tuning of HVc neurons with the time-frequency scale that would best characterize the acoustical structure in song
obtained in an independent manner. For instance, it is well known that
a given sound is best represented in a spectrogram when this
spectrogram is calculated at a particular time-frequency scale. For
example, on visual inspection, the acoustical structure of the zebra
finch song shown in Figure 2 seems best represented by the spectrogram
calculated with a 16 msec window. Similarly, when we look at the
spectrograms of the synthetic songs generated from amplitude envelopes
of the BOS obtained from a range of time windows such as those shown in
Figure 6, we can visually decide on a time window that seems to be the
best at characterizing the structure present in the original song. In
general, the optimal time window depends on the properties both of the
acoustical signal and of the particular acoustical features that are of
interest. Optimally, we would like to base our criteria for the "best
representation" not necessarily on all the information that could be
extracted from the spectrogram (or the set of amplitude envelopes), but on the aspects of that information that represent the behaviorally relevant bioacoustical structure of zebra finch song. To do so, one
might want to evaluate the quality of AM songs generated at different
time-frequency scales by testing the efficacy of the songs in
eliciting the appropriate natural behaviors.
Short of this, we estimated the time window at which a simple measure
of discrimination based on the spectrogram would give us the most
information and enable us to distinguish songs from different zebra
finches. Our measure of discrimination was based on the
cross-correlation between the amplitude envelopes of the different
songs (i.e., CA). We calculated the
pairwise correlations between 16 zebra finch songs from our colony (120 comparisons) and between the syllables in each of the songs that were
the most similar (n = 2031). The correlations were
calculated for a range of time scales from 1 to 256 msec and are shown
in Figure 9.
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Figure 9.
Cross-correlation between amplitude envelopes
calculated at different time-frequency scales for songs
(Song) and syllables (Syll) from
different birds. Sixteen different songs were used, resulting in 120 pairwise correlation measures for songs and over 2000 pairwise
comparisons for syllables. Low values of cross-correlation indicate
large differences between signals and therefore show the
time-frequency scales that are best at discriminating among zebra
finch songs. The error bars showing 1 SEM are smaller than the size of
the markers.
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The cross-correlation measure shows a tuning with a minimum at 32 msec.
This minimum point corresponds to the time-frequency scale at
which the amplitude envelopes of the two songs are the most different
according to the cross-correlation measure (other measures based on
higher order statistics might give slightly different answers). This
quantitative measure matches our visual estimates of the "best"
spectrograms in Figures 2 and 6. Note that, in contrast to the AM songs
used in the physiology, in this calculation the amplitude envelopes are
not distorted because new synthetic songs were not generated in the
process. Moreover, the amplitude envelopes (or the spectrogram)
obtained from the original decomposition using our overlapping filters
completely characterize the original songs, except for an absolute
phase. The same information about the identity of each song is
therefore present in the amplitude envelopes at any time-frequency
scale but in a different form. At particular (optimal) time-frequency scales, the temporal and spectral structure that is most useful in
distinguishing between songs is encoded in large fluctuations in each
of the envelopes. At other time-frequency scales, the same temporal
and spectral structure can only be recovered by examining the joint
small fluctuations in the envelopes from multiple bands (see Materials
and Methods). This is the effect that we are quantifying with the
measure of cross-correlation between amplitude envelopes. For the same
reason, the noise added to the amplitude envelopes of the AM songs at
those optimal time-frequency scales by randomizing the phase has the
smallest effect in altering the time-frequency structure of the
signal, as illustrated in Figures 2 and 6.
One might expect the time-frequency scale of individual syllables to
be different from that of an entire song, because a large fraction of
the temporal complexity of a full song is attributable to the more or
less precise sequence of syllables and silences. In fact, the curves
for song and syllable shown in Figure 9 peak at around the same point.
The effect of the overall temporal pattern in the entire song is
nevertheless reflected in the relatively larger width of the curve for
discriminations based on the entire song, particularly at finer time
resolutions; there the overall temporal pattern given by the sequence
of syllables still allows one to discriminate across songs. In
contrast, as the time resolution is made finer, all individual
syllables begin to resemble each other, being described by a few
Gaussian-shaped amplitude envelopes.
The time-frequency tuning of the correlation measure can now be
compared with the time-frequency tuning of HVc neurons to the AM songs
shown in Figure 8. The two curves are plotted together in Figure
10 to facilitate the comparison. The
symbols (*, ) in Figures 6-8 and 10 indicate
the time-scales for the peak of the averaged neural responses (*) and
for the peak of the average discrimination based on the
cross-correlation measure (). The symbols are used to
facilitate further the comparison between all of the figures, but note
that the strength of the neural response at the 4 msec peak is not
significantly different from those at 8 and 16 msec. However, it is
clear that the two curves are shifted along the time-frequency axis.
To test whether this shift was significant, we compared the
distribution of peaks in neuronal responses with the distribution of
minima in the cross-correlation values. The distribution of neural
sites with peak responses at the different time scales was as follows:
4, 13, 16, and 8 sites at 2, 4, 8, and 16 msec, respectively
(total = 41 neurons). The distribution of minima in the
cross-correlation was 5, 42, 66, 6, and 1 song pairs at 8, 16, 32, 64, and 128 msec, respectively (total = 120). A Kolmogorov-Smirnov
test (insensitive to the logarithmic scale) shows that these two
distributions are different from each other with high statistical
significance (p < 0.0001). The mean
time-frequency value for neuronal peak is 7.7 msec, whereas the mean
time-frequency value for minimum cross-correlation is 27.8 msec. A
one-tail t test done both with and without a log transform
shows that these two means are statistically different
(p < 0.0001 in both cases). The difference is
striking when one compares the spectrograms for the AM-4 song that
elicited a maximal response in 13 of 41 recording sites with the
spectrogram for the AM-32 song that elicited no peak responses and
small average responses overall (Fig. 6).
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Figure 10.
Comparison of the cross-correlation measure for
song similarity and of the response of HVc neurons as a function of the
time-frequency scale. The data in Figures 8A and
9 are plotted together to facilitate the comparison. Note that the
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Abstract
Introduction
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