Download Analogues of simple and complex cells in rhesus monkey auditory

Analogues of simple and complex cells in rhesus
monkey auditory cortex
Biao Tian, Paweł Kusmierek, and Josef P. Rauschecker1
Department of Neuroscience, Georgetown University Medical Center, Washington, DC 20057-1460
Receptive fields (RFs) of neurons in primary visual cortex have
traditionally been subdivided into two major classes: “simple”
and “complex” cells. Simple cells were originally defined by the
existence of segregated subregions within their RF that respond to
either the on- or offset of a light bar and by spatial summation
within each of these regions, whereas complex cells had ON and
OFF regions that were coextensive in space [Hubel DH, et al. (1962)
J Physiol 160:106–154]. Although other definitions based on
the linearity of response modulation have been proposed later
[Movshon JA, et al. (1978) J Physiol 283:53–77; Skottun BC, et al. (1991)
Vision Res 31(7-8):1079–1086], the segregation of ON and OFF subregions has remained an important criterion for the distinction
between simple and complex cells. Here we report that response
profiles of neurons in primary auditory cortex of monkeys show
a similar distinction: one group of cells has segregated ON and OFF
subregions in frequency space; and another group shows ON and
OFF responses within largely overlapping response profiles. This
observation is intriguing for two reasons: (i) spectrotemporal dissociation in the auditory domain provides a basic neural mechanism for the segregation of sounds, a fundamental prerequisite
for auditory figure-ground discrimination; and (ii) the existence
of similar types of RF organization in visual and auditory cortex
would support the existence of a common canonical processing
algorithm within cortical columns.
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cortical microarchitecture canonical circuit
boundary detection sound segmentation
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| single-unit recording |
n primary visual cortex, Hubel and Wiesel found two major
categories of cells with distinct receptive field (RF) types (1):
Simple cells have discrete subareas of a particular orientation
that respond either to the onset or the offset of a small spot of
light; complex cells, by contrast, respond with mixed ON and
OFF responses throughout their RF. In addition, simple cells
show spatial summation within each of their subregions. In both
auditory and somatosensory cortex, inhibitory surrounds or
sidebands next to excitatory RFs have been described, which
have been compared with the OFF sidebands of ON-center
simple cells (2–5). This analogy, however, misses the point that
visual simple cells are really characterized by the existence of
adjacent excitatory bands that respond with a firing-rate increase
to a light stimulus being turned on or off, rather than by the
existence or absence of inhibitory sidebands. Complex cells, by
contrast, have OFF responses but no inhibitory sidebands. It
would be of great interest to determine whether a similar distinction between segregated and nonsegregated ON and OFF
responses exists in other sensory areas, such as auditory cortex. If
so, this could provide more generalized insight into the microarchitecture and processing algorithms of cortical columns,
a debate that is still ongoing 50 y after Hubel and Wiesel’s initial
discovery (6–9).
To look for the existence of similar RF organization in primary
auditory cortex (A1), we tested excitatory responses, i.e., increases in firing rate, after switching a pure tone (PT) or band-passed
noise (BPN) burst on or off while varying its (center) frequency
in 1/3-octave steps. This resulted in two “response profiles,” or
“frequency-rate curves,” showing firing rate as a function of
www.pnas.org/cgi/doi/10.1073/pnas.1221062110
stimulus frequency: one profile for the ON response and one for
the OFF response. Two-dimensional (2D) space and sound frequency may be considered equivalent in vision and audition, respectively, based on the nature of the peripheral receptor surface
in retina and basilar membrane (10, 11). Therefore, we hypothesized that auditory cortical response profiles might exist with ON
and OFF responses segregated in frequency. Such an organization would result in distinct frequency tuning for ON and OFF
responses in the same neuron. If such neurons exist, they could
play an important role in the detection and enhancement of auditory event boundaries, just as simple-cell orientation detectors
have been assigned a function in visual contour segregation.
Auditory physiology has mostly reported excitatory responses
to the onset of an auditory stimulus, such as a tone burst. OFF
responses have been described and analyzed less frequently (12–
14). The relative lack of OFF responses in the published literature may in part be due to the more common use of anesthetized
animals in the past, in which transient ON responses dominate
and, possibly, to the predominant use of tonal stimuli (15). In the
present study, all recordings were performed in awake rhesus
monkeys that were trained to attend to an occasionally occurring
oddball sound. We recorded single-unit activity from primary
auditory cortex in two awake rhesus monkeys and measured
excitatory responses after the onset and offset of PT and BPN
bursts of varying bandwidth and center frequency. These tests
were meant to provide initial insight into whether a distinction
analogous to simple and complex cells can be made in auditory
cortex, to be followed up with testing of linearity and response
modulation analogous to visual studies in the future.
Results
A total of 149 neurons were collected from the left hemisphere in
the auditory cortex of two awake behaving monkeys. Of these, 144
were responsive to sound and 129 were located in A1. The
remaining 15 cells were located in cortical areas neighboring A1 in
the rostral direction (rostral field, R, and anterolateral field, AL,
as defined on the basis of tonotopic map reversal at the border
between A1 and AL/R). Further analysis was restricted to A1 cells.
Raw data from two neurons are shown in Fig. 1. In the first
example (Fig. 1A), the neuron is stimulated with a BPN burst
(bandwidth: 1/3 octave; center frequency varying in steps of 1/3
octave) at a sound intensity well above threshold (55 dBA, as was
most commonly used). Within its overall tuning range, the cell
responded with an excitatory ON response at frequencies from
3.2 to 6.3 kHz. Between 8 and 12.5 kHz, the ON response was
much reduced, and a firing increase (an excitatory OFF response)
appeared instead when the stimulus was turned off. At 16 kHz,
Author contributions: B.T. and J.P.R. designed research; B.T. and J.P.R. performed research; P.K. contributed new reagents/analytic tools; B.T., P.K., and J.P.R. analyzed data;
and B.T., P.K., and J.P.R. wrote the paper.
The authors declare no conflict of interest.
*This Direct Submission article had a prearranged editor.
1
To whom correspondence should be addressed. E-mail: [email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1221062110/-/DCSupplemental.
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Edited* by Michael P. Stryker, University of California, San Francisco, CA, and approved March 27, 2013 (received for review December 3, 2012)
A
B
Fig. 1. Responses of single neurons in primary auditory cortex (A1) of rhesus monkeys to band-passed noise (BPN) bursts centered at particular frequencies.
(A and B) Peristimulus time histograms (PSTHs) and raster displays for two different A1 neurons. Center frequency of the BPN burst increases in each row from
left to right in steps of 1/3 octave, as displayed above each PSTH. The bandwidth of the BPN burst was also 1/3 octave, and its duration was 250 ms. Two vertical
dashed lines indicate onset and offset times of the BPN burst at t = 0 and t = 250 ms, following a pretrial interval starting at −500 ms. ON and OFF responses
were quantified by measuring peak firing rate (minus baseline rate during the pretrial interval) within a 40-ms sliding window after stimulus onset or offset,
respectively. Statistical significance of the responses was determined with the Wilcoxon Signed Rank test. Spike times were recorded at a resolution of 1 ms
but displayed at a binwidth of 10 ms. Two different response types were noted, and typical examples for each type are displayed here. (A) Type-S (simple-like)
cell responds either to the onset or the offset of the BPN burst depending on center frequency. Only occasionally both significant ON and OFF responses are
seen. (B) Type-C (complex-like) cell regularly responds to both onset and offset of BPN burst (or not at all) regardless of center frequency.
the ON response reappeared with a sizeable peak, whereas the
OFF response was largely gone.
A different type of neuron is shown in Fig. 1B: In this case, the
cell responded to sound bursts with both ON and OFF responses
throughout most of its tuning range. We will refer to the first
type of cell (with discrete ON/OFF-response areas) as “type S”
or “simple-like” and to the second type of cell (with mixed ON/
OFF-response areas) as “type C” or “complex-like.”
A
The response profiles (frequency-rate curves) of a typical
type-S neuron are displayed for different bandwidths in Fig. 2A,
whereas Fig. 2B shows three examples of type-C cells. For type-C
cells, the OFF-response region was usually contained within the
ON region, whereas the response profiles of type-S cells consisted of largely segregated subregions that responded either to
switching the stimulus on or off. It should be noted that, when
stimulated with PTs, the OFF response of type-S cells often
B
Fig. 2. Excitatory response profiles of A1 neurons determined from peak firing rates in response to the onset and offset of a BPN burst with varying frequency. Peak firing rates were extracted from PSTHs as in Fig. 1. Green circles connected by green lines show ON responses. Red squares and lines show OFF
responses. Size of the symbols indicates significance level of the response in Wilcoxon Signed Rank test. (A) Response profiles of a typical type-S (simple-like)
cell. In addition to center frequency, bandwidth of the BPN stimuli was also varied, as indicated within each panel. ON- and OFF-response profiles appear
largely separated into discrete frequency ranges. The effect was consistent for different bandwidths, although OFF responses were often markedly diminished for PT stimuli (Upper Left). (B) Three examples of type-C (complex-like) cells. In all cases, the response profiles of ON and OFF responses are similar or
show significant overlap, although OFF responses are considerably weaker in two of the examples (Upper and Lower).
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Tian et al.
A
C
B
D
Fig. 3. Classification of A1 neurons into type-S and type-C classes. (A) ON/
OFF-area overlap plotted as 100-bin histogram smoothed with a Gaussian
kernel (σ = 5 bins). The histogram demonstrates bimodal distribution of ON/
OFF-area overlap. (B) ON/OFF-area overlap plotted against K–S distance (the
latter multiplied by −1 for presentation) as a 100 × 100 bivariate histogram
smoothed with a 2D kernel (σ = 5 bins). Distinct clusters are apparent in
the density plot. (C) Scatterplot of ON/OFF-area overlap against K–S distance. Each dot represents one neuron. (D) Class assignments resulting from
k-means clustering are shown in black (type S) and gray (type C) in the plot
of ON/OFF-area overlap against K–S distance (compare with C).
Tian et al.
A small number of neurons (n = 11) were tested at more than
one sound level. Ten neurons were tested at two, and one neuron
at three levels. Although response amplitude varied with sound
level (sometimes in a nonmonotonic fashion), as one would expect, ON and OFF responses changed in similar proportions, and
no systematic differences in the relationship of ON- and OFFtuning curves were apparent (see Fig. S5 for two examples).
Cells assigned to the two clusters were analyzed further to test
the hypothesis that they constituted two classes analogous to
simple and complex cells in visual cortex. First, we determined
best frequency (BF) quantitatively from response profiles, separately for ON and OFF responses, and the absolute difference
between BF–ON and BF–OFF was then calculated. Our hypothesis would predict that type-S cells with little ON/OFF
overlap should display a relatively large BF difference, whereas
cells with high ON/OFF overlap values should show similar BFs
for ON and OFF, i.e., a near-zero BF difference. As Fig. 4
demonstrates, most type-S (simple-like) cells indeed showed
relatively large differences between BF–ON and BF–OFF, with a
peak at 11/3 octaves (Fig. 4, Upper). Type-C (complex-like) cells,
by contrast, showed a distribution that peaked at zero and tapered down rather quickly (Fig. 4, Lower). The difference between
the absolute BF difference of type-S and type-C neurons was
significant (P < 0.003, Mann–Whitney test).
Although the response profiles in type-C cells may appear, at
first sight, more plain than those of type-S cells, two additional
measures point to the fact that type-C cells (like complex cells
in the visual cortex) are actually at a hierarchically higher processing level. One of these measures is excitatory tuning range
(ETR) of the cells. Fig. 5 demonstrates that type-C cells were
more broadly tuned than type-S (simple-like) cells. The difference was highly significant (P < 10−8, Mann–Whitney test). This
difference is mainly due to the existence of response profiles with
very broad ETR (>3 octaves), which hardly exist among type-S
neurons. Conversely, neurons with a tuning range of less than 1
octave rarely exist among type-C neurons.
A second measure to determine the relative processing stage is
response latency. Here again, type-C cells on average had longer
latencies than type-S cells (mean values: 67.9 vs. 47.2 ms, P <
0.03, t test; median values: 38 vs. 33 ms, P < 0.15, Mann–Whitney
test). Alternative approaches to cell classification resulted in even
clearer latency differences (SI Results).
A strong argument for the hierarchy of simple and complex
cells in the visual cortex is their laminar distribution, with simple
cells found mostly in layers 4 and 6, and complex cells found
mostly in layers 2, 3, and 5 (17, 18). However, the present
recordings were performed in awake monkeys using singlecontact electrodes and a vertical approach with long electrode
travel distances, rendering determination of laminar distribution
virtually impossible. This will remain an important facet of future
study, possibly involving multicontact linear probes or highresolution structural MRI.
Discussion
Our results demonstrate first of all that OFF responses in neurons of the auditory cortex have generally been underreported
(with some notable exceptions, see refs. 12, 14, and 19–21). Although ON responses in our sample were usually stronger than
OFF responses (P < 0.001, df = 121, paired t test), over 90% of
A1 neurons did show an excitatory OFF response at moderate
sound intensities for at least one frequency and bandwidth. The
more frequent occurrence of OFF responses in our sample may
be due to the use of BPN stimuli in comparison with tones of
a single frequency. As our results demonstrate, summation
occurs within the OFF subregion, which is more likely to occur
with BPN bursts (11). Even bandwidths as narrow as 1/6 of an
octave led to significant summation effects compared with PTs
(Fig. 2A). Equivalently, spatial summation is seen in simple-cell
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became almost negligible. However, when stimulated with BPN
bursts, an OFF response in the high-frequency response area was
clearly apparent, suggesting summation within these bandwidths.
The amount of overlap between ON- and OFF-response
profiles was determined quantitatively in all neurons from the
area under the frequency-rate curve (Fig. 3A). The distribution
of ON/OFF-area overlap was bimodal with a peak near 0.7,
a trough at 0.79, and a second peak near 1. To classify the cells
into different types, we performed a bivariate analysis by combining ON/OFF-area overlap with other measures of similarity
between ON and OFF response profiles (Methods). One combination that proved particularly effective was that of ON/OFF-area
overlap with Kolmogorov–Smirnov (K–S) distance (multiplied
by −1; Methods). Alternative approaches using other pairs of
response profile similarity measures, or using principal components derived from multiple measures, are shown for comparison in SI Results and Figs. S1–S3. There was good agreement
between classifications based on all these approaches (79.8–
88.4%, Table S1).
The 2D distribution of ON/OFF-area overlap and K–S distance revealed two distinct clusters (Fig. 3B): One cluster is
centered at ON/OFF-area overlap values close to 1 and at a K–S
distance around −0.15 and presumably corresponds to type-C
cells. The second cluster at smaller ON/OFF-area overlap values
(peak 0.6–0.7) and a larger K–S distance (around −0.3) is assumed to correspond to type-S cells. This 2D bimodal distribution was formally divided into two classes with unsupervised
k-means clustering (Methods); the resulting classes accurately
matched what was inferred from visual inspection of the distribution (compare Fig. 3 D with C). The number of clusters was
confirmed using an “evaluation function” proposed by Pham
et al. (ref. 16, SI Results, and Fig. S4). With this classification
method, about one half (n = 65) of 129 neurons located in A1
were classified as type-S cells and the other half (n = 64) as typeC cells.
Fig. 4. Comparison of best frequencies (BFs) of OFF vs. ON responses. BF
was determined separately for ON and OFF responses from response profiles
of the type shown in Fig. 2. Distributions of absolute difference between BF–
OFF and BF–ON are plotted in 1/3-octave bins. The distribution of type-S
neurons (Upper) was dominated by moderate values of difference between
BF–ON and BF–OFF, with a peak at 11/3 octaves. Notably, virtually no cells
(n = 2, 3%) had a difference close to zero. The distribution for type-C neurons
(Lower) had its peak at zero, indicating that BF–OFF and BF–ON typically
do not differ by much. In 64% of type-C neurons, BF–OFF and BF–ON were
within ±1 octave of each other, compared with 38% of type-S neurons.
RFs of visual cortex, where stimuli with optimal size lead to
larger responses than small spots or narrow slits of light (1, 22).
In fact, linear spatial summation within the RF has become part
of the definition of simple cells. Use of BPN stimuli in our study
also enhances OFF responses in type-C cells, because preferred
stimuli, as everywhere, evoke higher firing rates in neurons of the
auditory cortex (15, 23). Similarly, edges (roughly equivalent to
BPN stimuli with a bandwidth of 2 octaves or more) evoke strong
responses in visual cortical cells (1).
The fact that auditory cortical neurons can have distinct,
complementary response profiles depending on the response
Fig. 5. Excitatory tuning range of type-S and type-C cells in primary auditory cortex. The distributions of frequency tuning range of excitatory ON
responses in type-S (Upper) and type-C neurons (Lower) are shown in steps
of 1/3 octave. Type-C (complex-like) neurons have a significantly broader
excitatory tuning range than type-S (simple-like) neurons. This difference is
mainly due to the existence of response profiles with a very large range
(more than 3 octaves, n = 32, 50%), which hardly exist among type-S neurons
(n = 3, 5%). Conversely, neurons with a range of 1 octave or less rarely exist
among type-C neurons (n = 5, 8%) and are more common in type-S cells (n =
19, 29%).
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type (ON or OFF) was at first quite unexpected. Inhibitory
sidebands, as described previously, would not necessarily predict
the tuning of excitatory OFF responses. Although different best
frequencies for ON and OFF have been found (24), they have
been interpreted as inconsistencies or gradual phase shifts. The
existence of cells with discrete, nonoverlapping ON and OFF
subregions has not been reported to our knowledge. The finding
may have been missed in other studies because OFF responses
were simply not measured or distinguished, or their frequencytuning range was not directly compared with that of ON responses,
the assumption being that frequency tuning of excitatory ON
and OFF responses would be the same. The latter is largely
what we found in type-C cells. They had overlapping ON/OFF
regions, and both frequency-tuning range and BF were often
similar for ON and OFF responses. Thus, these cells are comparable to complex cells in the visual cortex, in which mixed ON/
OFF responses are also found throughout the RF (1). However,
in another type of auditory cortex neuron, which we termed type-S
cells, response profiles of excitatory ON and OFF responses
were clearly segregated with little overlap. This RF organization
is highly reminiscent of simple cells in the visual cortex, which
also have segregated ON/OFF subregions (1).
The distinction between simple and complex cells in primary
visual cortex has also been confirmed on the basis of the linearity
of extracellular response modulation by drifting sine-wave gratings (22, 25, 26), although other reports have found a more
continuous distribution when activity is recorded intracellularly
(7, 27). Simple cells, which are thought to add their inputs linearly, produce a response modulation ratio (between the amplitude of the first harmonic and the mean of the response, F1/
F0) >1; complex cells, with nonlinear integration, have F1/F0
ratios <1. In auditory cortex, dynamic spectral ripple stimuli,
which are considered the equivalent of drifting sine-wave gratings (28), have been used to search for linear and nonlinear
units (29). Both types of cells were found, but their distribution
was unimodal along a continuum with average F1/F0 ratios near
1 (30). Thus, simple and complex cells in visual cortex as well as
their possible equivalents in audition are not reliably identified as
distinct groups on the basis of response linearity alone, as most
cells seem to incorporate both linear and nonlinear mechanisms.
Combining both approaches in the same study may help to illuminate some of the differences. In particular, presenting alternating ON- and OFF-BPN bursts in the spectral region of the
ON response in simple-like cells might produce discharge modulated at the frequency of alternation up to the highest temporal
frequencies at which they respond. By contrast, the same stimuli
should produce responses in complex-like cells at two times the
frequency of alternation until the frequency becomes too high, at
which point they would switch to a sustained elevation of discharge. Such a result would constitute a very strong parallel to
simple and complex cells in the visual cortex.
Whether type-S cells (simple-like cells) in auditory cortex are
made up of convergent excitatory input from thalamic units or
involve intracortical mechanisms also deserves to be studied in
more detail. According to the “input alignment model” of Hubel
and Wiesel (1), ON and OFF responses of visual cortical units
originate from segregated ON and OFF channels in thalamic
cells, which provide excitatory input to cortical simple cells.
Thus, the ON subregions of simple cells are made up from excitatory convergence of ON-center cells in the lateral geniculate
nucleus (LGN), whereas OFF subregions are made up from
excitatory convergence of OFF-center cells. This model has received strong support from subsequent studies, in which segregated ON and OFF channels were found in the LGN and in the
afferents to visual cortex (31, 32) and activity of LGN cells and
simple cells was recorded simultaneously (33). Other accounts
have emphasized the importance of intracortical mechanisms
for setting up different RF types in the cortex (8, 34), or an
Tian et al.
Methods
Most of the electrophysiological recording techniques used here have been
described in detail (14, 49).
Acoustic Stimulation. Acoustic stimuli were presented in a double-walled
acoustic chamber. They comprised PTs and BPN bursts (duration: 250 ms)
produced digitally with the SIGNAL software (Engineering Design). The
stimulus bandwidths were 0 octaves (PT) and 1/6, 1/3, 1/2, 1, and 2 octaves
(BPN), and (center) frequencies covered 0.5–16 kHz in 1/3-octave steps.
Stimulus amplitude was calibrated and computer-controlled from SIGNAL in
5-dB steps until the response was maximal and well above threshold. Sound
pressure level of the stimuli was 45–75 dBA (as measured at the monkey’s
head measured with a Bruel & Kjaer 2235 Sound Level Meter), which was
well within the linear range of the sound delivery system.
Chronic Techniques. Behavioral training. Recordings were performed in two
awake rhesus monkeys (Macaca mulatta) weighing between 7 and 8 kg. The
animals were involved in a simple auditory discrimination task: namely, to
release a bar only to a specified stimulus (S+) to receive a water reward, and
Tian et al.
to hold the bar to all other (S−) sounds, where no water reward was given.
The S+ sound was a short melody consisting of four PT notes (CEGC with C at
512 Hz on a tempered scale), each of 100-ms duration; PTs and BPNs were
treated as S−. After a training period of several weeks, the monkeys were
able to perform the task with a correct-response rate at >90%, indicating
that they were attending to the stimuli. Monkeys under training and recording were given controlled access to water following National Institutes
of Health guidelines. All studies were approved by the Georgetown University Animal Care and Use Committee.
Implant surgery. When initial training was near completion, the animal’s head
was scanned by MRI at 1.5 T to localize target brain structures. A recording
chamber and a post for holding the head were surgically fastened to the
skull under isoflurane anesthesia based on the MRI scan, followed by performing a small craniotomy in the chamber, though the dura was left intact.
After complete recovery from surgery, the animals’ training was resumed
with the head fixed. This final stage of training again lasted several weeks
before recordings commenced.
Electrophysiological Recording and Analysis. Lacquer-insulated tungsten
microelectrodes (<10 MOhm) were inserted into cortex with a hydraulic
microdrive. Because the dura remained intact, a guide tube was inserted
into predetermined positions of a grid within the recording chamber (Crist
Instruments). Neuronal spike activity was recorded from single neurons on
the supratemporal plane in and around primary auditory cortex (A1), as
determined by functional criteria (tonotopic gradient, frequency tuning,
response latency, etc.). Neurons were sampled at an average distance of
250 μm along the track. In all animals, the left hemisphere was studied.
Recording sessions typically lasted 2–6 h per day, 5–7 d per week.
Action potentials were filtered, detected with a window discriminator, and
time stamps were collected at 1-ms resolution by a personal computer using
the CORTEX program. Raster displays and peristimulus time histograms
(PSTHs) were generated and evaluated in response to various forms of
acoustic stimulation. Response profiles (rate-tuning curves) were determined
for PT and each bandwidth of BPN at a 1/3-octave resolution. Stimulus presentation was repeated 10–50 times in a randomized fashion. A pretrial
interval of 500 ms was used to record spontaneous activity (baseline). For
off-line analysis, Matlab (Mathworks) routines were used. The maximum
number of spikes (minus baseline) within a 40-ms sliding window (shifted in
1-ms steps) was determined from the PSTH and divided by the same time
interval (40 ms) to receive a peak firing rate (FR). This procedure was applied
separately for both ON and OFF periods, i.e., within 250 ms after the onset
and offset of the stimulus, respectively, so peak FRs were obtained for ON
and OFF responses. Latency of ON and OFF responses was determined from
the time position relative to stimulus onset or offset of the sliding window
yielding the peak FR. Some responses (n = 11) were measured at two or
more intensity levels to test for level independence.
Best frequency (BF; for BPN: best center frequency) was determined by
fitting a quadratic function to the response profile at the frequency that
elicited the peak FR and the two neighboring frequencies, one on either side
(14). The location of the maximum of this quadratic function was then defined as the BF of the neuron.
The significance level of the neural response was determined by the
Wilcoxon Signed Rank test for paired data. An ON response was defined as
a significant increase (compared with baseline) in FR within the 40-ms sliding
window as applied during the stimulus-ON period; an OFF response was
defined as a significant increase within the same duration after stimulus
offset. Excitatory tuning range (ETR) was assessed as the number of stimulus
frequencies that elicited a significant ON response.
Classification of Neurons. Neurons were classified into type-S and type-C
classes based on measures of ON- and OFF-response profile similarity. The
principal measure was ON/OFF-area overlap. Before calculating this measure,
any negative values of ON and OFF response profiles resulting from baseline
subtraction were replaced with zeros. Areas under the ON and OFF profiles
were calculated as the sum of FR values for each profile, and the overlap area
was calculated by summing the smaller of the ON and OFF responses (FRs)
over all tested frequencies. ON/OFF-area overlap values were computed
separately for ON and OFF responses by dividing the overlap area by the area
under the respective profile. The larger of the two area overlap values was
taken as the final overlap measure. This step allowed handling cases like the
one shown in Fig. 2B, Upper: here, the overlap value was 0.43 for the ON
profile and 1 for the OFF profile. The latter value was chosen to indicate
complete coverage of one profile by the other.
Another measure of ON/OFF-profile similarity was K–S distance. It was
calculated by replacing negative values of ON- and OFF-response profiles
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interaction of subcortical and intracortical input (35, 36). In the
auditory system, segregated ON and OFF channels also exist at
the thalamic level, in the medial geniculate nucleus (MGN) (37).
On the other hand, the existence of multipeaked frequency
tuning curves in auditory cortex has been taken as evidence for
intracortical inhibitory mechanisms capable of shaping auditory
cortical response profiles (38, 39). In such a scheme, OFF responses could conceivably be generated as “rebound from inhibition” rather than by feedforward mechanisms. To decide
between these two alternatives, the precise alignment of OFF
responses relative to inhibitory regions of the receptive field (and
vice versa) will have to be established.
At the next hierarchical level within visual cortex, complex
cells are thought to originate from a convergence of simple cells
(1). This convergence could lead to greater feature specificity
and has been referred to in the auditory cortex as combinationsensitivity (11, 40). By virtue of this convergence in complex cells
the segregated subregions are lost and a “generalization” of the
stimulus over a larger part of the visual field takes place (25, 41).
Type-C cells in auditory cortex may similarly participate in
forming invariances across stimuli in one category and participate in the coding of complex sounds, such as environmental
sounds, animal communication sounds or (in humans) words in
speech (42). If confirmed, both findings would provide another
link toward the argument that cortical columns are made up of
the same circuitry across different regions and carry out similar
computational operations, but apply them to different input
signals (8, 35, 43). Other comparisons have drawn similar analogies between visual direction and auditory FM selectivity (44)
and between visual size and auditory bandwidth tuning (45).
Simple cells in vision are thought to detect the contours outlining the shape of a visual object, whereas complex cells may
participate in the integration of visual information across space
(46). By analogy, type-S cells in auditory cortex could be involved
in sharpening the contrast between two successive sounds with
different spectra, thus detecting the boundaries between events
occurring in the environment or facilitating the segmentation of
auditory scenes (47). Such mechanisms may have also enabled
the ultimate emergence of feature detectors apt for the segmentation of speech in humans. It is reasonable to assume that
such a process of boundary detection and event segmentation
would occur relatively early in the cortical hierarchy. Indeed,
evidence for perceptual stream segregation at the neural level
has previously been reported for rhesus monkey primary auditory cortex (48, 49). In a subsequent step, temporal integration of
acoustic information is necessary to accomplish the encoding of
sound sequences, e.g., in the form of musical melodies (50, 51) or
auditory “objects” (52–54). Type-C cells in the auditory cortex
may form the beginning of this integration.
with zeros, followed by computing cumulative values of the profiles and
finding the largest difference between them. The procedure is identical to
calculating the d statistics in the two-sample K–S test. Unlike ON/OFF-area
overlap (and most other measures that were tested for comparison purposes; SI Methods) whose values increase with increasing similarity of
ON- and OFF-response profiles, K–S distance measures dissimilarity, so it
decreases with increasing profile similarity. Thus, for consistency of presentation, K–S distance values were multiplied by −1 before plotting or use
in subsequent analyses.
All measures of ON/OFF-profile similarity were calculated separately for
each of six stimulus bandwidths. Of these, the measure obtained with the
bandwidth that elicited the highest FR was selected for further analysis (see
also SI Methods for an alternative approach).
ON/OFF-area overlap values were binned into 100 bins, smoothed with
a Gaussian kernel (σ = 5 bins) and plotted (Fig. 3A). A bimodal distribution with a clear trough was apparent. Eventual classification was
based on a bivariate distribution of ON/OFF-area overlap and K–S
distance (Fig. 3C). The same classification approach was also applied to
other pairs of measures, or to the first two principal components derived
from the full set of seven similarity measures (SI Methods). The bivariate
distribution was displayed as a density plot by constructing a 100 bin ×
100 bin histogram smoothed with a 2D Gaussian kernel (σ = 5 bins, Fig.
3B), which allowed to identify clustering of cells visually. Cells were then
divided into two classes using k-means clustering (Fig. 3D). The number
of clusters was selected based on visual inspection of the 2D density plot,
and confirmed with a formal measure (“evaluation function”) (ref. 16,
SI Methods).
Absolute difference between ON response BF and OFF response BF, excitatory tuning range (ETR), and latency were compared between the
resulting classes using the Mann–Whitney test.
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ACKNOWLEDGMENTS. This work was funded by National Institutes of
Health Grants R01-NS052494 and R01DC003489 (to J.P.R.) and National
Science Foundation Grant PIRE OISE-0730255.
Tian et al.