Laboratory of Auditory Neurophysiology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205
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ABSTRACT |
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Lu, Thomas and Xiaoqin Wang. Temporal Discharge Patterns Evoked by Rapid Sequences of Wide- and Narrowband Clicks in the Primary Auditory Cortex of Cat. J. Neurophysiol. 84: 236-246, 2000. The present study investigated neural responses to rapid, repetitive stimuli in the primary auditory cortex (A1) of cats. We focused on two important issues regarding cortical coding of sequences of stimuli: temporal discharge patterns of A1 neurons as a function of inter-stimulus interval and cortical mechanisms for representing successive stimulus events separated by very short intervals. These issues were studied using wide- and narrowband click trains with inter-click intervals (ICIs) ranging from 3 to 100 ms as a class of representative sequential stimuli. The main findings of this study are 1) A1 units displayed, in response to click train stimuli, three distinct temporal discharge patterns that we classify as regions I, II, and III. At long ICIs nearly all A1 units exhibited typical stimulus-synchronized response patterns (region I) consistent with previously reported observations. At intermediate ICIs, no clear temporal structures were visible in the responses of most A1 units (region II). At short ICIs, temporal discharge patterns are characterized by the presence of either intrinsic oscillations (at ~10 Hz) or a change in discharge rate that was a monotonically decreasing function of ICI (region III). In some A1 units, temporal discharge patterns corresponding to region III were absent. 2) The boundary between regions I and II (synchronization boundary) had a median value of 39.8 ms ICI ([25%, 75%] = [20.4, 58.8] ms ICI; n = 131). The median boundary between regions II and III was estimated at 6.3 ms ([25%, 75%] = [5.2, 9.7] ms ICI; n = 47) for units showing rate changes (rate-change boundary). 3) The boundary values between different regions appeared to be relatively independent of stimulus intensity (at modest sound levels) or the bandwidth of the clicks used. 4) There is a weak correlation between a unit's synchronization boundary and its response latency. Units with shorter latencies appeared to also have smaller boundary values. And 5) based on these findings, we proposed a two-stage model for A1 neurons to represent a wide range of ICIs. In this model, A1 uses a temporal code for explicitly representing long ICIs and a rate code for implicitly representing short ICIs.
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INTRODUCTION |
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Time-varying features of acoustic signals and
their neural representations in the cortex are of special interest to
our understanding of complex sound processing at this level of the
auditory system. Temporal features are fundamental components of
communication sounds such as human speech and animal vocalizations.
Both humans and animals are capable of perceiving very rapidly changing
components in complex sounds. It has long been known that, unlike at
the auditory periphery, neurons in the auditory cortex do not
faithfully follow such rapidly changing stimulus components (see
review, Langner 1992). Cortical neurons have only
limited stimulus-synchronization capacity. It is of great importance to
know what is this limit for neurons in the auditory cortex and,
furthermore, how the auditory cortex represents rapidly changing
stimulus components beyond this limitation. In the present study, we
attempted to shed light on these questions by studying neural responses
in the primary auditory cortex (A1) of cats to repetitive stimulus
events using click train stimuli.
Clicks have been widely used in perceptual studies to
determine the limits of temporal information processing by the auditory system because of their brief duration and easiness to implement. Ronken (1970) found that humans could discriminate
between time-reversed pairs of clicks (2 250-µs clicks of unequal
amplitude) with an inter-click interval as small as 2 ms. A similar
temporal limit can also be demonstrated by gap detection tasks between
two noise bursts (Plomp 1964
). Detection of extremely
fine gaps of 1-20 µs in high-frequency pulse trains has been
reported, although the perceptual task may have relied to some extent
on spectral cues (Pollack 1967
, 1968
). The actual
discrimination performance by humans in these studies is much better
than what would be predicted from neuronal activity observed in the
auditory cortex.
While the preceding studies demonstrate fine discrimination ability,
other studies have looked at the perceptual quality of successive
stimuli. Miller and Taylor (1948) examined the question of continuousness versus discreteness in repeated noise gap stimuli. They found that the rate of interruptions (gaps) in their noise stimuli
needed to be at least 10-15 Hz before the perception of each noise
burst in the stimuli begin to fuse and 40-250 Hz for a pitch to arise.
Hirsch (1959)
showed that perception of two distinct
successive stimuli requires at least 15-20 ms of separation. This was
also seen in magnetoencephalographic recordings (Joliot et al.
1994
). These data provide motivation for our neurophysiological studies.
Although perceptual studies have shown that humans can detect temporal
features on short time scales, existing neurophysiological studies of
the auditory cortex have revealed no direct neural correlates. There
have been a number of studies in the past using clicks or other
temporally modulated stimuli to probe the temporal resolution of A1
neurons (Bieser and Muller-Preuss 1996;
Creutzfeldt et al. 1980
; de Ribaupierre et al.
1972
; Eggermont 1991
, 1992
, 1994
, 1998
, 1999
;
Erulkar et al. 1956
; Gaese and Ostwald
1995
; Goldstein et al. 1959
; Phillips et
al. 1989
; Schreiner and Urbas 1988
;
Steinschneider et al. 1980
; Whitfield and Evans
1965
). Some of the earliest studies using click stimuli to
investigate limiting rate of stimulus-synchronized temporal discharges
in the auditory cortex were done in unanesthetized cats (de
Ribaupierre et al. 1972
; Goldstein et al. 1959
).
de Ribaupierre et al. (1972)
showed that there was a
broad range of stimulus-synchronized responses to click trains in a
cat's auditory cortex, with a median value between 50 and 100 Hz
(repetition frequency of clicks). Creutzfeldt et al.
(1980)
demonstrated large reductions in stimulus-following responses from thalamus to cortex in unanesthetized guinea pigs. Their
cortical responses tended to be transient and entrainment occurred only
when the modulation rate was <20 Hz, even though a correlated thalamic
neuron responded with stimulus-synchronized discharges up to 200 Hz.
Phillips et al. (1989)
recorded the responses of
auditory cortex neurons from anesthetized cat in response to repetitive
tone pulses and found that stimulus-synchronized frequencies reached no
more than 10 Hz. The mechanism of limited stimulus-synchronization was
hypothesized to be the result of a suppression period of 130-135 ms
immediately after each click, followed by a rebound in the neuron's
discharge rate (Eggermont 1992
).
The objectives of most previous studies using click trains or
amplitude-modulated (AM) tones have been to define modulation transfer
functions (MTFs) of A1 neurons (see review by Langner 1992). They focused largely on processing of stimulus intervals in the order of tens and hundreds of milliseconds since MTFs typically peak at 8-10 Hz (equivalent to inter-stimulus intervals of 100-125 ms) in anesthetized cortex. At higher repetition rates (or shorter inter-stimulus intervals), A1 neurons are either not
stimulus-synchronized to the individual stimuli or not responsive
altogether. In contrast, MTFs of auditory-nerve fibers are low-pass in
nature and have cutoff frequencies near ~1 kHz (Joris and Yin
1992
; Palmer 1982
). At the level of the auditory
cortex, few studies have investigated neural responses to rapidly
modulated sequential stimuli in the order of a few milliseconds to tens
of milliseconds. Such data are especially valuable to understand the
discrepancy between the missing temporal information at the cortex
corresponding to rapid successive stimuli and the perceptibility of
such stimuli by humans and animals.
The objectives of the present study are twofold: to quantitatively
define inter-click interval (ICI) boundaries below which A1 neurons are
no longer stimulus-synchronized or no longer responsive to click train
stimuli and to search for evidence that A1 neurons can implicitly
encode shorter ICIs by a rate code instead of an explicit temporal
code, such as suggested in Bieser and Muller-Preuss (1996). In the experiments reported here, we focused on
cortical responses to click trains with ICIs shorter than 100 ms in
populations of A1 units. The results of this study showed that A1 units
exhibit three types of distinct temporal discharge patterns at
different ICIs: stimulus-synchronized, rate-change, and oscillation.
Based on our findings, we suggested a two-stage model for the auditory cortex to represent ICIs across all ranges.
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METHODS |
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Animal preparation
Adult cats with healthy and infection-free ears were used.
Initially, ketamine (30 mg/kg), xylazine (3 mg/kg), and atropine sulfate (0.03 mg/kg) were administered intramuscularly. Pentobarbital sodium (3 mg · kg1 · h
1 iv) or ketamine (6.7 mg/kg im), and a 1:9
volume mix (0.25 ml/kg) of acepromazine (0.25 mg/ml) and atropine (5 mg/ml) were periodically administered as needed throughout an
experiment to maintain an animal at an areflexic level. Core body
temperature of 38°C was maintained and monitored by a heating pad and
rectal thermometer. Local anesthetic (lidocaine) was used before any
soft-tissue incision during surgery. The cats were cannulated through
either the cephalic or the saphenous vein, and a tracheotomy was
performed to allow unobstructed breathing. The cats were then placed
into a head holder. A sagittal incision was made over the left
hemisphere, and the skin was reflected. Underneath, the temporalis and
frontalis muscles were retracted to expose the skull underneath. The
location of the primary auditory cortex was estimated to be 1/3
distance from the frontal ridge to the occipital ridge and the same
distance from the midline. A 1-cm2 area of the
skull was removed, and the dura was reflected to expose the cortical
surface. The cortical surface was immediately covered with silicone
oil. A more precise location of the primary auditory cortex was
identified and later confirmed electrophysiologically (e.g., tuning,
threshold, latency), as a region between the anterior and posterior
ectosylvian sulci.
Acoustic stimuli
The stimuli used were pure tones, wide- and narrowband click
trains with fixed inter-click intervals (ICI). The set of click-trains consisted of ICIs that were systematically varied from 3 to 100 ms.
Typical ICIs were 3, 5, 7.5, 10, 12.5, 15, 20, 30, ... 70, 75, 100
ms. Each click-train lasted for 600 or 1,000 ms. The wideband clicks
were rectangular pulses 0.1 ms in width. This type of stimulus typically elicited responses from middle-layer A1 neurons under anesthetized conditions regardless of their characteristic frequency (CF). The narrowband clicks were sinusoids at a unit's CF whose amplitude was modulated by a Gaussian envelope. The widths of the
Gaussian function were determined by the standard deviation parameter
(a value of 0.2 was used for this study). The duration of these clicks
was ~4 ms, measured where the modulation depths had decayed to <5%
of its maximal depth. A sample waveform is shown in Fig. 6,
inset. In terms of bandwidth, these clicks were ~1 kHz
wide, measured where the Fourier magnitude had decayed to 5% of its
maximal depth. Except at 3 ms ICI, there is no overlapping between
successive clicks. This type of "Gaussian" click has previously been used in psychophysical experiments (Buell and Hafter
1988). The advantage of using narrowband clicks is that a
stimulus' energy is concentrated on a neuron's excitatory receptive
field, and the bandwidths of these clicks are comparable to a neuron's
frequency receptive field. The majority of Q10
(CF/bandwidth at 10dB above threshold) values from Phillips and
Irvine (1981)
ranged from 4 to 16. Schreiner and
Mendelson (1990)
reported average Q10
values of 4.28-5.39. Assuming Q10 of 5 for
neurons with CFs ranging from 1 to 20 kHz, this corresponds to
bandwidths of 0.2-4.0 kHz. Unlike rectangular clicks, Gaussian clicks
are less likely to activate inhibitory sidebands that typically flank
an A1 neuron's excitatory receptive field (Brosch and Schreiner
1997
; Phillips 1988
; Schreiner and Sutter
1992
; Shamma et al. 1993
; Suga
1965
).
All acoustic stimuli were delivered under free-field conditions by a speaker located 3 ft in front of the animal. The speaker (XTS-35, Radio Shack) had a flat (±5 dB) frequency response from 100 Hz to 20 kHz. Stimuli were generated digitally on a computer at full range of a 16-bit D/A converter at 50-kHz sampling rate and attenuated to desired sound pressure level (DA3, PA4, Tucker-Davis Technologies). Gaussian clicks were additionally low-pass filtered at 25 kHz (3382 Filter, Krohn-Hite). Stimuli from each stimulus set were presented in random order. Click stimuli were typically presented at 60 dB SPL for 20 repetitions for all units. In some units, several sound levels were tested for click stimuli. Inter-stimulus intervals were ~1 s in duration.
Recording procedure
All recording experiments were conducted with the animal placed
in an anechoic chamber (Industrial Acoustics Company). The interior of
the chamber was covered by 3-in acoustic absorption foam (Sonex,
Illbruck). Extracellular recordings were made using tungsten
microelectrodes (Microprobe; BAK Instrument) with impedances typically
~1.5-2.5 M at 1 kHz. For each cortical site, the electrode was
placed perpendicularly to the cortical surface and advanced to a depth
of 600-800 µm underneath the cortical surface, corresponding to
cortical layers 3b and 4. The CF and threshold were identified using a
manually controlled oscillator (4300B, Krohn-Hite) and attenuator.
Click stimuli were then presented in random order. Neural activity was
amplified and filtered at 0.3-7 kHz. Action potentials were sorted by
a window discriminator (SD1, Tucker-Davis Technologies) or a
template-matching spike sorter (MSD, Alpha Omega Engineering). In this
report, we conservatively labeled all units as multi-units. The primary
auditory cortex was systematically sampled from dorsal to ventral
regions as conditions permitted.
Data analysis
Data were initially examined with dot raster plots and
poststimulus time histograms. Temporal discharge patterns produced by
click stimuli were classified into three regions: stimulus-synchronized (at long ICIs), nonsynchronized (at intermediate ICIs), and oscillatory or rate-change (at short ICIs). Quantitative measures were applied to
calculate the boundaries separating these regions. For
stimulus-synchronized responses (excluding onset responses to the click
train), a mean vector strength, , was computed from
the following formulae (Goldberg and Brown 1969
):
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RESULTS |
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General observations
Data reported here were based on 175 units recorded from eight cats (mostly left hemisphere A1). The sampled units covered a wide range of CFs (1-20 kHz) and locations along the dorsal-ventral axis. Because no significant differences were found in our analyses between data obtained under pentobarbital-sodium and ketamine anesthesia, the results reported here combined all studied units from both conditions. Several representative examples of responses to click train stimuli are given in Fig. 1. Usually, the onset responses were similar across ICIs. What differentiates responses to click trains with different ICIs were the discharges induced by later clicks. In general, three classes of temporal discharge patterns were observed in response to click train stimuli: stimulus-synchronized discharges, stimulus-induced oscillatory discharges, and nonsynchronized discharges. The majority of units we studied (74%) exhibited significant stimulus-synchronized discharges at long ICIs. Stimulus-synchronized discharges can be seen in areas labeled with "I" in Fig. 1, A-C. The strength of response synchrony differed from unit to unit as did the shortest ICI at which discharges are synchronized to clicks. As the ICI is shortened, the ability of a unit to synchronize to stimuli begins to degrade. Stimulus-synchronized discharges are clearly present at ICIs >25 ms for the unit in Fig. 1A. No significant response synchrony exists at ICIs shorter than 40-50 ms for the units shown in Fig. 1, B and C. A very small number of units showed synchronized discharges to ICIs shorter than 10 ms (data not shown, see DISCUSSION). Quantitative measures of the boundary at which response synchrony disappears will be discussed in later sections.
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Another stimulus-induced discharge pattern is the oscillation evoked by
click trains with short ICIs usually <20 ms (marked as "III" in
Fig. 1B). The frequency of the oscillation was typically at
8-12 Hz, did not change significantly as ICI changed, and was clearly
unrelated to the repetition rate of a click train. The coherence of the
oscillation decays as time progresses after stimulus onset. Such
oscillatory responses in the auditory cortex have been previously
reported (Schreiner and Urbas 1986) and are thought to
result from thalamocortical loops (e.g., Eggermont
1992
). This oscillation was only observed in a subset of
cortical units that we studied (11%).
One type of nonsynchronized firings (after responses to the 1st click) is nearly random discharges at intermediate ICIs as illustrated in Fig. 1, B and C (labeled with "II"). At ~20-30 ms ICI, the response beyond the initial onset is undifferentiated from spontaneous activity. The firing rate was generally observed to be approximately the same as or slightly higher than spontaneous activity. Typically there was a range of ICIs at which such firings were observed. The majority of units exhibited this kind of firing property whether or not they had synchronized activity at longer ICIs.
The other type of nonsynchronized firings was observed at the
shortest ICIs tested (usually <10 ms) where some units responded to
click trains with a brief cluster of discharges whose magnitude decreases as ICI increases (labeled with "III" in Fig.
1D). This increased firing occurred 50-200 ms after the
stimulus onset. We encountered this type of response in ~25% of the
units we studied. What distinguished this rate response from the
response elicited by pure tones was the longer latencyabout 50-100
ms versus 10-15 ms for tones
and the longer duration of the response.
Sometimes this type of rate change was observed in units that also
exhibit stimulus-synchronized firings at long ICIs (Fig.
1C).
Based on properties discussed in the preceding text, we divided the responses to tested ICIs into three regions (from long ICIs to short ICIs): responses to the long ICIs that produced stimulus-synchronized responses is referred to as region I; the responses to medium ICIs that produced nonsynchronized discharges is referred to as region II; and the third response type at short ICIs is referred to as region III(osc) for the oscillation or region III(rate) for the rate-change response. These regions are denoted on the dot raster plots in Fig. 1. An A1 unit may display one, two, or three of these regions in its responses to click stimuli.
Temporal and rate measures
Cortical responses to click train stimuli were quantified by average discharge rate or by Rayleigh statistics if stimulus-induced synchronization was present. Figure 2A shows typical Rayleigh statistic versus ICI curves for units that had well-defined stimulus-synchronizing characteristics. For these units, the Rayleigh statistic generally increases with increasing ICI, sometimes reaching a plateau at long ICIs. Most of the units shown here were not able to synchronize to the very fast temporal modulations present in the stimuli with the shortest ICIs. Except for a small number of units, the Rayleigh statistic of most units decline to an insignificant level at ICIs less than ~20-40 ms.
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Figure 2B shows the normalized total discharge rate plotted against ICI for those units that display rate changes at short ICIs. Discharge rate at each ICI is computed at 50-200 ms and normalized to the rate at the shortest ICI (3 ms). As ICI increases, the discharge rate drops off quickly. These curves suggest that it may be possible for these units to encode short ICIs with rate changes.
Distribution of boundaries between regions of distinct discharge patterns
Based on the measures shown in Fig. 2, boundaries that separate the three discharge regions were calculated for each unit. The synchronization boundary (separating regions I and II) specifies the minimum ICI below which the stimulus-synchronized response is no longer significant. This corresponds to the intuitive notion of temporal features of stimuli explicitly represented by temporal patterns of discharge. The rate-change boundary [separating regions II and III(rate)] specifies the maximum ICI above which a change in the firing rate of the unit is no longer detectable with respect to ICI.
SYNCHRONIZATION BOUNDARY. Distributions of synchronization boundaries based on data from two representative experiments are shown in Fig. 3, A and C. Each unit appearing in the histogram is capable of temporally representing any stimulus ICI greater than its boundary value. The median synchronization boundaries for these two experiments are 39.3 ms (n = 57) and 26.8 ms (n = 49), respectively. The distributions of the synchronization boundary cover a wide range of stimulus ICIs, with 25-75th percentile ranges of 26.0-57.9 ms and 13.4-50.1 ms, respectively. Figure 4A summarizes synchronization boundaries from all experiments. The median synchronization boundary calculated from the pooled data were 39.8 ms (n = 131) with the 25-75th percentile range of 20.4-58.8 ms. Therefore the stimulus rate that at least half of A1 units can follow with time-locked discharge patterns is ~25 Hz.
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RATE-CHANGE BOUNDARY. Figure 3, B and D, shows distributions of rate-change boundaries for two individual experiments. The units included in the histogram have potential to code for those stimulus ICIs that are smaller than their boundary values using their discharge rate. The distribution of the pooled data are shown in Fig. 4B. It has a median value of 6.3 ms (n = 47) and a 25-75th percentile range of 5.2-9.7 ms. Both individual experiments and the pooled data have similar distributions.
Effect of sound intensity
In a subset of units that exhibited strong stimulus-synchronized responses, we also tested the robustness of the synchronization boundary to sound-level changes. Invariance to changes in sound pressure level would further support the notion that this boundary is an intrinsic property to an A1 neuron and is independent of external stimulus. The thresholds of the units to pure tones were typically well below the 60 dB SPL we used for most click stimuli. The sensitivity of the synchronization boundary was examined at 20 dB lower than this standard sound level. The majority of units showed a decrease in firing rate to click stimuli at this lower sound level, 40 dB SPL (data not shown), indicating that the firing rates of these units were not saturated for the SPL range tested. There were no significant changes in the median synchronization boundaries as a function of SPL as shown by data in Fig. 5A. The distributions of synchronization boundaries were not significantly different (P = 0.14, Wilcoxon rank sum) between the two sound levels (40 dB SPL: median = 30.8 ms [25%, 75%] = [21.4, 42.1] ms, n = 20; 60 dB SPL: median = 39.3 ms [25%, 75%] = [26.0, 57.9] ms, n = 57). Figure 5B compares the results of changing SPL on a unit-by-unit basis for those units that were presented with click stimuli at both 40 and 60 dB SPL. Most points fall near the diagonal line indicating that there is a high degree of correlation between the synchronization boundaries at the two sound levels. Therefore moderate changes in sound level did not seem to significantly alter the synchronization boundary.
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The difference in the rate-change boundary distributions was not significant (P = 0.18, Wilcoxon rank sum) between the two sound-levels (40 dB: median = 7.3 ms [25%, 75%] = [6.3, 12.1] ms, n = 5; 60 dB: median = 6.3 ms [25%, 75%] = [5.4, 7.4] ms, n = 21; Fig. 5C). We had only a few units that showed rate changes and for which we were able to record responses at both 40 and 60 dB. For these units, the rate-change boundary was similar at both sound levels, as shown in Fig. 5D. These data suggest that the rate-change boundary was largely independent of moderate changes in sound level.
Comparison between wideband and narrowband clicks
An important factor that can significantly shape responses to
click stimuli is the lateral inhibition that has been known to flank
excitatory receptive fields of most A1 neurons (Brosch and
Schreiner 1997; Phillips 1988
; Schreiner
and Sutter 1992
; Shamma et al. 1993
; Suga
1965
). The data presented in Figs. 1-5 were obtained with
rectangular clicks ("wideband clicks"), a class of signals whose
broad spectrum is likely to activate inhibitory sidebands. Wideband
clicks are commonly used in neurophysiological experiments because they
can easily evoke responses from neurons with all CFs. To help dissect
the contribution of excitatory and inhibitory mechanisms, we applied in
this study a class of narrowband clicks called "Gaussian" clicks.
The band-limited spectral characteristics of this stimulus can
significantly reduce the contribution of lateral inhibition. It was
expected that if lateral inhibition shaped the temporal response, the
use of Gaussian clicks would lessen the effect. However as shown in
Fig. 6A, the distributions of
synchronization boundaries were not significantly different (P = 0.09, Wilcoxon rank sum) between responses to
Gaussian clicks (median = 24.1 ms [25%, 75%] = [8.9, 27.3]
ms, n = 40) and rectangular clicks (median = 26.8 ms [25%, 75%] = [13.4, 50.1] ms, n = 49). Figure
6B further compares the synchronization boundaries between Gaussian and rectangular clicks on a unit-by-unit basis. These data
suggest that lateral inhibitions do not play a significant role on the
stimulus-following ability of A1 units in this preparation.
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We also examined the effect of stimulus bandwidth on the rate-change boundary (Fig. 6, C and D). No significant differences (P = 0.71, Wilcoxon rank sum) were found between the response distribution for the two stimulus types for the rate change boundary (Gaussian: median = 11.1 ms [25%, 75%] = [9.3, 14.1] ms, n = 7; rectangular: median = 10.8 ms [25%, 75%] = [9.2, 13.4] ms, n = 12). When the rate-change boundary is plotted on a unit-by-unit basis for responses to the two click types, most points fall near the diagonal line. This indicates that the rate-change boundary was not significantly affected by stimulus bandwidth.
Correlation to response latency
We calculated minimum latencies to the initial click of click
trains with ICIs >30 ms. In Fig. 7, the
synchronization boundaries are plotted against these values. Although
the correlation is loose, there seems to be a trend in the data with
shorter latency units giving smaller boundary values. If the units in
Fig. 7 are split into two sets according to their latency (11 and
>11 ms), then the synchronization boundaries of these two groups are
significantly different (P
0.01, Wilcoxon rank sum) from
each other. There is a significant shift in synchronization boundary
distribution that depended on latency. The trend is consistent with the
prediction one would make from a simple neuron model, i.e., the shorter
the integration period of a neuron, the shorter its response latency and consequently, the faster its ability to synchronize to successive stimuli.
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DISCUSSION |
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Comparison with previous studies
Most previous studies on cortical responses to successive stimuli
using click trains or amplitude-modulated (AM) sounds have focused on
modulation transfer functions, and in particular, the best modulation
frequency (BMF) (Eggermont 1994, 1998
, 1999
;
Gaese and Ostwald 1995
; Schreiner and Urbas 1986
,
1988
). As our purpose in this study is to investigate the
representation of rapidly modulated stimuli, the repetition rate (or
modulation frequency in a more general sense) of our click stimuli
ranged from 10 Hz (typically the BMF reported in previous studies) to
333 Hz (3-ms ICI). In comparison, modulation frequencies used in most
previous studies ranged from 1 to ~100 Hz. The synchronization
boundary calculated in our study is frequently referred to in the
literature as the limiting rate (of stimulus-synchronized responses).
Previous studies have found limiting rates of A1 neurons to be
typically <20 Hz in anesthetized preparations (Eggermont
1998; Gaese and Ostwald 1995
; Phillips et
al. 1989
; Schreiner and Urbas 1988
), and as high
as >100 Hz in unanesthetized animals (de Ribaupierre et al.
1972
; Goldstein et al. 1959
). There is a wide
variation in the reported limiting rates as well as in the criterion
for defining the limiting rate. Recordings of single units in
unanesthetized animals by de Ribaupierre et al. (1972)
found the median limiting rate of their data to be between 50 and 100 Hz with an exceptional unit whose limiting rate was as high as 1,000 Hz. Creutzfeldt et al. (1980)
recorded simultaneously
from thalamic and cortical neurons of unanesthetized guinea pigs and
computed modulation transfer functions based on the response amplitude
at steady state to tone pulses (trapezoidal modulation) in relation to
the amplitude of the response to the first tone pulse. They reported
that the steady-state amplitude of the response (n = 12) dropped to 50% when the modulation frequency was 25 Hz and to 25%
at ~50 Hz. In contrast, the synchronization boundaries that
Phillips et al. (1989)
found were almost all <10 Hz
using a series of tone pulses at the CF of the neuron and a criterion
that defined the limiting rate as 85% entrainment of spikes to the
click. In the study by Eggermont (1998)
, the limiting
rates (50% of maximum magnitude of BMF) due to clicks were found to be
clustered normally ~9-12 Hz with no significant differences due to
cortical area or single versus multiple unit recordings. The
variability of limiting rates that are reported by these studies is
quite large, ranging from ~8 up to 100 Hz.
The variation in these previous studies was attributed in large part to
the analytical method used (Eggermont 1991), although it
should be pointed out that some of these differences in limiting rates
were likely introduced by different types of repetitive or periodic
stimuli. Using entrainment rather than vector strength in analysis may
result in different values for limiting rates. With a single set of
data, Eggermont (1991)
explored the effects of various
analytical techniques on the limiting rate, which was defined in that
study as 50% of maximum of the particular measure used. The results
varied from 3 Hz using a rate calculation rMTF/click to 24 Hz for a
vector strength (most similar to ours) to >32 Hz for rMTF/train. The
different analytical techniques could account for part of the variation
in the findings of the studies mentioned. Given the wide variation in
reported limiting rates, our data on the synchronization boundary, with
an median limiting rate of ~25 Hz (equivalent to ~40 ms ICI), are
within range of published data using anesthetized preparations.
Occasionally, we encountered a few units that had exceptionally low synchronization boundary (~10 ms). These units did not show evidence of suppression after the initial click that was typically seen in most A1 units we studied, and they usually had particularly short response latency (<10 ms). We suspect that these rare units may be recorded directly from afferent thalamocortical inputs. It is also possible that these highly synchronized responses were due to multi-unit recordings. Our impression is that multi-unit recordings tend to display a higher degree of response synchrony than single-unit recordings.
Because response latency provides an indication of temporal integration
time, a neuron's latency may be correlated with its stimulus-synchronizing ability. In our results, we found that the
synchronization boundary was loosely correlated with response latency.
A similar covariation of latency with BMF has been reported in the
inferior colliculus of the cat (Langner et al. 1987).
The precision of the cortical neurons' responses to transients with respect to the first spike latency has been shown to be only slightly worse than in the auditory nerve (Heil and Irvine 1997
;
Phillips and Hall 1990
). The precision of the first
spike latency at the cortical level appears to be enough to support
entrainment to very short ICIs (Phillips 1989
) but it
does not explain the low entrainment rates in this and previous
studies. It is important to note that the timing information that
Phillips reported was only for onset responses. Other factors besides
spike timing can reduce entrainment (Phillips 1989
). The
minimum response latency in the cortex can be relatively precise, and
it is reasonable to expect that the stimulus-synchronizing ability of
these neurons can also be high since there would be less overlap in the
responses to successive stimuli. The loose correlation between a
unit's synchronization boundary and its first spike latency indicates that temporal integration plays a role in shaping the synchronized response but at the same time, suggests that there must be other factors that limit the unit's stimulus-synchronization ability.
An important difference between our study and previous studies
employing click and AM stimuli is that we investigated cortical responses to click trains with very short ICIs. We identified in this
study rate-change responses that occurred in a subset of A1 units when
stimulated by click trains with ICIs <10-15 ms. This phenomenon has
not been reported in previous studies because our maximum repetition
rate (or equivalently, modulation frequency) was higher than in most
previous studies. For example, maximum repetition rate was 64 Hz (16 ms
ICI) in Eggermont (1998). Although de Ribaupierre
et al. (1972)
did use click train rates up to 3,000 Hz, the
number of cortical units sampled was relatively small in that study. In
the present study, a small, but significant number of units displayed
rate-change property (25%), out of a large sample size of 175 units.
We tested the entire range of ICIs (3-100 ms) in each of these
recorded units.
Finally, it is important to point out that the results of the present study were based on recordings made from middle-layers of A1, which are active under the anesthetics used. Future studies need to further investigate response properties of upper layer neurons to these repetitive stimuli, presumably under unanesthetized conditions.
Effects of stimulus bandwidth and its implications
The fact that changing the bandwidth of our click stimuli
did not significantly alter the synchronization boundary indicates that
lateral inhibition is not a significant factor in shaping the temporal
response to successive stimuli, at least in anesthetized preparations.
This result is supported by observations from previous studies.
de Ribaupierre et al. (1972), in addition to clicks, also tested a train of noise bursts, 1-2 ms duration for each burst.
Although clicks are considered wideband stimuli, they contain regions
in their spectrum where the amplitude is nearly zero. Noise bursts, on
the other hand, have a more evenly distributed spectrum. de
Ribaupierre et al. (1972)
did not note any significant differences in the limiting rate between the noise bursts and the click
trains, although their sample size was small (n = 7). Schreiner and Urbas (1988)
reported that the modulating
envelope shape (rectangular AM vs. sinusoidal AM) had little influence on the temporal-following ability in terms of BMF. The evidence suggests that the excitatory mechanisms, namely temporal integration by
an A1 neuron, may play a primary role in shaping stimulus-synchronized responses. Another mechanism that may have significant influence on
responses to successive stimuli is on-CF inhibition. Responses to
single click (or the initial click in a click train), whether wide- or
narrowband, almost always produced a prolonged period of inhibition
(see examples in Fig. 1), which is similar in nature to the inhibition
evoked by a brief CF tone burst. Dissecting the contribution of on-CF
inhibition from temporal integration will require other experimental
manipulations and needs be investigated in future studies.
Effect of anesthetics
Two types of anesthetics were used in our experiments, ketamine
and pentobarbital sodium. There appeared to be no significant differences in the response boundaries we characterized under both
anesthetic conditions, although spontaneous activity was higher under
ketamine than under pentobarbital-sodium condition. A previous report
indicated that anesthetics have a suppressive effect on the
stimulus-following responses of cortical neurons (Goldstein et
al. 1959). It found that in the same animals click-following responses of evoked cortical potentials were limited to ~100 Hz under
pentobarbital sodium or Dial anesthesia and ~200 Hz when the animals
were unanesthetized. Anesthetics may reduce the efficacy of the
temporal integration mechanism and prolong inhibition, both of which
can contribute to a reduced stimulus-following capacity (Goldstein et al. 1959
). The exact mechanisms on how the
anesthetic operate are yet unknown. The other side effect of
anesthetics may be the 8-to 10-Hz intrinsic oscillations seen in this
and a number of previous studies (Eggermont 1992
;
Schreiner and Urbas 1986
, 1988
). In contrast,
de Ribaupierre et al. (1972)
did not report intrinsic
oscillations in awake cats. The range of repetition rate used in that
study was approximately similar to ours. Despite these limitations of
anesthetics, data obtained in this and previous studies serve as a
basis for future studies under awake and behaving conditions. A
rigorous comparison of cortical responses under both anesthetized and
unanesthetized conditions using the same stimuli would provide
important insight into fundamental mechanisms underlying temporal
processing in the auditory cortex.
Representation of rapid inter-stimulus intervals
The limit on response synchronization found in this study poses a significant question as to how cortical neurons can represent information that is too rapid to be encoded explicitly by temporal discharge patterns. The results of this and other studies show that the majority of cortical neurons cannot follow temporal sequence of stimuli at rates greater than ~20-40 Hz. Thus if the cortex relies exclusively on the temporal discharge patterns of individual neurons to encode complex sounds that are rapidly modulated, it is expected that the fidelity of that representation would be poor. This hypothesis is, however, contradicted by demonstrated perceptual capabilities of humans and animals.
When the temporal discharge patterns of individual cortical neurons no
longer follow the stimulus, an alternative coding scheme must be used.
Our data suggest that the representation of rapid inter-stimulus
intervals may be coded by a rate measure. This is consistent with data
from Bieser and Muller-Preuss (1996) for sinusoidal
amplitude modulated sounds. At very short stimulus ICIs, our results
show that there is a population of neurons that is able to signal ICI
changes by the discharge rates of the constituent neurons. This result
suggests that the representation of rapid repetitive stimuli has been
transformed from an explicit temporal code to an implicit rate code at
A1, possibly through both subcortical and cortical processing. The
resultant representation of the stimulus by discharge rate may be
correlated to the perceptual quality of such sounds as a single event
rather than a series of discrete events (Hirsch 1959
;
Miller and Taylor 1948
).
We considered the possibility that the rate-change responses we observed are due to spectral effects. While spectral effects cannot be completely ruled out, it does not seem to be a significant factor for several reasons. First, the rate-change boundary did not appear to be much affected by the click bandwidth. Since the narrowband Gaussian clicks are less likely (or to a smaller extent) to activate inhibitory sidebands than wideband clicks and both click types produce the same rate-change response, spectral effects due to inhibitory sidebands are minimal. Second, shortening the ICI of the click trains does not change the envelope of the power spectra only the magnitude. Since the envelopes of the power spectra have consistent shapes at each ICI, there are insufficient spectral cues for discrimination between different ICIs. We also observed that changing sound level did not seem to significantly alter the rate-change boundary. This suggests that the rate-change response was also largely independent of the greater power associated with shorter ICIs.
Temporal/rate-code model
Our findings suggest that a plausible model of representing
sequential stimuli would consist of a combined temporal and rate representation. At longer ICIs, consistent with previous studies, sequential stimulus events can be coded explicitly by neuronal temporal
discharge patterns. At shorter inter-stimulus intervals, our data
suggest that the ICI can be represented implicitly by a rate code. If
there is a sufficient overlap in ranges of ICIs represented by these
two coding schemes, the combination should provide an adequate
representation of the entire range of ICIs used in this experiment. We
illustrate this two-stage model in Fig.
8. As ICI is shortened, the percentage of
units able to explicitly represent the stimuli decreases. However, the
percentage of units able to signal ICI changes increases. The model
suggests that as the inter-stimulus interval becomes shorter, the
cortical representation shifts from being predominantly explicit
temporal coding to predominantly implicit rate coding. For the entire
range of ICIs to be adequately represented, there should be a
sufficient overlap between the response boundaries. In our samples,
74% of studied units exhibited stimulus-synchronized responses at some
ICIs. Approximately 25% of these units can signal ICIs down to ~20
ms. At ICI equal to 10 ms, only 10% of units were able to represent
this and other longer ICIs. A total of 25% of all units we studied
showed rate-change responses at short ICIs, and 10% of all units could
signal ICI changes 10 ms. Although the number of units is small,
their existence, as revealed by the present study, supports the notion
of a two-stage processing scheme. It remains to be seen whether the
composition of the response patterns we identified using click stimuli
changes under unanesthetized conditions. Nevertheless the model
presented here is suggestive of the neural correlate of the perceptual
change from discrete events to a sustained sound with increasing the stimulus repetition rate from 40 to 250 Hz (Miller and Taylor 1948
), which is equivalent to 4 to 25-ms ICI.
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ACKNOWLEDGMENTS |
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We thank Dr. I. Bruce, Dr. R. Ramachandran, and D. Barbour for helpful comments on the manuscript and Dr. L. Liang for technical assistance.
This study was supported by Whitaker Foundation Research Grant RG-96-0268, National Institute on Deafness and Other Communication Disorders Grant DC-03180, and a grant from the National Organization for Hearing Research.
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FOOTNOTES |
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Address for reprint requests: X. Wang, Dept. of Biomedical Engineering, Johns Hopkins University School of Medicine, 720 Rutland Ave., Ross 424, Baltimore, MD 21205 (E-mail: xwang{at}bme.jhu.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 14 October 1999; accepted in final form 10 April 2000.
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REFERENCES |
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