1Department of Bioengineering and 2Department of Otorhinolaryngology: Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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ABSTRACT |
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Doan, Daryl E. and James C. Saunders. Sensitivity to simulated directional sound motion in the rat primary auditory cortex. This paper examines neuron responses in rat primary auditory cortex (AI) during sound stimulation of the two ears designed to simulate sound motion in the horizontal plane. The simulated sound motion was synthesized from mathematical equations that generated dynamic changes in interaural phase, intensity, and Doppler shifts at the two ears. The simulated sounds were based on moving sources in the right frontal horizontal quadrant. Stimuli consisted of three circumferential segments between 0 and 30°, 30 and 60°, and 60 and 90° and four radial segments at 0, 30, 60, and 90°. The constant velocity portion of each segment was 0.84 m long. The circumferential segments and center of the radial segments were calculated to simulate a distance of 2 m from the head. Each segment had two trajectories that simulated motion in both directions, and each trajectory was presented at two velocities. Young adult rats were anesthetized, the left primary auditory cortex was exposed, and microelectrode recordings were obtained from sound responsive cells in AI. All testing took place at a tonal frequency that most closely approximated the best frequency of the unit at a level 20 dB above the tuning curve threshold. The results were presented on polar plots that emphasized the two directions of simulated motion for each segment rather than the location of sound in space. The trajectory exhibiting a "maximum motion response" could be identified from these plots. "Neuron discharge profiles" within these trajectories were used to demonstrate neuron activity for the two motion directions. Cells were identified that clearly responded to simulated uni- or multidirectional sound motion (39%), that were sensitive to sound location only (19%), or that were sound driven but insensitive to our location or sound motion stimuli (42%). The results demonstrated the capacity of neurons in rat auditory cortex to selectively process dynamic stimulus conditions representing simulated motion on the horizontal plane. Our data further show that some cells were responsive to location along the horizontal plane but not sensitive to motion. Cells sensitive to motion, however, also responded best to the moving sound at a particular location within the trajectory. It would seem that the mechanisms underlying sensitivity to sound location as well as direction of motion converge on the same cell.
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INTRODUCTION |
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Various characteristics of sensory stimuli are
processed within the brain, and perhaps at the evolutionary root of
many of these sensory capabilities is the task of localizing predators, prey, and mates in the external environment (Butler and Hodos 1996; Wagner et al. 1997
). Indeed, nature's
most evolved species have bilateral sensory capability in olfaction,
vision, and hearing. Bilateral activation of the sensory organs in
these systems allows full realization of their capacity to detect and
localize stimulation at a distance.
Much of the auditory system, from the superior olivary complex to the
auditory cortex, appears dedicated to processing the time, intensity,
and frequency cues arriving at the two ears. This information is
essential for localizing sound sources in the spatial environment about
the animal (for reviews, see Brown 1994;
Gourevitch 1987
; Wagner et al. 1997
).
Sensitivity to interaural phase (Spitzer and Semple 1991
,
1993
; Yin and Chan 1990
; Yin and Kuwada
1983
) and amplitude (Kleiser and Schuller 1995
;
Stumpf et al. 1992
) differences have been demonstrated
at various levels of the auditory system, and these cues are essential
for localizing sound on the horizontal plane.
It also is known that neurons in the auditory cortex are sensitive to
sounds arising from specific locations in space (Ahissar et al.
1992; Benson et al. 1981
; Imig et al.
1990
; Middlebrooks and Pettigrew 1981
;
Poirier et al. 1997
; Rajan et al. 1990
).
Although the exact role of the cortex in sound localization remains to be defined, it has been shown that the integrity of primary auditory cortex (AI) is required for normal localization behavior (Neff et al. 1975
). Unilateral auditory cortex lesions produce
localization deficits in the contralateral sound field (Jenkins
and Masterton 1982
; Jenkins and Merzenich 1984
).
The extent of these deficits, however, appears to depend on the
localization task employed and the species studied. Rats, with
bilateral lesions of the auditory cortex for example, show little or no
loss in the ability to localize a sound when they are given a simple
right-left discrimination task (Kelly 1980
; Kelly
and Glazier 1978
; Kelly and Kavanagh 1986
). Cats
with unilateral lesions of the auditory cortex can do this as well but
perform poorly in localization tasks in the contralateral field
(Jenkins and Merzenich 1984
).
The search for signal processing strategies at the auditory cortex
includes the possibility that this brain region analyzes dynamic
characteristics of the acoustic stimulus. One highly dynamic property
of sensory stimulation is motion (Boring 1942). The
sensation of auditory motion emerges when the cues for localization
dynamically interact with each other (Wagner et al.
1997
). There is accumulating evidence that the primary auditory
cortex is capable of encoding information associated with moving sound
sources (Altman 1968
; Altman and Kalmykova
1986
; Sovijarvi and Hyvarinen 1974
). Recent single-unit work with cats and monkeys has begun to detail some of the
response characteristics to sound motion in the mammalian auditory
cortex (Ahissar et al. 1992
; Poirier et al.
1997
; Stumpf et al. 1992
; Wagner et al.
1997
). The present study describes a method for digitally
presenting simulated moving sounds to the animal that offers a high
degree of specification and control and reports responses of cells in
AI of the rat during sound motion stimulation.
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METHODS |
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Sound source trajectories
The simulated moving sound sources used in this study were located in the right frontal horizontal plane as illustrated by the three circumferential (C1, C2, C3) and four radial (R1, R2, R3, R4) segments in Fig. 1A. Within each of the seven segments, sound source motion could occur bidirectionally, giving a total of 14 different motion "trajectories." Additionally, the simulated sound sources could move at two speeds (either 1 or 2 m/s), bringing the total number of unique motion conditions to 28. Positive motion was defined arbitrarily as outward for radial segments and clockwise for circular segments. The sounds representing the simulated moving sources were computer generated and presented binaurally to the animal through earphones attached to hollow earbars. The trajectories all were constrained to the horizontal plane for simplicity, and all sounds were modeled from pure-tone sources. This array of virtual moving stimuli occurred contralateral to the recording site in the left auditory cortex.
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Figure 1A shows that the circular segments each covered a
span of 30° at a radius of 2 m from the center of the animal's
head. Each of the radial segments were spaced at 30° increments in
the horizontal plane and were the same length as the circumferential segments (/3 m), with their midpoint located 2 m from the animal.
Each one of the motion segments was presented in the following way. The source was simulated as a stationary stimulus at the start point of the trajectory for 500 ms, then rapidly accelerated, reaching a constant velocity after covering 10% of the trajectory distance. Constant velocity motion continued until 90% of the trajectory distance was covered. It then decelerated over the remaining 10% to come to rest at the endpoint, where it remained stationary for another 500 ms. Constant velocity motion occurred for 839 ms at 1 m/s, and 419 ms at 2 m/s, and in both cases covered a simulated distance of 0.84 m. The 0.84-m constant velocity trajectory of circumferential motion was equivalent to 24°. The sound in the circular trajectories had velocities of 28.5 and 57°/s, respectively.
Calculation of the motion waveform
Details of the computational procedures used to generate motion
waveforms are beyond the scope of this presentation but in large part
were based on the spherical model presented by Kuhn (1987). The general principles are outlined here. The head was modeled as a sphere for the purposes of this experiment, and the intensity effects of the head shadow were based on this model. Phase
differences were modeled strictly on the direct line distance from the
source to either ear and ignored phase-related effects due to specific
head shape. The simulated distance between the source and head (2 m)
and the small size of the rat's head meant that errors in travel time
due to the exact shape of the head were small and thus not taken into account.
The pure-tone stimulus was modeled in Eq. 1 by expressing
sound pressure (Ps) at the source as a function
of time (ts), where f is the
frequency of the stimulus.
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(1) |
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(2) |
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(3) |
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(4) |
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(5) |
Finally the sound pressure versus time waveform could be reconstructed for each ear from Eqs. 4 and 5 using small increments of ts for any type of sound-source motion as modeled by xs(ts) and ys(ts). These motion equations were configured to represent the varying directions and speeds of motion for the 28 conditions summarized in Fig. 1. The complete set of trajectory waveforms for each of the five frequencies (1.6, 3.2, 6.4, 12.8, and 25.6 kHz), and 28 conditions were saved to computer hard disk (a total of 140 synthesized waveforms).
The fact that we actually synthesized simulated moving sound sources was validated partially by creating a number of trajectories (radial and circumferential) by applying the radius of the human head (8.5 cm) in the equations. When the authors listened to these synthesized waveforms through earphones, a pronounced motion was heard in the horizontal plane of the right frontal quadrant, for radial and circumferential stimuli. When sound motion was resynthesized for the size of the rat head, the authors still could sense motion in the stimuli, but it was much less distinct. Other than the indirect evidence from our results, we have no way of independently assessing if the rat actually "sensed" sound motion. Regardless, the advantages and disadvantages of using these types of sound simulations are dealt with in greater detail in the discussion.
Stimulus generation and calibration
Sound stimuli were presented and analyzed with modular components (Tucker Davis Technologies; Gainesville, FL) under computer control. The modules consisted of an A/D and D/A converter (125-kHz sampling rate) for measuring sound pressure levels and presenting sound waveforms stored on hard disk, anti-aliasing filters (40-kHz high-frequency cutoff), digital attenuators, and an impedance matching and amplifier module. A triggering module provided pulses synchronized to the stimulus presentation.
Two piezoelectric speakers (model KSN1165A, Motorola, Schaumburg, IL) generated the stimuli. These were attached to two 110-mm earbars that had a 2.2-mm hole drilled down the center. Calibrated probe-tube microphones inserted near the tips of the ear bars measured sound pressure levels (SPL). Each probe tube was 1.5 mm in diameter and 20 mm long and was coupled to a 12.5-mm diameter condenser microphone (Brüel and Kjær, model 4134; Nærum, Denmark). All intensity measurements were expressed as dB SPL relative to 20 µPa.
Animal preparation
Long Evans rats of either sex (350-400 g) were anesthetized with a mixture of 70 mg/kg ketamine and 8 mg/kg xylazine. All animal procedures were performed under an approved protocol from the Institutional Animal Care and Use Committee at the University of Pennsylvania. When anesthesia was achieved, the animals were tracheotomized. A small dental drill allowed us to cut an ~0.9-mm hole in the skull over the left temporal bone. This exposed the dura above the auditory cortex. The dura was punctured, and care was exercised to prevent blood from entering the exposed cortical surface. A drop of mineral oil was placed over the hole in the skull to prevent drying and to thermally insulate the underlying cortex.
The rat was placed in a sound-attenuated booth, and the ear bars were inserted into the external auditory canals and secured to a modified rat stereotaxic apparatus. Care was taken to ensure a patent pathway through the hollow ear bar to the tympanic membrane. Body temperature was maintained at 37.5°C, and a heating lamp was positioned adjacent to the exposed cortex to maintain temperature in that region.
Electrode preparation and neural recordings
Glass micropipettes, filled with 3 M KCL (impedance 10-20
M), and a high-impedance differential amplifier, were used to record spike discharges from single units. A tungsten microelectrode (2-5
M
) was lowered into the pia mater and used as the reference electrode. The recording electrode was positioned perpendicular to the
surface of the auditory cortex and advanced slowly in 1-µm steps.
Neural signals were amplified 10,000 times with a band-pass of 0.1-3.0
kHz. The amplified signal was connected to a spike discriminator
adjusted to produce a pulse output only during the peaks of single-unit
action potentials.
Procedure
The animal was prepared as described in the preceding text, and the SPL at each tympanic membrane then was calibrated over the range from 1.1 to 36.2 kHz, and correction factors were calculated at each frequency to achieve a stimulus level of 100 dB SPL. Stimulus intensities <100 dB SPL were obtained by linearly decreasing the input voltage to the speaker by the appropriate amount.
The test protocol for each cell was the same, and after collecting a set of data from one unit, the electrode was lowered further until another was encountered. This continued until cell activity could no longer be detected when the medial limits of the cortex were reached (~1,200-1,400 µm below the surface). After this single penetration, the electrode was withdrawn and the experiment terminated. Spontaneous activity in each cell was monitored repeatedly and used as an index of the health and stability of the cell. Cells with spontaneous activity that deviated from the mean by more than a factor of two with time were removed from the data pool.
TUNING CURVES.
Search stimuli consisted of acoustic noise bursts, clicks, or tone
bursts, and these were varied as the electrode was lowered. When a cell
was encountered (whether obviously responsive to sound or not) tuning
curve testing was initiated. The stimulus was changed to tone bursts
with a 10-ms rise and decay time, a 30-ms duration, and an
interstimulus interval of 300 ms. Eleven frequencies spaced at
half-octave intervals were used for the tuning curve analysis; 1.1, 1.6, 2.2, 3.2, 4.5, 6.4, 9.0, 12.8, 18.1, 25.6, and 36.2 kHz, and these
represented a compromise between efficient testing time and resolution.
Each of the 11 tones could be set to 17 intensities, spaced at 5-dB
intervals between 10 and 90 dB SPL. These 187 frequency and intensity
combinations were presented in random order, five times each.
Individual nerve responses were counted for each tone burst, and the
average spike discharge for the five samples at each
frequency/intensity combination was calculated and displayed as a
spectral response plot (Evans and Nelson 1973;
Kaltenbach and Saunders 1987
; Saunders et al.
1996
). From this plot, the best frequency of the tuning curve
and its threshold were estimated.
MOTION STIMULI. Due to hard-disk space limitations in storing the 28 motion trajectories, only five motion test frequencies were available (1.6, 3.2, 6.4, 12.8, or 25.6 kHz). All motion testing occurred at the frequency closest to the cell's best frequency at a level 20 dB above the threshold as defined by the tuning curve. The motion stimuli were downloaded into the D/A converter, and each of the 28 motion conditions was presented randomly. Neural activity was monitored during each trajectory, and the time of occurrence of a spike within the stimulus was recorded with an event timer and stored on hard disk.
The entire set of trajectories was presented a total of five times to increase the amount of neural activity sampled. A 3.5-s interstimulus delay occurred between each trajectory.Graphic representation of the motion trajectories
After all of the motion conditions were presented, the average spike rate was calculated for each trajectory. Differences between the two velocities were not readily apparent in the data, and so we decided to combine the results from the two speeds. This doubled the amount of neural spike activity available for analyzing each of the motion trajectories. The trajectory that exhibited the maximum number of spike responses during its presentation was noted and referred to as the "maximum motion response."
The resulting 14 averaged discharge rates (representing the 7 trajectories by 2 directions) then were described on a polar coordinate plot. Figure 1B illustrates an example of such a response plot. It is important to note that the coordinate system in this representation is independent of the actual location of the motion segments in the contralateral acoustic field (Fig. 1A). Indeed, we must emphasize that the variable of interest in this plot is the direction of motion for each trajectory. The fact that the individual data points on the plot are interconnected should not be taken literally, and the focus should be on the response levels in the positive and negative directions of each moving segment.
The distance from the center of the polar plot represents the level of discharge activity in spikes/second. The solid line circle toward the center of the plot illustrates the rate of spontaneous activity. The averaged discharge rate at each of the trajectories (the motion response plot) is represented by the dark line and each point is the average of 10 samples (5 replications at each of 2 speeds). The thin lines indicate 1 SE above and below the average. In this example, the maximum motion response occurred at trajectory +R4 and had a response of ~3.8 spikes/s. Spontaneous activity in this example was ~1 spike/s.
The polar plot provided a global indication of the neural activity
associated with each trajectory. A description of activity within a
trajectory was obtained by constructing "neuron discharge profiles." These profiles noted the occurrence of a discharge during
the motion trajectory on a plot of either degrees from the midline (for
circumferential motion) or simulated distance from the head (for radial
motion). The three trajectories C1, C2, and
C3 (see Fig. 1A) were combined to represent the
discharge profile from 0 to 90°. The radial profile represented the
discharge pattern from 1.58 to 2.43 m from the head (recall that
the constant motion segment, which was /3 (1.047) m, was reduced by
the 10% acceleration and deceleration at the beginning and end of the segment). The discharge profiles summed all the spike activity obtained
over the 10 separate samples (5 replications at 2 speeds) of the
trajectory. In those cells with relatively low rates of sound-driven
activity, the five replications clearly improved the detection of
response areas within the trajectory. Finally, the profile was divided
into equal intervals and the number of discharges within each of these
bins was counted and plotted against radial or circumferential distance
in meters or degrees. The discharge profiles for the maximum motion
response were compared for movement in both directions.
Statistical analysis of the motion response
Sound-driven activity at the beginning and end of each trajectory was sampled for 500 ms at spatially fixed locations. The number of discharges elicited by these static stimuli then was compared with that elicited during motion across the same location and helped differentiate cell activity to a moving or stationary stimulus.
We also chose to analyze the maximum motion response of each cell using
the bootstrap statistical techniques described by Efron and
Tibshirani (1993). This method is useful when dealing with
either nonnormal populations or complicated test parameters, both of
which apply to the data in this experiment. The method creates an
expected distribution of neural activity derived from random samplings
of the empirically obtained data at each of the motion trajectories.
When a large pool of empiric data are available to estimate this
theoretical distribution, as in the present case, the method provides
an accurate estimate of the underlying distribution (Efron and
Tibshirani 1993
).
The bootstrap procedure conducted 1,000 "mock trials," and on
each trial, 14 "mock responses" of neural activity were
established. A computer was programmed to randomly select 10 samples
(with replacement) from the pool of 140 spike rates representing the experimentally obtained data for each cell during presentation of the
14 motion trajectories, two speeds, and five replications. The neural
activity in these 10 samples was averaged (mock response 1), and the
value arbitrarily assigned the label "mock trajectory +R1" and stored in disk memory. Another sample then was
selected (mock response 2), and the average spike rate was assigned the label "mock trajectory R1." The process was
repeated assigning the results to successive trajectories until all 14 trajectories had a mock response. At this point the computer determined
the maximum motion response for this first mock trial. This was the trajectory with the largest value of mock neural activity. The value of
mock neural activity on this trial then was entered into a table, which
organized the "mock" maximum motion response with the appropriate
trajectory. This entire process then was repeated 1,000 times. The
probability (P) of the empiric-maximum motion response then
could be found by determining the proportion of mock maximum motion
responses that showed a response of equal or greater value.
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RESULTS |
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General observations
A total of 205 auditory cells were encountered in 37 animals.
About 30-40 min of recording time were required to collect a complete
set of data for each neuron, and although the recordings were generally
stable, it was difficult to maintain isolation on a single cell for
this duration. Complete data sets were obtained from 77 units, and the
results from these are reported here. The level of activity in these
cells during acoustic stimulation was typically twice as great as the
level of spontaneous activity. Approximately 100 additional cells were
encountered that failed to respond or responded poorly to our
repertoire of sound stimuli. These nonresponsive cells in the auditory
cortex were not considered unusual (Clarey et al. 1992).
Cortical responses
Many AI neurons exhibited V-shaped tuning curves from which the
best frequency could be determined easily. In addition, the relationship between stimulus intensity and neuron discharge rate at
the best frequency (the rate-intensity function) yielded monotonic functions in many cells. More complicated responses including nonmonotonic rate-intensity functions and W-shaped tuning curves, similar to those reported by others (Clarey et al.
1992), also were observed. The best-frequency threshold for all
77 cells appears in Fig. 2 along with the
rat behavioral thresholds (Heffner et al. 1994
). The
most sensitive best-frequency thresholds approximated the behavioral
audiogram as would be expected. The large variability in these
thresholds is the result of our testing procedure. We would expect the
thresholds of many cells to be elevated unless the best frequency of
the cell happened to correspond exactly with the test frequency.
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Motion response plots
UNIDIRECTION MOTION-SENSITIVE CELLS.
Motion-sensitive cells were defined as those that responded well to
simulated motion in one or more segments with the response to a
trajectory in one direction much greater than the response in the
opposite direction. This differential response to motion direction
within a segment was the key criterion for determining motion
sensitivity. A "unidirection" motion cell was one with the above
characteristic occurring in only one motion segment. The polar plots of
12 unidirection motion-sensitive cells are shown in Fig.
3. The heavy line in each plot shows the
average discharge rate (in spikes/s) during the constant velocity
portion of the simulated moving sound. The probability that the
measured maximum motion response was equal to the expected maximum mean response (as calculated from the bootstrap method) is shown
above the plot. The probability among the 17 unidirection
cells varied widely but never exceeded 0.25. The compelling aspect of
each of these plots is that the cell responded to motion in only one direction within the same segment. For example, the trajectory having
the maximum motion response was +R4 for cell 1A
(Fig. 3) and represented a response rate that averaged ~4 spikes/s.
This cell thus responded best to radial motion oriented 90° to the right of the midline in an outward direction (see Fig. 1A).
The cell discharge rates for all other trajectories were smaller, including motion along the same pathway in the opposite direction (R4). Spontaneous activity was 1.6 spikes/s. The
P value of 0.003 reflects the fact that only 3 of 1,000 bootstrap trials produced an expected maximum response that exceeded
the response seen for the +R4 trajectory (4 spikes/s).
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MULTIDIRECTION MOTION-SENSITIVE CELLS.
A second class of motion cells was identified, and because they could
be characterized by at least two major peaks in the polar response
plot, they were named multidirection motion cells (Fig.
4). Like the unidirection cells described
in the preceding section, these were deemed motion selective because
they responded in only one direction of motion for each of the segments
showing elevated activity. The multiple directions sensitive to motion on the polar plots were responsive, for example, to one particular direction of radial motion and a second direction of circular motion.
The multidirection motion-sensitive trajectories always were adjacent
to each other in space and an example is cell 52C. The
multiple directions for this cell occurred along the adjacent trajectory +R3 and +C3. A similar type of
response was seen in cell 12C where the two directions
occurred along the adjacent trajectory +C2 and
R2. Also note that the reverse direction
(
R3,
C3 and
C2,
+R2) for these two examples (52C and
12C) showed response levels near spontaneous activity of the
respective cells, and this is similar for the rest of the examples in
Fig. 4.
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OMNIDIRECTION LOCATION-SENSITIVE CELLS. Another class of cell appeared sensitive to sound location without any distinctive preference for motion direction. These cells were classed as omnidirection, and examples appear in Fig. 5. A cell in this category was deemed sensitive to location if the level of heightened neural activity within a given segment was approximately the same for motion in both directions. Cell 47B is a good example of this. The highest discharge rates in this cell occurred in the R4 trajectory for the positive and negative directions. Thus sound coming from the 90° position on the horizontal plane activated this cell independent of its direction of motion. These omnidirection cells also might show large responses to multiple segments, but the level of activity in these additional peaks was approximately the same for both trajectory directions. As a generalization, when multiple segments appeared active, they occurred in adjacent radial or circumferential segments. An example is cell 5A in Fig. 5. Segment C2 shows activity in both directions as does segment R2.
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Distinction between motion and location sensitivity
A careful scrutiny of the polar plots presented in the preceding text reveals an overall consistency with the descriptions we provided. Nevertheless we well recognize that the data in various cells are not as clear-cut as we would hope. The analysis presented in this section, we believe, offers additional and perhaps more convincing evidence for motion sensitivity.
The neuron discharge profiles for circumferential motion in three representative cells is presented in Fig. 6. The three motion profiles for the circumferential segments from 0 to 90° appear at the very top of the figure, and the flat horizontal line represents the constant velocity portion of each segment. The profile for each cell has a top and bottom panel depicting the two directions of motion (arrows). The bottom panel always shows the trajectory having the maximum motion response. The horizontal line with vertical ticks describes the neuron discharges and represents the combined activity for both speeds and five replications. The solid line in each panel describes the number of neuron discharges in successive 4° bins along the 90° path of motion. Neuron activity during the acceleration or deceleration portion of each segment was not considered, and there is a gap in the discharge profile between 27-33° and 57-63° where this occurred. When successive bins were plotted, the results across these boundaries were rendered as a dotted line.
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Figure 6A, bottom, shows a burst of activity in unit
12E between ~18 and 35° for simulated clockwise motion, and at
25° the response was ~10 spikes/bin. The binned data in Fig.
6A, top, reveal that neural activity remained relatively
constant throughout the 90° of counterclockwise motion at a level of
~2.5 spikes/s. The maximum motion response for cell 54A
(Fig. 6B) was found in trajectory C2.
Examination of the discharge profile revealed that this cell responded
most vigorously to counterclockwise motion from ~55 to 35°,
reaching a maximum of 28 spikes/bin ~50°. Motion in the opposite
direction (Fig. 6B, top) was relatively constant at ~6
spikes/bin over the full 90° range. Finally, Fig. 6C
(cell 45A) illustrates a maximum motion response in
trajectory +C3. The discharge rate in this cell increased
from ~8 spikes/bin (between 3 and 27°) to 27 spikes/bin at 52°.
Counterclockwise motion, however, remained fairly constant at ~5
spikes/bin throughout the range of motion.
Figure 7 presents a similar analysis for
motion-sensitive cells responding to radial trajectories, and the
results are again combined for the two velocities and five trials. The
radial trajectories were analyzed by bins ~0.07 m in width. Figure
7A, depicting cell 34E, shows that simulated
motion toward the head, between 2.0 and 1.7 m, had a discharge
rate of ~4.9 spikes/bin. Between 2.4 and 2.1 m the discharge
averaged 1.7 spikes/bin. Simulated motion in the opposite direction
showed activity ~2 spikes/bin throughout the trajectory. Motion in
the positive direction for unit 10B (Fig. 7B)
between 1.9 and 2.1 m was ~7.6 spikes/bin. However, simulated
motion in the opposite direction was only 2 spikes/bin throughout the
trajectory. Finally cell 25B (Fig. 7C) shows
motion selectivity within the R2 trajectory between 2.15 and 1.90 m rising to a level of ~5.5 spikes/bin, whereas
simulated motion in the opposite direction (+R2) produced
relatively constant activity at ~0.9 spikes/bin.
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A concern with the radially sensitive cells in Fig. 7 is their
relationship to the rate intensity function. Many cortical cells show
nonmonotoic growth in discharge rate as stimulus intensity increases
(Clarey et al. 1992). As the simulated radial motion moves the sound toward or away from the head, the stimulus level necessarily changes with distance. It is possible that the selectivity for motion within the radial neuron discharge profiles reflects the
nonmonotonic change in discharge rate as the stimulus level changes.
The change in intensity across the 0.84-m simulated distance of the
trajectory was ~4 dB. Given this small change in stimulus intensity
and the fact that testing was conducted at levels only 20 dB above
threshold, we feel it unlikely that the regions of selectivity within
the radial discharge profiles were due to the nonmonotonic
rate-intensity function.
The discharge profiles in Figs. 6 and 7 were representative of all unidirection cells, and define in more detail the directional preference demonstrated in the polar plots of Fig. 3. These data also show that motion sensitivity occurred in a particular region within the trajectory. Equally interesting was the observation that the width of this region varied among cells.
Recall that each of the segments consisted of five parts, the first and last of which simulated sound at a fixed location in space. The circumferential segments had stationary stimuli located at 0, 30, 60, or 90°. If the maximum motion response of a circumferential trajectory crossed a boundary where these fixed location conditions existed, as in the three examples of Fig. 6 (they were chosen for this characteristic), then a comparison could be made between the activity elicited by a moving or stationary stimulus at the same spatial location.
Such a comparison is presented in Table 1. Cell 45A, for example, had a spontaneous response rate of 12.4 spikes/s. An examination of Fig. 6C, bottom, reveals that this cell had its maximum motion response between trajectories C2 and C3. Table 1 indicates that the response rate, for a moving stimulus, between 55-57° and 63-67° was estimated to be 26.8 spikes/s. When the stationary segments at the 60° location were considered (the average of all C2 and C3 conditions at this location), a response rate of 15.8 spikes/s was seen. This was greater than the spontaneous activity (12.4 spikes/s) but not as great as the activity during motion. If this cell was insensitive to motion, we would have expected the level of neural activity for both the moving and stationary stimuli to be the same. An examination of the other circumferential cells in Table 1 shows that the level of neural activity during motion stimulation was always greater than during stationary stimulation at the same location.
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The stationary locations in space at the beginning and end of
trajectories C1, C2, and C3 also
represent the midpoint of radial trajectories R1,
R2, R3, and R4. The discharge rates
produced by these stationary locations could be compared with the
maximum motion activity in radial discharge profiles if it occurred at the simulated 2-m distance. Four such motion-sensitive radial cells met
this criterion (3 were presented in Fig. 7), and their neural activity
also is summarized in Table 1. Cell 25B, for example,
responded best along trajectory R2 located 30° from the
midline. The spontaneous activity in this cell was 11.4 spikes/s. The
stationary stimuli at 30° for the C1 and C2
trajectories caused the cell to respond at 15.5 spikes/s. The maximum
motion activity, however, between 1.95 and 2.05 m in Fig.
7C was 25.5 spikes/s (assuming an average velocity of 1.5 m/s). The remaining three radial cells in Table 1 can be interpreted in
the same way, and the response to motion was always greater than the
response to a fixed stimulus at the same position in space.
Figure 8A offers another way of examining the distinction between motion and location within the same motion segment. The average discharge rate during the moving and stationary parts of radial segments, for all 12 unidirection motion-sensitive cells, was plotted against simulated distance from the head. A representation of the motion profile appears in the middle of the panel. The large black bar shows the average activity that occurred in the maximum motion radial trajectory. On either side of this bar are two smaller bars representing the average spike rate for the stationary parts of the segment at 1.48 and 2.53 m from the head. A paired samples t-test on the mean spikes/second revealed significantly larger activity during motion than during presentation of the stationary stimulus at 1.48 m (t = 6.05; df = 11; P < 0.01) or 2.53 m (t = 5.69; df = 11; P < 0.01). A similar comparison of neural activity appears in Fig. 8B for the seven omnidirection cells responding best to radial trajectories. The level of spontaneous activity and sound-driven activity during stationary or motion stimulation was higher than that seen in the motion-sensitive cells of Fig. 8A. The most striking aspect in Fig. 8B was the relatively high level of activity elicited by the stationary stimuli at the ends of the trajectory. A t-test for paired samples on the mean response rates during motion and during stimulation at 1.48 m revealed chance differences (t = 1.85; df = 6; P > 0.05). The same was true with the comparison at 2.53 meters (t = 1.89; df = 6; P > 0.05). These results suggest that the distinction between a moving or stationary stimulus could not be identified in the response of these location-sensitive cells. This distinction, however, was identified readily in the responses of the motion-sensitive cells. The results in Fig. 8 support this conclusion for data comparisons within a motion segment, whereas those in Table 1 show the same relationship for comparisons among segments.
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Figure 9, A and B, presents two neuron discharge profiles for omnidirection cells, one with its maximum motion response to a circular trajectory and the other to a radial trajectory. The two panels show motion for the respective segments in both directions. These examples, unlike those in Figs. 6 and 7, show heightened neural activity to stimuli moving in both directions and within the same approximate regions of the trajectory. Generally, the radial cells showed regions of activity over a much wider extent of the trajectory than that seen in the motion-sensitive cells (data not shown).
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Distribution of units among cell types
Table 2 indicates that 30 of our units (39%) were either uni- or multidirection motion-sensitive cells (17 and 13 cells, respectively), whereas 15 appeared to be omnidirection cells with apparent sensitivity to location rather than motion (19%). The remaining 30 cells (42%) showed a predilection for neither motion or location. The occurrence of circumferential and radial cells with their preferred direction also is indicated in Table 2.
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DISCUSSION |
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Preliminary considerations
We are confident that our recordings were from the AI area based
on skull landmarks (Roger and Arnault 1989) and vessel
or fissure landmarks on the cortical surface (Sally and Kelly
1988
). Moreover, the tuning curves, rate-intensity functions,
latencies, and peristimulus time histograms were all consistent with
previously reported behavior of cells in primary auditory cortex
(Aitkin 1990
; Brugge 1982
, 1985
;
Imig et al. 1982
; Newman 1988
).
Additionally, the units exhibited a rostral to caudal tonotopic
organization, and their locations were at the expected position of
primary auditory cortex. Finally the area of our recordings in a number
of preparations was verified histologically. Although we could not
identify the recording site of single neurons, the cortical layers
penetrated by the electrode were clearly identified in 30-µm coronal
sections, and auditory cortex was confirmed using the histological
criteria of Sanides (1972)
.
Before discussing the motion results, several issues should be addressed. One of these concerns the motion waveform synthesis. Although the stimuli used here mimicked many aspects of sound motion, they fell short of reproducing true sound motion. Intensity effects were recreated from a spherical model of the head, which ignored any contributions of the pinna. In addition, time of arrival at each ear was calculated using the direct line distance from the source to the ears and ignored travel time around the head. The slight increase in travel time to the contralateral ear from the curved surface of the skull would add only one or two microseconds to the phase delay between the ears (for sound sources 90° to the head) because of the small head size of the rat. Inclusion of this additional time delay would have minimal impact on the current results.
Another shortcoming of the stimuli was an internalized sensation
of motion, meaning that the sound appeared to be moving within the head
of the listener rather than at a true location in space. This occurred
when the authors listened to the motion segments through headphones.
The production of externalized sound motion over headphones must meet
two criteria. The sound must have broadband spectral content
(Wightman et al. 1987), and the pinna transfer function,
which defines the external ear's effect on the spectral content of
incoming sounds, must be factored into the motion equations (Kuhn 1987
). In addition, the computation time involved
in synthesizing simulated motion waveforms increases by a factor equal
to the number of frequencies in the stimulus and quickly becomes
impractical when broadband stimuli are considered. Moreover, estimates
of the pinna transfer function are not only difficult to obtain but are
unique to each animal and each ear because of inherent variations in
pinna shape and associated outer ear structures. Unfortunately, with
the sound internalized there is no absolute judgement that the sound is
indeed located 2 m from the head. Nevertheless when test sounds
were synthesized at different distances, we could judge them in
comparison as being closer or further away. When the authors listened
to the radial trajectories through headphones they approached or
receded, but it was unclear how far away they were. Despite the issues
raised above, there is every reason to believe that we indeed produced
an array of simulated moving stimuli.
These limitations in the stimuli are offset by advantages. The digital simulation of motion eliminated awkward mechanical devices for moving sounds in space. It provided precise control over direction and speed and simulated the source as a moving point of sound. Moreover changes in the calculations could reposition the source at different simulated distances from the head. Equally important, the equations used to calculate the waveforms can be modified to isolate those stimulus cues (e.g., phase disparity, intensity differences, and Doppler shifts) that contribute to motion sensitivity. Although the data are not presented here, these cues were played back individually, or in pairs, to determine the most effective combinations. In future work, single or paired cues could be held constant as the remaining cue(s) is changed dynamically.
Another concern is the use of anesthetics and their effect on cortical
responses. The ketamine and xylazine cocktail employed here is known to
decrease spontaneous cortical activity relative to the unanesthetized
condition (Lumb and Jones 1984). Nevertheless the level
of activity remaining was high compared with other anesthetic agents
(Zurita et al. 1994
). The important point is that neuron responses in conscious animals might differ from those found here (Ahissar et al. 1992
).
Motion and location sensitivity
The distinguishing characteristic of location and motion is that the former relies on stationary stimulus cues, whereas the latter is associated with dynamic cues. The property of motion, as represented in the coding of cortical cells, results from the dynamic interplay of interaural phase, intensity, and frequency. The interplay of these cues is perhaps clearest for circumferential motion where interaural phase and intensity systematically change as the sound moves, for example, from 0 to 90°. The dynamic property of radial motion, however, might appear to be nothing more than an amplitude modulated signal with a constant phase disparity to position the sound on the horizontal plane. Indeed the equations for radial motion calculated stimulus intensity based on the distance from the source to the middle of the head. These radial stimuli, nevertheless, also contained dynamic components for phase and frequency at the two ears (Eq. 5). These components were small because of the small head size and low motion velocity, and their contributions to the cellular responses for radial trajectories are currently unknown. Nevertheless the well-defined regional selectivity seen within the radial neuron discharge in Fig. 7, suggests that these slight phase disparities and Doppler shifts may be important.
Various investigations have demonstrated that cells in AI are sensitive
to sound location (Ahissar et al. 1992; Imig et
al. 1990
; Middlebrooks and Pettigrew 1981
;
Poirier et al. 1997
; Rajan et al. 1990
).
The issue raised by location sensitivity is whether or not it can be
distinguished as an independent process or as a contributing process to
an emergent neuron-coding scheme for motion sensitivity. Moreover it is
important to consider how this distinction can be demonstrated and the
validity of that demonstration (Poirier et al. 1997
).
Investigators studying motion sensitivity in the auditory nervous
system have developed a variety of techniques for graphically representing the neural activity of moving stimuli. This is not a
trivial task because of the number of variables that need to be
portrayed (i.e., direction, speed, spatial location, neural activity,
etc.). The polar response plots presented here were designed to
emphasize activity with regard to the direction of motion within the
various segments. We felt that this was important because the most
cogent criteria for demonstrating motion sensitivity lies in the
differentiation of neuron activity for stimulus movement in one
direction but not the other (Ahissar et al. 1992;
Poirier et al. 1997
; Wagner et al. 1997
).
When a cell responds to movements in both directions of the trajectory,
it is most likely detecting location rather than motion. The examples
of polar plots presented in Figs. 3 and 5 used the idea of directional
selectivity to distinguish between motion and location in the responses
of our cells. Other schemes for plotting motion data may be identified
in the future, but for now we feel the polar plots presented here
clearly distinguish between motion and location sensitivity.
In addition, it was also possible to compare movement activity with activity at overlapping or adjacent fixed spatial locations (Table 1 and Fig. 8). When this comparison was made in motion-sensitive cells, the neuron response to moving stimuli was greater than to stationary stimuli. Moreover, for cells responding to radial trajectories that were apparently sensitive to location, a difference between stationary and moving stimuli could not be identified (Fig. 8B). Evidence for a distinction between motion and location sensitivity was even more compelling when the maximum motion trajectories were described as discharge profiles (Figs. 6, 7, and 9). Collectively, all these observations support the conclusion that there were distinct cell types in the rat primary cortex responsive to sound that was either at a fixed location or moving.
This conclusion, however, was less distinct when the neuron discharge
profiles were considered further. Indeed the real significance of these
profiles is that they revealed regions of elevated neural activity
within the circumferential or radial trajectories of motion-sensitive
cells. From cell to cell the position and spatial extent of these
regions varied. Some cells were highly selective, with the region of
increased activity apparent over only 10 to 20°, or a few tenths of a
meter, whereas other cells showed heightened activity over a wide
spatial extent (data not shown). This positional selectivity also has
been reported in cat AI motion-sensitive cells (Poirier et al.
1997).
The importance of this observation is that the omnidirection (location
sensitive) cells failed to show any proclivity toward detecting motion,
whereas the motion-sensitive cells (uni- or multidirection) responded
to movement at a particular spatial location within the trajectory.
This suggests a convergence on the motion-sensitive cells of underlying
neural codes that encrypt the cues for both location and motion. Other
authors have argued for a similar convergence of these two processes at
the primary auditory cortex (Ahissar et al. 1992;
Poirier et al. 1997
; Wagner et al. 1997
),
and we believe that the discharge profiles convincingly demonstrate
this emergent property. Finally the observation of regional specificity
within a trajectory for cells sensitive to circumferential motion
raises an important question. What does the topography of motion
selectivity look like in the horizontal plane with changing distance
from the head? For example, if circumferential motion were simulated at
2, 4, or 8 m from the head (with velocity held constant), does the
angular width of the motion-selective receptive field become broader or
narrower within the trajectory as simulated distance changes?
Distribution of cell types
Our results indicate that 39, 19, and 42% of the cells were
sensitive to either motion or location, or insensitive to either of
these conditions, respectively. Poirier et al. (1997)
reported that 26% of their AI cells, in lightly anesthetized cats,
were motion sensitive as defined by directional selectivity. About 11%
of their cells responded only to location. Ahissar et al. (1992)
, recording from the primary auditory cortex of awake
monkeys, reported that 35% of their units responded differentially to
sound-motion direction with 30% sensitive to location alone.
Stumpf et al. (1992)
indicated that most of their
cortical units in AI were sensitive to motion directed toward the cat
(radial motion in the present experiment). Only 10% of their units
were sensitive to circumferential movement along the horizontal plane.
Table 2 indicates that 70% of our motion units were sensitive to
radial movements, but of these only 43% were predisposed to motion
toward the head. These comparisons with other studies, while broadly approximating the present observations, need to be treated cautiously because of considerable procedural differences and the relatively small
sample size reported here.
Multidirectional cells
To our knowledge, cells with multiple directions of motion sensitivity have not been reported previously. The important observation in these data (Fig. 4) is that the peaks almost always were found in segments that were spatially adjacent to each other. One of these segments was radial, whereas the other was circumferential. It is not clear if this observation is a peculiarity of the stimuli used in this study (e.g., the use of multiple discrete trajectories) or a true phenomenon of coding in primary auditory cortex. Moreover both of these segments were motion sensitive because they responded differentially to the direction of movement. We speculate that these cells were sensing a more complex vector of motion that might be oblique to the circumferential or radial trajectories.
Conclusion
The current data demonstrate that there are cells in the rat primary auditory cortex sensitive to the direction of sound motion. Some of these cells respond exclusively to radial or circumferential trajectories, whereas others appear sensitive to both. An examination of discharge profiles revealed inherent location specificity in the motion-sensitive cells. Last, the use of digitally simulated motion may simplify future studies of motion sensitivity in the auditory system. These stimuli are relatively easy to generate and can be specified accurately and component parts can be separated to tease out the unique contributions of phase, intensity, or Doppler cues. Future developments will allow these stimuli to be presented over a wider range of frequencies and simulated motion conditions than employed here.
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ACKNOWLEDGMENTS |
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The authors thank Dr. Virginia Richards and W. T. Saunders for contributing to the work. Comments on the manuscript by K. Duncan, M. Dubin, M. Eisen, R. Ipakchi, A. Lieberman, and S. Hsuing are greatly appreciated.
This work was supported in part by grants from the Pennsylvania Lions Hearing Research Foundation and from the National Institute of Deafness and Other Communications Disorders to J. C. Saunders.
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FOOTNOTES |
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Address for reprint requests: J. C. Saunders, 5 RavdinORL, 3400 Spruce St., Philadelphia, PA 19104.
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 August 1998; accepted in final form 29 January 1999.
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REFERENCES |
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