Department of Physiology and Waisman Center, University of Wisconsin, Madison, Wisconsin 53705
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ABSTRACT |
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Reale, Richard A. and John F. Brugge. Directional Sensitivity of Neurons in the Primary Auditory (AI) Cortex of the Cat to Successive Sounds Ordered in Time and Space. J. Neurophysiol. 84: 435-450, 2000. Two transient sounds, considered as a conditioner followed by a probe, were delivered successively from the same or different direction in virtual acoustic space (VAS) while recording from single neurons in primary auditory cortex (AI) of cats under general anesthesia. Typically, the response to the probe sound was progressively suppressed as the interval between the two sounds (ISI) was systematically reduced from 400 to 50 ms, and the sound-source directions were within the cell's virtual space receptive field (VSRF). Suppression of the cell's discharge could be accompanied by an increase in response latency. In some neurons, the joint response to two sounds delivered successively was summative or facilitative at ISIs below about 20 ms. These relationships held throughout the VSRF, including those directions on or near the cell's acoustic axis where sounds often elicit the strongest response. The strength of suppression varied systematically with the direction of the probe sound when the ISI was fixed and the conditioning sound arrived from the cell's acoustic axis. Consequently a VSRF defined by the response to the lagging probe sound was progressively reduced in size when ISIs were shortened from 400 to 50 ms. Although the presence of a previous sound reduced the size of the VSRF, for many of these VSRFs a systematic gradient of response latency was maintained. The maintenance of such a gradient may provide a mechanism by which directional acuity remains intact in an acoustic environment containing competing acoustic transients.
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INTRODUCTION |
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Natural environments are filled with transient sounds that arrive at the two ears in unpredictable succession, either directly from primary sources or indirectly as a result of reflections from objects near and far. The central auditory system has devised mechanisms, distributed between and including the lower brain stem and cortex, to extract information about the identity and direction of sounds in this competitive environment when the time interval between the transients ranges from tens of microseconds to hundreds of milliseconds.
Psychophysical localization studies employing successive sounds have
focused attention mainly on the precedence effect, a term that
encompasses a complex set of perceptual phenomena related to sounds
that are separated in time by less than about 50 ms (reviewed by
Blauert 1997; Litovsky et al. 1999
;
Zurek 1987
). These studies have usually been cast in the
context of a listener's ability to localize and identify a sound
source that competes with its early reflections in a reverberant space.
Neurons in the auditory brain stem, midbrain, and cortex are sensitive
to temporal separations of a few tens of milliseconds and thus are considered part of the neural circuitry that underlies these phenomena (Carney and Yin 1990
; Fitzpatrick et al. 1995
,
1997
; Litovsky and Yin 1998a
,b
; Mickey
and Middlebrooks, 2000
; Mickey et al. 1999
;
Yin 1994
). Results of psychophysical studies of spatial resolution (Grantham 1986
; Perrott and Pacheco
1989
), source-echo detection (Stellmack et al.
1997
), localization aftereffects (Thurlow et al.
1965
), and motion discrimination (Saberi and Hafter
1997
) have also made clear, however, that time constants
involved in processing directional information may range from tens to
hundreds of milliseconds, which is considerably longer than that
required for precedence phenomena (see also Blauert
1972
; Grantham and Wightman 1978
; Perrott
1982
). Successive sounds having these longer time intervals
have also been reported to have profound influences on lateralization
judgments (Hari 1995
; Stellmack and Lutfi
1996
; Stellmack et al. 1997
). Hartmann
(1997)
refers to spatial perceptual phenomena with such long
time constants as de-reverberation, a situation found in large concert
halls in which a listener is not fully aware of reverberant sounds even
though their energy may be many times that of the incident signals. All
of these latter observations suggest that even though a listener may
hear two sounds in space separated by time intervals ranging from tens to hundreds of milliseconds, information about each of their directions may not be processed entirely independently.
The fidelity by which a primary auditory cortex
(AI) neuron responds to a probe stimulus depends on when in the past a
conditioning stimulus occurred. Such data have often been presented in
the form of "recovery functions," which plot changes in response
strength to the probe as a function of conditioner-probe interval.
Despite methodological and species differences among studies of this
kind, the range over which suppression has been shown to occur is in the hundreds of milliseconds (Altman et al. 1997;
Borsanyi 1964
; Brosch and Schreiner 1997
;
Calford and Semple 1995
; Fitzpatrick et al.
1997
; Howard et al. 2000
; Lee et al.
1984
; Liegeois-Chauvel 1991
; Perl and
Casby 1954
; Puletti and Celesia 1970
;
Rosenblith 1950
; Rosenblith and Rosenzweig
1951
; Rosenzweig 1951
, 1954
; Rosenzweig and Rosenblith 1950
, 1953
; Serkov and Yanovskii
1970
). Long AI recovery times have usually been interpreted in
terms of forward masking and lateral inhibition (e.g., Brosch
and Schreiner 1997
; Calford and Semple 1995
) or
coding of temporal intervals, such as occurs in speech and
communication sounds (e.g., Creutzfeldt et al. 1980
;
Eggermont 1991
, 1994
; Phillips and Farmer
1990
; Phillips and Hall 1990
; Schreiner
et al. 1997
). These studies were typically carried out under
monaural or binaural earphone listening conditions using sounds that
did not include auditory spatial cues. Considering the persistent
effects that a binaural conditioning sound may have on the spatial
perception of a subsequent sound, we naturally questioned the extent to
which directional sensitivity of an AI neuron to a directional probe,
as reflected in the spatial receptive field, may likewise be affected
by a conditioning stimulus arising from the same or different
direction. We showed previously that AI neurons have broad spatial
receptive fields when their directional selectivity is tested with
single transient sounds that were synthesized in an otherwise anechoic
virtual acoustic space (Brugge et al. 1994
, 1996
). These
receptive fields typically exhibit functional gradients, and an
information theoretic analysis of such spatial receptive fields
obtained from an ensemble of AI neurons produced estimates of
sound-source direction with an accuracy approaching that of a human
listener (Jenison 1998
). We also demonstrated that the
gradient structure of the spatial receptive field that apparently
underlies the ability of AI neuronal assemblies to encode sound-source
direction is highly robust in the sense that it remains intact when
continuous background noise is introduced into the sound field or when
the level of stimulus is changed (Brugge et al. 1998
).
The present report describes the sensitivities of AI cortical neurons
to directional probe signals that are preceded in time by a
conditioning sound originating from the same or different sound-source
direction. In these experiments, we again used an approach based on the
ability to deliver over earphones signals that mimic sounds coming from
any chosen direction in free space (Chen et al. 1995;
Reale et al. 1996
, 1998
; Wu et al. 1997
).
This configuration may be considered the simulation of a directly
propagated sound and its echo. We first describe the time course of
recovery of the response to a probe sound when the conditioning sound
arises from the same or different directions in virtual acoustic space. We then go on to show that spatial receptive fields derived from responses to the probe signal, while altered by a conditioner, retain
directional information.
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METHODS |
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Animal use and care are in compliance with the "Guide for the Care and Use of Laboratory Animals" Publication No. 86-23 (revised 1985) of the National Institutes of Health and with the Animal Welfare Act of 1966 and its amendments of 1970 and 1976. Fifteen adult cats, with no sign of external or middle ear infection, were premedicated with acepromazine (0.2 mg/kg im) and ketamine (20 mg/kg im). A catheter was inserted into the femoral vein for intravenous drug administration and fluid replacement. Atropine sulfate (0.1 mg/kg sc), dexamethasone sodium (0.2 mg/kg iv), and procaine penicillin (300 K units im) were also administered before the animal was deeply anesthetized either with pentobarbital sodium (11 cats) or with halothane (4 cats). Pentobarbital sodium was administered intravenously (40 mg/kg). Halothane (0.8-1.8%) was administered with a carrier-gas mixture of oxygen (33%) and nitrous oxide (66%) through an endotrachael tube using a scavenged Verni-Trol vaporizer system and an anesthesia ventilator. Samples of inspiratory and expiratory air were drawn continuously from within the endotracheal tube and a respiratory gas analyzer (Ohmeda 5250) used to measure pulse rate, oxygen saturation, airway pressure, and concentrations of O2, CO2, N2O, halothane, on a breath-by-breath basis. When halothane was employed, a muscle relaxant (pancuronium bromide, 0.15 mg/kg iv) was administered just before recordings began, if spontaneous respiration was irregular or otherwise compromised. Paralysis could be maintained throughout the experiment by supplemental doses of pancuronium. Muscle relaxation under halothane anesthesia, combined with careful monitoring of inspired and expired gases and vital signs, provided a highly stable long-term recording environment.
When the animal reached a surgical plane of anesthesia, the pinnae and other soft tissue were removed from the head. Hollow earpieces were inserted into the truncated ear canals, sealed in place, and connected to specially designed earphones. The transfer characteristics of the left- and right-ear sound-delivery systems were measured in vivo near the tympanic membrane. A chamber was cemented to the skull over the exposed left auditory cortex, filled with warm silicone oil, and sealed hydraulically with a glass plate on which a Davies-type microdrive was mounted. Action potentials were recorded extracellularly with tungsten-in-glass microelectrodes from single neurons in cortical area AI; their times-of-occurrence were measured with a 1-µs resolution and stored for off-line analyses.
Acoustic stimulus generation
Sound produced by a free-field source and recorded near the
tympanic membrane of the cat is transformed in a direction-dependent manner by the pinna, head, and upper body structures (Musicant et al. 1990; Rice et al. 1992
). The simulation
of these transformed sounds as a function of sound-source direction
constitutes a virtual acoustic space (VAS). A veridical model of VAS
(Chen et al. 1995
; Wu et al. 1997
) was
used to synthesize, in quasi-real time, transient signals for
sound-source directions positioned in a spherical coordinate system
(
180 to +180° azimuth,
36 to +90° elevation) and centered on
the cat's interaural axis. The VAS used was derived from a single cat.
The intensity of any VAS signal was expressed simply as dB attenuation
(dBA) relative to the maximum peak-to-peak amplitude for a particular
sound source for that cat. Directional stimuli were impulsive
transients (6.4-ms duration) that mimicked in their spectrum and time
waveform sounds arriving from a source in free space. This simulated
source generated either a broadband (3-dB corner frequencies at 800 Hz
and 40 kHz) or narrowband (3-dB bandwidth = 2kHz) impulsive
transient waveform that was realized as the impulse response of a
linear-phase finite-impulse response filter. Narrowband sounds had
their center frequency set equal to the characteristic frequency (CF)
of the neuron and were employed for those neurons where broadband
transient sounds did not evoke consistent responses. Digitally
synthesized signals were compensated for the transmission
characteristics of the sound-delivery system before D/A conversion.
Tone burst stimuli delivered monaurally or binaurally were used to
estimate the CF of a neuron and some response area features related to
binaural interactions as described previously (Brugge et al.
1996
). The partial tonotopic map obtained by repeated electrode
penetrations made during the course of an experiment confirmed that the
recordings were obtained from neurons in AI.
Measurements of directional sensitivity
Directional sensitivity was assessed using two stimulus paradigms. In one only a single directional probe sound was presented at a repetition period of 1-2 s to minimize the influence of a prior stimulus. In the other paradigm, a conditioning directional sound preceded the probe by a time interval ranging from a few milliseconds to hundreds of milliseconds.
For each neuron isolated, we first obtained a virtual space receptive
field (VSRF or, simply, spatial receptive field) by delivering, in
random order, a single probe sound from each of hundreds of VAS
directions separated by 4.59° steps in azimuth and elevation, as
described previously (Brugge et al. 1994
, 1996
; Reale et al. 1996
, 1998
). Each VSRF was constructed from
the onset latency of the response to a sound at each of the VAS
directions. Typically, we obtained VSRFs at several different
intensities with one intensity being 20-30 dB above the threshold
determined at the most sensitive region of the spatial receptive field.
Second, we employed three variations of the conditioner-probe paradigm. In all three variations, the conditioner-probe pair was composed of two
directional transient sounds, referred to as a lead and a
lag, that were separated by an inter-sound interval (ISI)
ranging from 0 to 400 ms. The repetition period between the lagging
sound of one pair and the leading sound on the next pair was held
constant at 1 s. In the first variation, responses were recorded
to 40 repetitions of the two-sound stimulus that had both their leading and lagging directions coincident with the cell's acoustic
axis. Technically, the acoustic axis is that direction, for a
single frequency, at which maximum pressure gain is recorded near the tympanum. The cell's acoustic axis was operationally defined by the
frequency corresponding to the CF of the neuron under study. Unless the
ears are perfectly symmetrical, there is a different acoustic axis for
each ear (Musicant et al. 1990
). The VAS constructed for
these experiments employed symmetrical models for the left and right
ears, and we further defined the cell's acoustic axis to be on the
side that yielded the lowest threshold. In the second variation,
responses were recorded to 40 repetitions of a two-sound stimulus that
had the lagging direction coincident with the cell's acoustic axis
combined with a different direction for the leading sound. For both of
these variations, we typically employed ISIs of 0, 2, 4, 6, 8, 10, 20, 30, 40, 50, 100, 125, 200, 300, and 400 ms to derive recovery functions
for both response magnitude (i.e., number of action potentials) and
response latency (i.e., mean first spike latency). In the third
variation of the conditioner-probe paradigm, we obtained VSRFs by
combining a lead sound coincident with the cell's acoustic axis with a
lag sound delivered, in a random order and at a fixed ISI (50-400 ms),
from each of hundreds of VAS directions separated by 4.5
9° steps in
azimuth and elevation. Because of time limitations, spatial receptive
fields derived from responses to these conditioned probe signals were
often restricted to the frontal hemisphere. VSRFs were also modeled
using a spherical approximation technique based on the so-called von
Mises basis function (Jenison et al. 1998
). This
approach allowed us then to study the dependence of maximum-likelihood
estimation performance on the spatial response properties of a
population of AI neurons under conditions in which a single sound was
presented in VAS and when the same sound was conditioned by a previous
sound at different ISIs. The spherical approximation and ideal-observer methods employed in this study have been described in detail elsewhere (Jenison 1998
, 2000
; Jenison et al.
1998
).
Typically, the discharge pattern to a conditioner-probe stimulus at ISIs greater than 20 ms could be unequivocally decomposed into responses attributable to the lead and lag sounds. Thus the spike count and response latency attributable to each sound of the two-sound stimulus were measured in nonoverlapping time windows of 10-20 ms. This situation obtained because under the conditions of these experiments, any effective stimulus evoked from a single AI neuron one spike or a short burst of action potentials whose times of occurrence typically spanned a range of less than 20 ms. Furthermore the majority of cells exhibited little or no spontaneous activity. When, however, the ISI was less than 20 ms, decomposition was not reliable and, thus the measurement window was widened to accommodate the total response to both transients.
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RESULTS |
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Results presented in this paper were obtained from 50 isolated
neurons in 15 cats drawn from a larger series of experiments in which
we studied the VSRFs of several hundred neurons under VAS conditions
(Brugge et al. 1994, 1996
, 1998
). The neurons reported in this paper responded to either broadband or narrowband directional transients and remained in contact with the electrode long enough to be
studied parametrically and quantitatively for the effects of successive
sounds on their spatial receptive field properties. The CFs of these
neurons ranged from 7.3 to 28.6 kHz. Five of the 50 neurons were
obtained under halothane anesthesia. Their recovery times after a
conditioning stimulus were indistinguishable from those obtained under
pentobarbital sodium anesthesia.
Interactions when successive signals arrive from the same direction on the cell's acoustic axis
Figs.
14
illustrate data from two neurons obtained under the conditioner-probe
paradigm (variation 1) where both the leading and lagging sounds
arrived at the two ears from the same direction on the cell's acoustic
axis. The data shown are also representative of cells in our sample
that were capable of responding consistently to both the leading and
the lagging signal. The dot rasters (Figs. 1 and 3) show the patterns
of action potentials generated by 40 repetitions of the two-sound
stimulus for ISIs ranging from 0 to 400 ms. The accompanying recovery
functions (Figs. 2 and 4) plot the change in response strength
(normalized spike count) and response latency attributable to the
leading and lagging sounds for each of the ISIs tested.
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Figure 2 shows that for ISIs of 200 ms or greater the response to the
lagging sound () resembled in both strength (A) and latency (C) the response to the lead (
). However, gradual
reduction in ISI from 100 to 20 ms resulted in a rapid reduction and,
eventually, total elimination of the response component attributable to
the lagging sound. Effects on the discharge to the leading sound were unremarkable. At ISIs that spanned the range from 0 to 20 ms, we did
not attempt to decompose the two-sound stimulus into lead and lag
responses (see METHODS) but rather designated a joint response (Figs. 1 and 2, B and D). Strength of
the joint response remained constant at about 50% of that obtained at
the longest ISIs (300 and 400 ms) studied since at the latter ISIs each
component of the two-sound stimulus produced nearly equivalent
contributions to the joint response. Response latency showed little
systematic change over the ISI range from 0 to 20 ms. These
observations are consistent with assignment of the joint response to
the leading sound with little or no contribution by the lagging sound.
About 70% of neurons studied with the two-sound stimulus that had both the leading and lagging directions coincident with the cell's acoustic
axis exhibited recovery functions of the kind illustrated above. As
shown in all of the dot rasters, the precision in response latency
changed little with changes in ISI despite obvious changes in the
absolute values of first-spike-latency and spike count.
The remaining 30% of cells in the group were distinguished by the
shapes of their recovery functions at ISIs shorter than 20 ms. Data
presented in Figs. 3 and 4 are representative of this group of neurons.
Recovery functions for ISIs between 30 and 400 ms (Fig. 4, A
and C), for both discharge strength and latency, are similar
to those illustrated in Fig. 2. Across the range of shorter ISIs
(Fig. 4, B and D), however, the recovery function for response strength was nonmontonically related to ISI, reaching a
maximum at an interval of 8 ms. The fact that the response latency remained relatively constant under these conditions suggests that the increase in spike count occurred mainly in the later part of
the discharge, a point made clear in the accompanying dot rasters (see
Fig. 3). There, at an ISI of 20 ms, the discharge pattern consisted of
a burst of three action potentials time-locked to the onset of the lead
sound and preceding the onset of the lagging sound (). Reducing the
ISI to 8 ms resulted in the emergence of a fourth
time-locked action potential in the joint response. At still
shorter ISIs this facilitation did not occur, accounting for the
nonmonotonic character of the recovery function. Although this cell
exhibited an increase in firing in this range of delay, a
few neurons exhibited equally strong and systematic
decreases (e.g., Fig. 6F).
Interactions when successive signals arrive from different directions
The question arises as to whether the relationships demonstrated
when leading and lagging signals arrive from the same direction would
be maintained when the leading sound originates from a direction different from that of the lagging sound (see METHODS,
variation 2). Under such conditions, individual sounds in the stimulus
pair may, when presented alone, evoke quite different response
strengths and latencies reflecting their respective positions in the
VSRF (see Brugge et al. 1996). In all 18 cases reported
here, the lagging sound was placed on or near the cell's acoustic
axis, while the leading sound was usually located at a complementary
direction in the opposite hemisphere. Thus the strength of the response to the leading sound alone could be the same or less than that exhibited by the lagging sound when presented alone depending on the
spatial spread of the VSRF at the intensity used. In 6 of the 18 cells,
the leading sound failed to evoke a consistent response when presented
alone. In five neurons, the response was less than 50% of that evoked
by the lagging sound when presented alone, and in seven others the
response exceeded 50%. In all cases the lagging sound on the cell's
acoustic axis always produced a robust time-locked discharge when
presented without the conditioner.
Long recovery functions observed when both signals arrived successively
from a single direction (variation 1) were also obtained when the
signals arrived from different directions under the conditioner-probe paradigm (variation 2). Because of the similarity to results presented in Figs. 1-4, only one example of this will be illustrated here, representing the situation that arose when the leading sound, presented
alone, evoked no spikes (Fig. 5). The
VSRF for this neuron occupied the ipsilateral hemisphere (180 to 0°
azimuth), and the leading sound was positioned in the contralateral
hemisphere (+36° azimuth and +27° elevation). Whereas the neuron
showed no evidence of a response to this leading sound, the presence of the conditioner at this direction resulted in complete suppression of
the response to the probe sound on the cell's acoustic axis for ISIs
between 2 and 50 ms. A partial recovery of response to the two-sound
stimulus occurred at the ISI of 0 ms. Thus for this cell, and for all
others where the lead and lag arrived from different directions and
thus had differing response strengths and latencies, recovery functions
measured in either strength or latency were similar, though not
identical, to those observed when both sounds originated from the same
direction on the cell's acoustic axis.
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Similarities and differences in recovery under the two conditions
described in the preceding text are illustrated in Fig. 6. Here for all neurons in our sample the
strength and latency recovery functions obtained when the two
transients originated at the same direction (A, C, E, and
G) are compared with the situation in which the lagging
sound was on the cell's acoustic axis and leading sound in the
opposite hemisphere (B, D, F, and H). Long recovery times were observed regardless of the direction of the conditioner, the recovery functions were graded over a range of 50-200
ms or longer, with few exceptions complete suppression of the response
to the lagging probe signal inevitability occurred at an ISI of 50 ms,
and when affects were noted at ISIs <10 ms, they were most frequently
of a facilitative or summative nature. Differences in the dynamic range
of suppression were noted, however, as seen in the differences in
slopes of the recovery functions shown in Fig. 6, A and
B. To evaluate these differences, we obtained the ISI at
which each strength recovery function fell by 50% (half-maximal point)
and compared the distribution of this variable under the two
conditioner-probe variations (Fig. 7).
The median half-maximal value was 94.7 ms when both sounds arose from
the cell's acoustic axis but was significantly less than this
(1-sample sign test, = 0.004), 72.5 ms, when the leading sound
arose from some other direction in VAS. Although the distributions are
broad and overlapping, the results indicate that the strength of the
interaction was related to the relative directions of the leading and
lagging sounds within the VSRF.
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Spatial receptive field dependence on a conditioning sound
To study the possible relationships between the relative
directions of the leading and lagging sound on an AI directional response, we employed the third variation of the conditioner-probe paradigm: spatial receptive fields were obtained to probe sounds presented throughout VAS that were conditioned, at fixed ISIs, by a
lead sound on the cell's acoustic axis. This direction for the
conditioner was chosen to ensure that the cell would respond robustly
to the lead sound of every two-sound pair. Figure
8 illustrates results of this experiment
on six AI neurons. VSRFs of these cells, plotted as orthographic
projections, represent the major classes of VSRFs described previously
in AI (Brugge et al. 1994, 1996
). For each neuron
isolated, we first obtained a VSRF by delivering, in a random order,
the single probe sound from each of hundreds of VAS directions 20-30
dB above threshold (control), which was often sufficient for the field
to cover a hemisphere. Each
on the plot indicates the occurrence of
a time-locked response to the stimulus at that direction. VSRFs shown
below each control VSRF were obtained when the same probe sounds were
each conditioned by a lead sound on the cell's acoustic axis. The ISI
(ms) employed is indicated to the top left of each VSRF.
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Conditioned VSRFs were usually not demonstrably different from controls
for ISIs of a few hundred milliseconds or longer. There was in all
cases studied a decrease in the size of the conditioned VSRF when the
ISI fell within the dynamic range of the neuron's recovery function,
as described earlier. The spatial receptive field was reduced in size
at shorter ISIs where the decrease in the locations of effective
directions tended to occur at the periphery of the VSRF. The ISI
required to change the size of the VSRF varied from one neuron to the
next, ranging from more than 200 ms (Fig. 8C) to about 70 ms
(Fig. 8E). For most neurons studied, however, an interval of
75 or 85 ms was sufficient to severely reduce the size of the VSRF
(e.g., Fig. 8, A, B, D, and
F), and for all neurons in our sample at an ISI of 50 ms or
less a response to the probe was hardly in evidence regardless of sound
direction (e.g., Fig. 8, C and E). The size
reduction of the VSRF occurred in such a way that the VSRF remained
relatively circumscribed around the region in VAS that evoked the
strongest response. This was commonly the region of the cell's
acoustic axis (e.g., Fig. 8A), although, as
illustrated in Fig. 8B, this was not always the case. The
ISI-dependent reduction in size of the VSRF illustrated here is
consistent with our observations of the time course of recovery in
response strength made earlier with successive signals arriving from
just one or two directions (Figs. 1-7). This effect of a previous
sound on the VSRF was observed for all of the receptive field classes
described in our earlier work (Brugge et al. 1996).
Previous results indicated that AI neurons spatial receptive fields
exhibit systematic gradients of response latency or response strength
(Brugge et al. 1994, 1996
) that can be quantitatively modeled using spherical basis functions (Jenison et al.
1998
). In the frontal hemisphere, gradients of increasing
latency or decreasing strength can radiate from a central location in
the field, often near the cell's acoustic axis. Our recorded sample of
spatial receptive fields were obtained at a relatively high spatial
resolution with each of hundreds of sample directions tested only one
time. Thus we used response latency rather than spike rate as the
measure of response strength as these two variables are typically
highly correlated (Brugge et al. 1996
; see also Phillips 1989
). Moreover response latency itself can
provide information about sound direction (Jenison
1998
).
Figure 9 illustrates the distribution of
response latency for each of the VSRFs illustrated in Fig. 8. For each
stimulus condition, response latency was restricted to several
milliseconds around a mean latency. The mean latency tended to lengthen
with decreasing ISI. In one or two cases, there were two peaks in the
distribution. In previous studies, we demonstrated that not only was
there a functional gradient in the VSRF but that a gradient was
maintained in the face of changes in signal intensity or in the
presence of continuous background noise (Brugge et al.
1998). The question here is whether systematic VSRF gradients
are also delineated in the presence of a conditioning sound that is
positioned to fall near the most sensitive direction in the cell's
spatial receptive field.
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VSRFs were constructed to show the spatial distribution of response
latency when a single probe was employed (control) and when this probe
was preceded, at a fixed ISI, by a conditioning sound positioned on the
cell's acoustic axis. A set of these VSRFs, the frontal-field portions
of which were illustrated in Fig. 8A, are illustrated here
as quartic-authalic equal area maps (Fig. 10). This map projection displays in
two dimensions the entire auditory space surrounding the animal and
minimizes distortion in the frontal hemisphere. Empirical data
(left) from this representative neuron were used to
construct a VSRF based on first-spike-latency (color coded) for each
ISI. Nonresponsive directions are colored black. The empirical data
were also modeled with spherical basis functions (right) to
provide a mathematical characterization of the systematic latency
organization within the spatial receptive field (Jenison et al.
1998). Using this approach the VSRF can be described by a
continuous function, a feature we have taken advantage of in deriving
azimuth functions from each of the modeled VSRFs.
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From Fig. 10 we see first that under control conditions, when the
stimulus consisted of but a single transient signal, there was an
orderly spatial representation of response latency. The directions in
VAS that were associated with shortest latency tended to cluster, and
this aggregation of shortest latency was surrounded by directions for
which latency became progressively longer. This general pattern was
observed among VSRFs from different AI neurons, although the exact
shapes of iso-latency contours may vary (see also Brugge et al.
1996). Second, an orderly gradient structure remained even when
the size of the VSRF was demonstrably reduced by the presence of a
conditioning sound falling on the most sensitive region of the VSRF and
within a few hundred milliseconds of the probe. As we already showed in
the preceding text (see Fig. 9A), the absolute latency and
the its distribution may shift with changes in ISI, and these shifts
are reflected in comparisons of gradient patterns between the control
VSRF and any conditioned VSRF. Nevertheless the center of the VSRFs
remained at or near that of the control with gradients in response
latency radiating from it. Third, the model used was a reasonable
representation of the empirical VSRF.
Gradients of response latency as observed in derived azimuth functions
Models of the kind shown above in Fig. 10 can reveal in fine
detail the spatial gradients of response latency that are exhibited by
AI spatial receptive fields. From modeled VSRFs, we were able to derive
plots of response latency as a function of stimulus azimuth at a fixed
elevation (azimuth functions). In previous studies
(Brugge et al. 1996, 1998
), we showed that such graphs exhibited steep functional gradients in the receptive field, which we
interpreted as being a key response feature underlying directional acuity (Jenison 1998
). In Fig.
11 we illustrate families of azimuth functions obtained from eight neurons. The fixed elevation chosen for
each passed through the cell's acoustic axis. The parameter is ISI.
Dashed lines represent data derived from modeled control VSRFs where
the single-sound paradigm was employed. Solid curves represent data, at
different ISIs, from modeled VSRFs in the conditioner-probe paradigm.
Figure 11, A-G, illustrates data from seven neurons plotted only for the frontal acoustic hemisphere; the curves in Fig.
11H are continued into the right rear acoustic quadrant.
Figure 11, A and B, was derived from VSRFs shown
in Figs. 8 and 10. In all cases shown, as well as in those not
illustrated here, response latency changed systematically with changes
in stimulus direction along the azimuth. Response latency tended to be
shortest for directions in the right acoustic hemisphere (contralateral
to the cortex under study) and became progressively longer for
directions on either side of this. We note especially that the greatest
changes in latency tended to occur with changes in azimuth direction
across the midline, between about +18 and
18°.
|
Maximum likelihood estimation of direction from ensemble responses
It was shown previously that a theoretical ideal observer
can be derived to estimate sound-source direction from an ensemble of
AI neurons whose empirically measured VSRFs have been described analytically (Jenison 1998, 2000
). In this approach, the
systematic fine structure of first-spike latency in a VSRF was
characterized by approximation with spherical basis functions, and the
unsystematic noise was isolated in the residuals. Examination of the
residuals supported a linear model for the dependence of variance on
first-spike latency. Each neuron in an ensemble of AI neurons was then
reasoned to possess a Gaussian probability density function that
utilized both the spherical basis function model of the spatial
receptive field and the linear model of variance (Jenison et al.
1998
). Thus a single presentation of a sound from a source at a
particular direction elicits from this AI ensemble a corresponding
population of response latency values that is conditioned on only two
parameters: azimuth and elevation. The ideal observer estimates the
azimuth-elevation pair that maximizes the likelihood function, which
under the assumption of independent noise between the cells, is the
joint product of the probability density functions of all the neurons
in the ensemble. Importantly, a second presentation of the same sound
at the same source direction would typically result in a
different set of response latency values and, hence, a slightly
different estimate of the parameter azimuth-elevation pair by the ideal
observer. A lower bound on the variance of these repeated estimates was provided by calculating the Cramer-Rao lower bound (CRLB). The CRLB was
then calculated for different sound-source directions and different
ensemble sizes.
A similar simulation was employed here. A set of VSRFs from one single
neuron were each modeled (e.g., see Fig. 10, right) using
spherical basis functions (Jenison et al. 1998). The set consisted of a control VSRF and conditioned VSRFs (one for each ISI).
The VSRFs of this modeled set were then replicated using the empirical
distribution of receptive field centroids (Fig. 12A) mapped previously by
Brugge et al. (1996)
thereby producing a 65-cell neural
ensemble. The simulation is not expected to be critically dependent on
this number of neurons, since near-asymptotic CRLBs have been estimated
with far fewer neurons (Jenison 1998
). The results of
this simulation are shown in Fig. 12B. There,
represents
results from the control VSRFs and
,
,
,
, and
are from
conditioned VSRFs, at the designated ISI. The ordinate shows the
minimal error (square root of the CRLB) calculated with the ideal
observer analysis; the abscissa marks those directions along the
azimuth (for a fixed elevation) at which the simulation was executed.
For ISIs of a few hundred milliseconds, error functions were within
about two degrees of the control function. At shorter ISIs the outcome
of the simulation showed that was no major degradation (re control) in
the ensemble performance. On the contrary, performance of the
conditioned ensemble was often slightly better than the control at the
shortest ISIs modeled. This outcome is a direct consequence of the
maintenance of internal structure in the contracted spatial receptive
fields and of the assumption of independence among these neurons. In
summary, these simulation results suggest that the directional
precision provided by ensembles of AI neurons would be largely immune
to competing transient sounds within the range of ISIs investigated.
|
Interactions exhibited while listening with one ear
For any sound-source direction in VAS, the signals delivered to
the right and left ears are appropriate in interaural time, intensity,
and spectrum for that particular direction in the free field. All
recovery functions discussed to this point were studied under these
binaural conditions. However, there is evidence at the level of the
midbrain to indicate that the neural interactions observed when sounds
are delivered binaurally in rapid succession may also be observed with
monaural delivery and therefore lacking these binaural cues (Yin
1994). Although this question was not central to the current
study, in four cases, we obtained recovery functions under both
binaural and monaural listening conditions across the full set of ISI
values. In these four cases, monaural listening to the directional
signal was achieved simply by delivering sound only to the ear
contralateral to the cortex under study. Using this monaural
adaptation, two of the neurons were studied with variation 1 (i.e.,
both lead and lag directions on the cell's acoustic axis) and the
other two cells with variation 2 (i.e., only lagging direction on the
cell's acoustic axis). Although the number of such comparisons between
monaural and binaural stimulus conditions are few, the results seemed
unequivocal in that under both conditions the shapes of recovery
functions were similar to those obtained under binaural listening conditions.
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DISCUSSION |
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In the present experiments, we employed transient conditioner and probe sounds that contained temporal and spectral properties appropriate for their respective sound-source directions in the free field. We found that, without exception, the response of an AI neuron to the probe sound was suppressed if preceded in time by a conditioning sound from the same sound source located at the same or different direction within the cell's spatial receptive field. Suppression was a graded function of ISI; it was weakest for ISIs of a few hundred milliseconds and became progressively stronger as the ISI was decreased systematically. For all cells, complete suppression was observed when the interval between conditioner and probe was reduced to about 50 ms. The occurrence of suppression need not depend on the leading sound evoking spike discharges from the cell. However, the dynamic range for suppression was, on the average, shorter when the two sounds arose from two widely spaced directions as compared with the same direction. Long recovery times obtained under monaural listening conditions were within the range of those observed under binaural conditions. For some neurons, there was a response to a conditioner-probe stimulus at ISIs between about 4 and 10 ms that was not equivalent to the response to the conditioner alone.
Influence of a conditioning sound on recovery time
Recovery functions we recorded were similar in many ways
to those obtained by others from field AI of the anesthetized cat based
on evoked-potential responses to nondirectional click pairs delivered monaurally or binaurally (Borsanyi 1964;
Hocherman and Gilat 1981
; Perl and
Casby 1954
; Rosenblith 1950
; Rosenblith
and Rosenzweig 1951
; Rosenzweig 1951
, 1954
;
Rosenzweig and Rosenblith 1950
, 1953
). We did not
observe, however, the cyclic recovery functions reported by
Rosenzweig and Rosenblith (1953)
. The recovery times we
obtained also agree with those reported in forward-masking studies of
single AI neurons using nondirectional monaural or binaural clicks
(Serkov and Yanovskii 1970
), or tone bursts
(Brosch and Schreiner 1997
; Calford and Semple
1995
; Schreiner et al. 1997
). They are also
consistent with the time course of suppression of spontaneous activity
following the transient response to a free-field click
(Eggermont 1991
).
The mechanisms that underlie the relatively long recovery to an
acoustic transient are not limited by the refractory period of the
neuron, for AI cells are quite capable of firing bursts of spikes with
interspike intervals of 1-2 ms, and some can entrain to repetitive
stimulation at repetition rates approaching 800/s (de
Ribaupierre and Goldstein 1972). Recovery time of an AI neuron is also not limited by the precision in spike timing. Phillips' (1989)
studies of AI neurons in cats under pentobarbital sodium anesthesia showed that for brief tone pulses repeated at rates where
spike rate and spike entrainment declined toward zero, the precision of
spike timing was easily sufficient to sustain perfect entrainment for
frequencies of 60-100 Hz. Our results also indicate that whereas first
spike latency typically lengthens during the suppression period, the
timing of the spikes remains quite precise. Phillips
(1989)
suggested that other factors, such as adaptation, must
contribute to the high-frequency cutoff of the response of an AI neuron
to repeated acoustic transients. Eggermont's (1999)
more formal analysis of the low-pass characteristics of AI neurons includes adaptation in a model based on presynaptic facilitation and
depression. Calford and Semple (1995)
hypothesized that
forward masking is the result of inhibitory mechanisms operating at the level of the cortex or at lower levels in the auditory
pathway. Cortical inhibition is further suggested by the earlier
results of Serkov and Yanovskii (1971
, 1972
) from
intracellular recordings from AI neurons in the unanesthetized and
curarized cat. Here it was shown that an acoustic click or a shock to
the auditory thalamic radiations resulted in a long-lasting
hyperpolarization. The presence of a long-lasting hyperpolarization was
also shown not to require a preceding excitatory event, a finding that
is in full agreement with our results. For the great majority of cells recorded under these conditions, the duration of
hyperpolarization was found to be 30-200 ms, with the most
common duration being in the range of 80-100 ms. This time
interval overlaps the dynamic range of suppression we observed using
directional transients.
The recovery time of an AI neuron following the presentation of a
transient sound in space depended on the relative directions from which
the conditioning and probe stimuli arose. On average, the longest
suppression was caused by conditioning stimuli arising from the most
sensitive region of the spatial receptive field, which is typically the
cell's acoustic axis. Here stimulus amplitude is greatest at the CF of
the neuron under study. Hence, this area is often a spatial focus of
highest response magnitude and shortest response latency (Brugge
et al. 1996). These results complement those of Brosch
and Schreiner (1997)
, which showed that the recovery time of an
AI neuron following a tone burst depended on the relative positions of
the conditioning and probe stimuli in the neuron's frequency-intensity
response area; the longest suppression occurred when the conditioner
(masker) was at the neuron's CF. Our results are also in accord with
the earlier findings that recovery of responsiveness of AI to a probe
click, as measured by changes in membrane potential (Serkov and
Yanovskii 1971
, 1972
) and the magnitude of the evoked potential
(Rosenzweig 1954
; Rosenzweig and Rosenblith
1953
), depends on the intensity of a conditioning click and
hence on the amplitude of the response evoked by that signal. These
previous findings in cortical area AI of a relatively long recovery
period after the presentation of nondirectional clicks, tones, and
noise or electrical stimulation of geniculocortical radiations, coupled
with our new data showing very similar recovery times in response to
directional transients, lead us to conclude that the same neural
circuits in AI may be engaged in forward masking, in processing
temporal sequences, and in integrating directional sounds in
reverberant spaces.
The question naturally arises concerning the possible effects of
anesthesia on the AI recovery process and hence on the recovery time.
In cats anesthetized with sodium pentobarbital (Borsanyi 1964; Etholm et al. 1976
) or with chloralose
(Borsanyi 1964
), the recovery time of the averaged
click-evoked potential recorded in AI was found to be lengthened by
tens of milliseconds as compared with the awake animal. Aitkin
and Dunlop (1968
, 1969
) and Etholm et al. (1976)
found a similar anesthesia effect on evoked potentials recorded in the
medial geniculate body of the cat. They noted a comparatively smaller
effect in the inferior colliculus (IC); a finding confirmed in single
unit studies in the rabbit IC by Fitzpatrick et al.
(1995)
. Ketamine and pentobarbital sodium were shown to result
in equivalent estimates of recovery times (Brosch and Schreiner
1997
; Calford and Semple 1995
). Although AI
recovery times may be lengthened by general anesthesia, recovery times have also been reported to range as high as 300-700 ms in the unanesthetized cat (Serkov and Yanovskii 1970
) and
rabbit (Fitzpatrick et al. 1997
, 1999
). In the auditory
cortex of the awake human (Howard et al. 2000
;
Lee et al. 1984
; Liegeois-Chauvel 1991
;
Puletti and Celesia 1970
) and the monkey (Lu et
al. 1999
), the recovery functions derived from click-evoked
responses are very similar to those we obtained from single neurons in
AI of the anesthetized cat. Furthermore the results of intracellular
recordings of AI neurons in the unanesthetized (but paralyzed) cat have
shown that acoustic transients commonly evoke inhibition lasting tens
to hundreds of milliseconds (Serkov and Yanovskii 1971. 1972
). Regardless of the anesthetic state of the animal, AI
recovery times are uniformly longer than those recorded in subcortical
auditory regions, including the MGB (Aitkin and Dunlop 1968
,
1969
; Aitkin et al. 1966
; Etholm et al.
1976
), superior olivary complex (Fitzpatrick et al.
1995
, 1999
), IC (Fitzpatrick et al. 1995
, 1999
;
Litovsky and Yin 1998a
,b
; Yin 1994
),
cochlear nuclei (Fitzpatrick et al. 1999
;
Kaltenbach et al. 1993
; Parham et al.
1998
; Wickesberg 1996
), and auditory nerve
(Fitzpatrick et al. 1999
; Parham et al.
1996
). Creutzfeldt et al. (1980)
demonstrated
directly in the unanesthetized guinea pig that auditory cortical
neurons ceased to follow frequencies above 20 Hz, whereas
thalamocortical neurons projecting on them could follow at frequencies
up to five time higher. Thus cortical limits in the temporal processing
of repetitive transients appear to result from the progressively longer
time constants of inhibitory mechanisms that become evident at
successive stages in the ascending auditory pathway.
Minimal recovery time
Despite the considerable amount of electrophysiological evidence
that characterizes auditory cortex as having long time constants, there
are also data indicating that a certain proportion of AI cells may have
recovery time constants that are relatively short. Serkov and
Yanovskii (1970) reported that in the unanesthetized cat
recovery times of AI evoked potentials could be as short as 3 ms,
although the greatest majority of cells they recorded exhibited recovery times >80 ms, with some as long as 700 ms. Fitzpatrick et al. (1997
, 1999
) also showed that auditory cortical recovery times in the unanesthetized rabbit were typically long, although some
were as short as 2-3 ms. More recently, Mickey and his
colleagues (1999
, 2000
) reported that neurons in fields AI and
AII of the cat cortex exhibit interactions in the response to clicks
arriving from two directions with ISIs of less than 1 ms. We did not
study ISIs within a 1-ms time frame. In certain AI cells, however, we did observe increases or decreases in firing strength associated with
changes in discharge pattern at ISIs between about 4 and 10 ms. A
similar finding has been reported in auditory cortex of the awake
monkey (Lu et al. 1999
). Schreiner and his
colleagues have shown that AI cortex is functionally segregated along a
number of acoustic dimensions (for review, see Schreiner
1998
), and thus it may be that neurons with short time
constants are confined to areas not sampled in our experiments. It is
also possible that such neurons occupy layers of cortex that are
usually relatively silent under the general anesthesia used here.
Thus there appears to be a population of AI neurons sensitive to
short temporal intervals as well as to intervals in the range of tens
to hundreds of milliseconds. It may be that these populations of cells
are engaged in processes associated with the precedence phenomena of
fusion and lag discrimination suppression (Litovsky et al.
1999) and summing localization (Mickey et al.
1999
). A possible role of auditory cortex in precedence
phenomena is also suggested by results from lesion-behavior studies
(Cranford 1982
; Cranford et al. 1971
;
Masterton and Diamond 1964
; Whitfield et al.
1972
, 1978
). Several studies have shown that cats discriminate interaural time differences of a few milliseconds and thus may experience the precedence effect (Cranford 1982
;
Cranford and Oberholtzer 1976
) and that lesions of
auditory cortex affect localization performance that depends on
processing short time intervals (Cranford et al. 1971
;
Masterton and Diamond 1964
; Whitfield et al.
1972
, 1978
). Recently, Mickey and Middlebrooks
(2000)
reported on auditory cortical neurons whose response
properties may help account for such behavior.
Influence of a conditioning sound on spatial receptive field
In the present experiments, when a conditioning sound was positioned near the most sensitive directions in the neuron's spatial receptive field (typically but not always the cell's acoustic axis), successive probe sounds were more likely to be suppressed when their sound-source directions were farther from, rather than nearer to, the direction of the conditioner. Thus the spatial receptive field, when conditioned at ISIs from 50 to 400 ms, usually appears to contract about the direction of the conditioner.
In human psychophysical studies, listeners can easily resolve the
presence of two successive sounds arising within a few tens to hundreds
of milliseconds of each other regardless of their directions in space.
Nevertheless, the leading sound can have a powerful residual influence
on the spatial acuity (Grantham 1986; Perrott and
Pacheco 1989
; Tollin and Henning 1998
) or
perception (Hari 1995
; Stellmack et al.
1997
; Thurlow et al. 1965
) of the lagging sound
in this time interval. These lingering perceptual effects might be
related to the significant changes in absolute response latency and
firing probability that we observed routinely in the spatial receptive
fields of AI cells following a conditioning signal. Previously we
reported that the VSRFs of a considerable proportion of recorded AI
neurons are characterized by spatial gradients of response latency
and/or firing probability (Brugge et al. 1994
, 1996
). An
ideal observer model based on an ensemble of AI neurons predicted that
sufficient information was provided by these functional gradients to
account for auditory spatial acuity of both of cat and human in
anechoic space (Jenison 1998
, 2000
;
Jenison et al. 1997
). In the current study, both firing probability and response latency were altered by the conditioning stimulus, although spatial gradients based on these response measures were still recognized in the spatial receptive field. An ideal observer
analysis of a simulated ensemble of these AI neurons showed that high
spatial acuity was preserved for conditioning ISIs ranging from tens to
hundreds of milliseconds. Functional gradients are also maintained when
the size and shape of the VSRF field was altered by changes in stimulus
intensity (Brugge et al. 1996
, 1998
) or by the presence
continuous background noise (Brugge et al. 1998
). In
other words, the gradient structure of an AI spatial receptive field is
highly robust. It appears, therefore that the VSRF gradients maintained
by AI neurons in the face of these conditioning or competing sounds may
be sufficient to support much of a listener's ability to perceive a
sound in space and judge its direction.
Although lesions of auditory cortex disrupt sound localization
performance based on detection of small interaural time differences, no
study of this kind in experimental animals has taken up the question of
possible affects on those aspects of spatial hearing that involve
long interstimulus time intervals. Lesions of auditory cortex affect
localization performance in a way that suggests that they actually
disrupted the animal's organization of auditory space (Heffner
and Heffner 1990; Wegener 1964
). Location in
space appears to be no longer a part of this animal's perception of the external environment (Masterton and Diamond 1964
),
and therefore to such an animal, a sound source does not have a right
or wrong direction, it simply has no direction (Whitfield
1977
). Thus it may be that primary auditory cortex is part of a
forebrain circuit that plays a role, not only in localizing the source
of a sound per se but in the animal's perception of its external
acoustic environment.
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ACKNOWLEDGMENTS |
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This work was supported by National Institutes of Health Grants DC-00116 and HD-03352.
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FOOTNOTES |
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Address for reprint requests: R. A. Reale, 627 Waisman Ctr., University of Wisconsin, Madison, WI 53711 (E-mail: reale{at}waisman.wisc.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 8 December 1999; accepted in final form 3 April 2000.
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REFERENCES |
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