Department of Neuroscience, Brown University, Providence, Rhode Island 02912
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ABSTRACT |
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Sanderson, Mark I. and James A. Simmons. Neural Responses to Overlapping FM Sounds in the Inferior Colliculus of Echolocating Bats. J. Neurophysiol. 83: 1840-1855, 2000. The big brown bat, Eptesicus fuscus, navigates and hunts prey with echolocation, a modality that uses the temporal and spectral differences between vocalizations and echoes from objects to build spatial images. Closely spaced surfaces ("glints") return overlapping echoes if two echoes return within the integration time of the cochlea (~300-400 µs). The overlap results in spectral interference that provides information about target structure or texture. Previous studies have shown that two acoustic events separated in time by less than ~500 µs evoke only a single response from neural elements in the auditory brain stem. How does the auditory system encode multiple echoes in time when only a single response is available? We presented paired FM stimuli with delay separations from 0 to 24 µs to big brown bats and recorded local field potentials (LFPs) and single-unit responses from the inferior colliculus (IC). These stimuli have one or two interference notches positioned in their spectrum as a function of two-glint separation. For the majority of single units, response counts decreased for two-glint separations when the resulting FM signal had a spectral notch positioned at the cell's best frequency (BF). The smallest two-glint separation that reliably evoked a decrease in spike count was 6 µs. In addition, first-spike latency increased for two-glint stimuli with notches positioned nearby BF. The N4 potential of averaged LFPs showed a decrease in amplitude for two-glint separations that had a spectral notch near the BF of the recording site. Derived LFPs were computed by subtracting a common-mode signal from each LFP evoked by the two-glint FM stimuli. The derived LFP records show clear changes in both the amplitude and latency as a function of two-glint separation. These observations in relation with the single-unit data suggest that both response amplitude and latency can carry information about two-glint separation in the auditory system of E. fuscus.
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INTRODUCTION |
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The big brown bat, Eptesicus
fuscus, actively probes the environment by emitting trains of
brief (0.5-15 ms), wideband, frequency-modulated (FM) ultrasonic
sounds (frequencies of ~20-100 kHz) and perceives objects, such as
flying insect prey and obstacles to flight, from echoes of these sounds
(Griffin 1958; Simmons 1989
). Acoustic dimensions of these echoes are rendered into spatial dimensions of the
biosonar images perceived by the bat by a process that is not simply a
conversion of individual echo parameters into corresponding perceptual
parameters. Auditory representations for the time and frequency
dimensions of echoes are quite different, yet somehow information in
these two representations converge onto the same perceptual dimension
of images, which requires a transformation of one of these
representations into the units of the other (Simmons et al.
1990
.) An idealized point-target returns a single echo of each
broadcast at a delay corresponding to the target's distance, or range
(echo delay = 5.8 ms/m of target range), and the basis for the
bat's perception of target range is auditory processing of latency
differences between responses to broadcasts and responses to echoes
arriving at that particular delay (Dear and Suga 1993
;
Dear et al. 1993
; O'Neill and Suga 1982
;
Simmons et al. 1996
; Wong and Shannon
1988
). However, a more realistic, complex target contains
multiple reflecting points, called "glints," which return
reflections at different times corresponding to their range separations
(5.8 µs/mm of range difference), which are small compared with the
target's absolute range. For example, the acoustic signal returned
from a flying insect, the size-and-shape dimensions of which are only
1-3 cm, contains several individual reflections of the incident sound
arriving at time separations of only 60-120 µs. These time
separations are much shorter than the bat's sonar sounds, which are
several milliseconds, and shorter, too, than the integration time for
echo reception, which is ~300-400 µs (Simmons et al.
1989
), so the reflected sounds overlap in time and combine
together to interfere with each other. Integration time is the interval
over which energy in a filter is summed: the output of the filter
effectively blurs the separation of two discrete signals if their time
separation is less than the filter's integration time.
The combined "echo" from the insect thus has an overall delay that
corresponds to target distance as represented by response latencies
plus an interference spectrum, composed of peaks and notches at
specific frequencies presumably represented by response-strength in
frequency-tuned neurons (as previously proposed by Schmidt 1992; Simmons 1989
). In spite of the difference
in format between response latencies for representing target range and
frequency tuning of responses for representing the echo spectrum, the
bat nevertheless perceives the relatively small size-and-shape
dimensions of the target along the same psychological scale of distance
used for perceiving the overall distance to the target. Somehow from the echo interference spectrum the bat estimates the delay separations of the overlapping sounds returned by the target's glints and expresses these estimates in the same numerical units as target range
itself (Simmons et al. 1990
, 1998
), which requires an
auditory rerepresentation of the features of the interference spectrum to transform the peaks and notches at different numerical values of
frequency into an estimate of the delay separations in numerical values
of time. Is this done by converting the frequencies of spectral
features into latencies for subsequent processing alongside latencies
already used to represent echo delay? The bat's system of shape
representation is surprisingly acute; expressed in terms of delay
separation, the two-point resolving power of the big brown bat's sonar
system has been measured to be in the range of 2-10 µs
(Mogdans and Schnitzler 1990
; Simmons et
al. 1998
; see Simmons et al. 1995
), and acuity
for changes in two-point separation also is in the region of several
microseconds (Schmidt 1992
; Simmons et al.
1974
).
An additional source of spectral peaks and notches is introduced into
echoes by the directional filtering characteristics of the bat's
external ear serving as a receiving antenna (Wotton et al.
1995). The ear's pinna and tragus are obliquely opposed, curved surfaces that guide sound into the external auditory canal and
down to the tympanic membrane, in the process creating several reflections arriving at the eardrum after slightly different delays. These delays create their own interference notches in the spectrum of
echoes besides those introduced by the target itself. For humans and
other animals, the frequency of notches in the external-ear transfer
function varies with the elevation of the sound source (cats,
Rice et al. 1992
; humans, Blauert 1969
).
In the big brown bat, the most prominent spectral notch systematically
varies from 55 kHz down to ~30 kHz as elevation decreases from 0°
(straight ahead) to
50° below the horizon (Wotton et al.
1995
). Thus, spatial information regarding two features of
objects-shape and elevation-is associated with significant notches in
the spectrum of echoes, and in both cases, the bat perceives these
notches in terms of the time separation of multiple reflections by a
transform process (Simmons et al. 1990
, 1998
;
Wotton et al. 1996
).
Neurons at various stages in the big brown bat's auditory system
[e.g., cochlear nucleus (CN), Haplea et al. 1994;
nucleus of the lateral lemniscus (NLL), Covey and Casseday
1991
; IC, Casseday and Covey 1992
;
Ferragamo et al. 1998
; Jen and Schlegel
1982
] are each tuned to a specific best frequency in the range
of ~10-100 kHz, and it is presumed that interference notches in echo
spectra are represented by the absence of responses in neurons tuned to the frequency of a notch; but critical details about these responses are not known. Because successive frequencies are arranged in descending order in the FM sweeps of the bats signals, information conveyed by responses to spectral peaks or notches at specific frequencies might be translated into a latency code related to the time
at which these frequencies occur in the sweep. How do neural responses
to the spectral features of echoes contribute to transforming the shape
the echo spectrum, which intrinsically is a multivalued
(multiple-frequency) representation, into a single estimate of the
delay separation of reflections? The present study was conducted to
address these questions.
Portions of this paper were presented previously at the 22nd meeting of the Association for Research in Otolaryngology (1999).
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METHODS |
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Recordings
Four big brown bats (E. fuscus), collected in Rhode
Island, were used in this study. Hair was removed from the bat's head with depilatory cream. To prevent infection, a sterile field was maintained, and sterile tools and cotton swabs were used during surgery. Under isoflurane anesthesia, the skin was washed with a 10%
povidone-iodine solution followed by a 70% alcohol wash. The skin and
muscle overlying the skull in the region of cortex and inferior
colliculus (IC) were removed, and a small stainless steel post was
attached to the skull with cyanoacrylate cement. After surgery local
topical application of lidocaine (2% gel) was used. After allowing 5
days for recovery from this surgery, the awake bat was placed in a
"bat-shaped" plexiglas holder and its head fixed in place using the
cemented post as the point of attachment. Two holders of different
sizes were made to comfortably accommodate the bats used in these
experiments. If the bats showed any sign of distress that was not
alleviated by an offering of mealworms or water, the experiment was
terminated. Access to the IC was obtained by carefully drilling a small
hole (~100-200 µm) in the skull with a sharpened sewing needle
viewed through an operating microscope (Jena, type 212). This caused no
apparent discomfort to the bats, which rest quietly during this
procedure. For each bat, no more than four craniotomies per side were
drilled. Data in this study were collected from both the left and right hemispheres of the IC. Gelfoam was used to cover each craniotomy at the
end of each experimental day. No antibiotic was needed to prevent
infection after each experiment. The bats did not exhibit any signs of
infection during the course of these experiments, possibly because the
bat's resting body temperature is low. The experiments lasted between
3 and 6 h with mealworms and water offered at periodic intervals
throughout that time period. After each experiment, the bat was
returned to its individual cage in a colony room. An individual bat had
a minimum of 1-day rest in between experimental days, but most often
3-4 days separated consecutive experimental days for a given bat. At
the end of the experiments, the bat was given an overdose of
pentobarbitol sodium for use in other histological experiments. The
surgical and experimental procedures conformed with National Institutes
of Health guidelines.
The bat and the loudspeaker for stimulus delivery were situated inside
a sound-proof booth (Industrial Acoustics) the walls of which were
covered by anechoic foam panels. The bat was placed at a distance of 35 cm from the speaker, positioned so that the speaker was at ~0°
elevation and 0° azimuth. The ambient temperature in the booth was
maintained between 72 and 75°F. Single-unit activity was recorded in
the IC with 3 M NaCl glass microelectrodes (2-10 M) advanced by a
hydraulic microdrive (Trent-Wells). Signals picked up by the electrode
tip were amplified (WPI dAMP 1,000 times), and filtered to a band of
200-8,000 Hz by a variable band-pass filter (Wavetek/Rockland Model
442). Amplified spike waveforms were thresholded and time stamped
(100-µs resolution) using Brainwave hardware and software (version
3). Event waveforms representing putative spikes were cluster-cut
off-line using Brainwave software to remove any multiunit or evoked
potential activity from subsequent single-unit analysis. At the same
time, analogue averaged local field potentials (LFPs) were collected
from the same recorded signals using the same 200- to 8,000-Hz filter
settings (acquisition and averaging on an R. C. Electronics Model
ISC-16 data-acquisition board, at a 10-kHz sampling rate, using
custom-written software). Acoustic travel time from speaker to bat was
subtracted from all spike time stamps and field potential records. The
indifferent electrode (tungsten, Frederick Haer) for these recordings
was inserted into frontal cortex.
Local field potentials
We recorded the local evoked field potential to estimate the
"population" response to the stimulus sequence. The field potential is the sum of synaptic and action potentials picked up by the electrode; the amplitude of the potential reflects the number, proximity, and synchronicity of electrical events near the tip of the
electrode. Averaging over trials attenuates events that have variable
timing from trial to trial. Because of the phasic nature of the
majority of NLL and IC cells and their relatively stable response
latencies (Ferragamo et al. 1998; Haplea et al. 1994
), field potentials are well suited to providing an
estimate of the responses of large numbers of underlying neural elements.
In the auditory system, the synchronous nature of events in the lower
brain stem leads to large amplitude evoked potentials. The sources of
peaks N1-N4 in the evoked
potential are identified by their latency (auditory nerve:
N1 at 0.8 ms, CN: N2 at 1.5 ms, SOC: N3 at 1.9 ms, LLN:
N4 at 2.9 ms) (Friend et al. 1966; Grinnell 1963a
; Suga and Schlegel 1973
).
The widths of these peaks of the field potential are very brief,
similar to the width of an extracellular action potential
(N1: 300-800 µs; N4:
1-1.5 ms) (Grinnell 1963a
). Generally, the property
which most affects the volume of tissue over which an electrode
"samples" field potentials is impedance, and the degree to which a
field potential is a reflection of local (IC) or distant (cortex,
thalamus, NLL, CN, auditory nerve) electrical events is easy to test
because the IC has a tonotopic structure in the dorsal-ventral axis.
Moving the electrode over the dorsalventral extent of the IC led to
changes in the BF of the N4-evoked potential
(Friend et al. 1966
). The N4 is thought to reflect the input of NLL fibers to the IC (Friend et al. 1966
; Suga 1969b
; Suga and Schlegel
1973
). With a low-impedance electrode (e.g., an implanted
silver wire) the activity of IC units shows up as a slow component
after the N4 (Friend et al. 1966
;
Grinnell 1963a
). The slow component reaches a peak at
7-9 ms and is 15-20 ms in duration (Friend et al.
1966
); this effect is due to the broadly distributed latencies
of IC cells (4-26 ms) (Suga and Schlegel 1973
). When
the electrode was in the IC, the "hash" of poorly isolated units,
not the slow component, dominated the field potential after the
N4. This was due to the higher impedance of our
glass microelectrodes as compared with silver wire electrodes (Friend et al. 1966
; Grinnell 1963a
).
Field potentials were recorded in the IC proper and also in sites ventral to the IC, probably the NLL (E. Covey, personal communication). The data plotted in Figs. 2 and 3 were collected after the electrode was advanced beyond the most ventral active sites in the IC (at depths of ~2,000 µm), through an additional ~800 µm of tissue in which no auditory-evoked responses could be recorded and into sites with large auditory-evoked potentials (hundreds of µV) corresponding in latency to the N4 response peak. These recordings required less averaging due to the strong, highly synchronous activity in the lower brainstem. Similar results were observed in recordings collected at depths <2,000 µm along the lateral extent of the IC, but these required a greater degree of trial averaging to reduce the background noise.
The averaged LFP records were plotted relative to the onset of each FM
sweep in the stimulus protocol. To remove the common-mode response, the
difference between an average LFP evoked by one stimulus and a
reference average LFP evoked by another stimulus was calculated to form
a derived LFP. The reference LFP was chosen to be the nearest neighbor
response, either to a stimulus before or after the current stimulus.
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(1) |
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(2) |
Stimuli
FM signals such as the big brown bat's biosonar sounds are
characterized by multiple acoustic parameters of amplitude, start and
stop frequencies, harmonic structure, sweep rate (kHz/ms), sweep shape
(e.g., linear, sinusoidal, hyperbolic), and duration. Several of these
parameters necessarily covaryfor example, if duration is changed
while the frequency range is fixed, then sweep-rate changes as well. As
stimuli, we used FM sweeps that matched natural Eptesicus
vocalizations from the bat's insect-pursuit sequence (see
Simmons 1989
)
a 10-ms sweep duration from an early
stage of pursuit and a 2-ms duration from a later stage of pursuit. A
standard 2- and 10-ms FM sweep was created in software (2 harmonics, hyperbolically sweeping from 100 to 40 kHz in the 2nd harmonic, 50 to
20 kHz in the 1st harmonic, cosine shaped rise-fall times: rise-time
equaled fall-time, 400 µs for 2-ms sweeps, and 2 ms for 10-ms
sweeps). The equation for the instantaneous frequency of these signals
is
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(3) |
Simulated two-glint echoes were created by presenting two FM sweeps added together at various delay separations (0-24 µs). Figure 1 shows a spectrogram of the 0-µs two-glint separation signal (Fig. 1D), example stimuli waveforms (Fig. 1E), and spectra (Fig. 1F). The minimum step size used for two-glint separation was 2 µs, which matched the D/A board's clock (500 kHz). The use of a 2- and a 10-ms FM sweep protocol was necessary to examine the relative timing of responses to FM signals with different sweep rates and to allow us to record from the many cells in the IC that prefer slower sweep rates and/or longer durations.
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The interference spectra of two-glint signals where both signals are of
the same phase contain a series of peaks and notches at regular
intervals given by
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(4) |
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(5) |
Stimuli were presented at a 10-Hz repetition rate using the following protocol (Fig. 4A): after four stimuli with 0-µs two-glint separation, two-glint separation changed from 2 to 24 µs and then back down to 0-µs two-glint separation, in 2-µs steps. Two additional stimuli, 29 and 30, with 0-µs two-glint separation were added onto the end of the protocol. Three of the seven 0-µs two-glint separation FM sweeps were not generated using the delay and add procedure; these sweeps (Fig. 4A, *) were, in effect, single sounds, 6 dB weaker than FM sweeps generated using a delay and add with 0-µs delay. In total, 30 FM sweeps were presented in 3 s. This set of stimuli was presented to the bat for 20-33 repetitions. Stimuli were presented at ~10-20 dB above threshold for the 0-µs two-glint separation stimulus. Most often the stimulus level was set at 30-50 dB SPL peak to peak.
Because of hardware constraints, there was a period of 1.2 s
between the last stimulus of the protocol on a given trial and the
first stimulus of the protocol on the following trial. Thus a given
neuron was in a less-adapted state for the first several stimuli
compared with later stimuli in the stimulus sequence. Recovery periods
for IC units range from 3 to hundreds of milliseconds with ~80% of
the units recovering completely within 100 ms (Friend 1966; Grinnell 1963b
; Suga and Schlegel
1973
). A normalized index of adaptation value for each of the
74 neurons was calculated by taking the difference in spike count
between an early and late presentation of an identical stimulus
condition (e.g., spike counts for stimulus 1 vs. 29, Fig.
4A) and dividing the result by the maximum of the two spike
counts. The index varies between
1 and 1, with a value of 1 corresponding to complete adaptation after the response to the first
stimulus. A value of 0 indicates no adaptation. For the comparison
between the responses to two stimuli, a t-test was used to
determine whether the mean of resulting distribution of adaptation
indices (n = 74) was significantly different from zero.
The distribution of adaptation indices for comparisons between the
first three and last three stimuli (for example, 1 vs. 29 or 2 vs. 30)
had means greater than zero (P < 0.05). With the stimulus
protocol of Fig. 4A, there are a total of 20 possible different comparisons between identical stimuli. No other stimulus comparisons showed significant effects of adaptation (e.g., the distribution of adaptation indices calculated for stimulus 4 vs. 28 was
not significantly different from 0; P = 0.24). Thus by the time the stimulus sequence reached stimulus 4 (a 0-µs 2-glint stimulus), adaptation effects at the population level had leveled off.
This can be qualitatively assessed in the spike counts shown in Fig. 4,
E-I: if adaptation had a significant impact on the response, then the response to the second presentation (
) of a
stimulus within the sequence would be lower than the response to the
first presentation (
) of that same stimulus. This result is also
visible in the population average of spike counts across the stimulus
sequence (Fig. 4D). A strong adaptation effect would have
resulted in a obvious negative slope (linear regression fit of the data
had a slope of
0.04 spikes · stimulus
1 · s
1,
R2 = 0.001).
Pure tone bursts (4.4 or 10 ms) were presented at either a 20- or 4-Hz presentation rate to characterize the frequency tuning characteristics of the units. The stimuli had linear rise/fall times set at 0.44 or 1 ms for the 4.4- and 10-ms stimuli, respectively. Frequencies were presented in an ascending order. The initial stimulus had a 20-Hz presentation rate and consisted of 59 pure tones spaced logarithmically from 10 to 100 kHz. As time permitted, subsequent pure tone sequences spanning a smaller frequency range around BF with linear steps (1 kHz) were presented at a fast (20 Hz) or slow (4 Hz) presentation rate, depending on the preference of the neuron under study. The pure tone sequences initially were presented at 20 dB above threshold and then, as time permitted, at one or two additional amplitudes (+10, +40) above threshold. Stimulus thresholds for BF ranged from 8 to 82 dB SPL peak to peak (mean 40 ± 20 dB SPL).
All stimuli were converted at 500 kHz using custom-written software controlling a Tucker-Davis QDA2 board. The analog signal then was high-pass filtered (5 kHz, Wavetek), attenuated (Hewlett Packard 350D), and amplified (Apex PA02M high-voltage operational amplifier) before being sent to a speaker (Panasonic leaf-tweeter, FAS-10TH1000) located 38 cm from the bat in the sound-proof booth.
Data analysis
The files of time stamps for single-unit responses to each
stimulus presentation were exported to MATLAB and analyzed off-line as
dot-raster plots and peristimulus time histograms (PSTH). The number of
spikes per stimulus was calculated by counting the number of spikes in
a 100-ms window after stimulus onset and dividing by number of stimulus
presentations (usually 33). Spike-count functions were generated by
fitting a spline curve to the average of the spike counts from two
identical stimulus conditions in the protocol using stimuli 4-28 (see
Fig. 4A for stimulus protocol). The preceding stimuli, 1-3,
and the following stimuli 29 and 30, were used only for measuring
adaptation effects. The 24-µs condition, stimulus 16, was only
presented once during the stimulus protocol. Thus the value in the
spike-count function for this 24-µs two-glint separation was based on
a single spike count. In the overwhelming majority of single-unit
spike-count functions, it was clear which stimulus condition evoked the
smallest response (representative examples in Fig. 4, E-I).
The following criterion was used to qualify a response to a particular
stimulus as being the local minimum in a spike-count function: each of
the two spike counts that contributed to the average value at the local
minimum of a spike-count function had to be less than the smallest
response evoked by the 0-µs two-glint separation stimuli (Fig.
4A, 4 and 28). The position of the local minimum, if any,
was taken from the spline curve. Spike-count functions from cells with
BF >50 kHz had two local minima because a second spectral notch moves across any of these frequencies for two-glint separations 16 µs
(Fig. 4I). Each local minimum in the spike-count function
was tested. Local maxima were estimated using the same criteria but with the opposite sense.
The width of the valley, or dip, in the spike count function around the minimum was measured by using the spike count at the 0-µs two-glint stimulus as a baseline: the 50% level between baseline and minimum was found and the width at 50% was measured (see arrows in Fig. 7C for examples). For spike count functions with a minimum that fell near the upper border of the two-glint separation axis (Fig. 7A), only the lower half-width of the dip was measured.
First-spike latency was estimated by taking the mean of the
distribution of first-spike times in a window after stimulus onset. Simultaneous displays of PSTHs and dot rasters were used to identify and select appropriate analysis window start and stop times
(Heil and Irvine 1998). The window start and stop time,
relative to FM sweep onset, was identical across an entire stimulus
protocol. The window length was identical for all the data collected
for a single unit (window length = stop
start time;
usually set to 20 ms). These windows excluded most of what we presumed
to be spontaneous activity. Even low-rate spontaneous activity could bias first-spike latency estimates calculated from fixed windows that
started at stimulus onset and ended 25 ms later. No latency analysis
was carried out on units with high spontaneous rate (>10 spikes/s,
n = 2). Estimates of first-spike latency based on
less than eight spikes across all trials with a given stimulus were discarded. A shift in first-spike latency in responses to the two-glint
separation protocol was qualitatively assessed as follows. First-spike
latency was plotted versus stimulus condition as shown in Fig. 4,
E-I, right (for clarity, these plots
only show the first-spike latencies for stimuli 4-16, see Fig.
4A). The plots were examined to determine whether the
first-spike latency values for identical stimuli later in the protocol
(17-28) exhibited a similar trend wherein latencies increased
specifically around two-glint separations that placed a notch near BF.
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RESULTS |
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LFPs
When the separation of two-glint stimuli is >400-500 µs,
separate neural responses should be observed for each stimulus
(Friend et al. 1966; Grinnell 1963b
). Our
standard recovery cycle experiment was conducted using two-glint
separations that spanned a range from 4 ms to 200 µs (Fig.
2). The main response in the LFP is called the N4 (Fig. 2A, labeled
R1 on the bottom trace), and occurs at
a latency of 3 ms; this is appropriate for neural elements in the NLL
(Covey and Casseday 1991
; Haplea et al.
1994
). We analyzed the N4 response
because of its brief duration and low variability from trial to trial.
A separate N4 response (labeled
R2 in Fig. 2A) to the second of a pair
of FM sweeps was visible down to a 1-ms two-glint separation (Fig.
2A, arrow). Recovery to 100% of peak-to-peak height
(relative to the response evoked by the initial FM sweep of each
2-glint pair) was achieved at an two-glint separation of 2 ms (Fig.
2B). The derived LFPs for these recovery cycle experiments show a biphasic waveform with a latency correlated to two-glint separation (Fig. 2C, and the surface plot in E).
This relationship is quantified by a linear fit to the peak latency
values of the derived LFPs (y = 0.9713x + 3,090, R2 = 0.999; Fig.
2D). The derived LFP waveform disappears at the 800-µs
two-glint separation (which is the difference between the responses to
800 and 600-µs 2-glint stimuli plotted in 2A, indicated Fig. 2, C and E, arrows).
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Another stimulus protocol was run using smaller steps between
successive two-glint conditions (1,400- to 100-µs 2-glint separations in 100-µs steps): the derived LFPs showed a consistent biphasic waveform present down to the difference between the 800- and 700-µs two-glint conditions (Fig. 2F, bottom arrow). These results
with FM sweeps generally agree with other recovery cycle experiments, which report separate N4 responses evoked by
click stimuli with intervals sometimes as short as 500 µs
(Friend et al. 1966; Grinnell 1963b
).
Recovery cycle results vary with differences in stimulus intensity,
spectra, and duration (Friend et al. 1966
,
Grinnell 1963b
), and the difference in recovery times of
800 versus 500 µs is probably due to the longer stimulus duration
used in the present experiments.
The unexplored region of these response functions is for stimuli with two-glint separations smaller than the minimum recovery time for a full-fledged second response. The derived LFPs for glint separations <500 ms showed complex oscillations, often with opposite phase (Fig. 2F, top arrow). The derived LFPs in Fig. 2F show a structure both in amplitude and latency, indicating that information may be available to the auditory system to deconvolve two separate events from a single complex response. For this reason, we focused on responses to two-glint separations <25 µs.
Averaged LFPs in the IC also were collected in response to two-glint separations presented well within the shortest recovery time of the N4 LFP (using the single-unit stimulus protocol described in METHODS consisting of 2-glint stimuli with separations ranging from 0 to 24 µs in 2-µs steps). These stimuli have deep spectral notches (Fig. 1F), the position of which is a function of two-glint separation (Fig. 3C, top). Figure 3A shows a series of averaged LFPs from a the same recording site as shown in Fig. 2 (depth, 3,500 µm; BF, 66 kHz). Sixteen stimulus repetitions were used in the averaged LFPs, but the effect was clearly visible in with just one presentation. The shape of the local evoked potential changed when a stimulus was presented that had a notch near the BF of the recording site. The peak LFP amplitude decreases for stimuli with a two-glint separation of 8 and 22 µs. The local minima (Fig. 3B) occur at 7.5 and 21 µs, which have spectral notches, 66.7 and 71.4 kHz, respectively, near BF (the site was tuned to 71 kHz, see Fig. 3C, bottom). Figure 3C, top, shows the position of spectral notches in the two-glint stimuli. The vertical dotted line shows where BF for this site intersects with the primary and secondary spectral notches: at 7 and 21 µs, respectively, nearly matching the minima in Fig. 3B.
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The derived LFPs in Fig. 3D show large peaks and troughs wherever a difference in either amplitude or latency (or both) occurs between one evoked LFP and the LFP evoked to the previous stimulus. As expected, the largest peaks in the derived LFPs occur for those LFPs evoked in response to stimuli with two-glint separations near 8 or 22 µs. What was not expected a priori was that the timing of peaks and troughs in the derived LFP would move in a systematic fashion, correlated with the timing of the spectral notches in the two-glint stimuli. Figure 3E plots the timing of the primary and secondary spectral notches (dashed and solid lines, respectively) in the two-glint stimuli. Because of the hyperbolic FM sweep shape (Eq. 3, Fig. 1D), the timing of the spectral notches is related linearly to two-glint separation (see Fig. 3C, top, which plots spectral notch position on a hyperbolic frequency axis). The stimuli are composed of two harmonics sweeping down from 50 to 20 and 100 to 40 kHz, respectively (Fig. 1D). The first prominent spectral notch (83.3 kHz) appears with a latency of 0.274 ms in second harmonic of the 6-µs two-glint separation stimulus. For the 12-µs two-glint stimulus, the primary spectral notch of 41.7 kHz occurs with a latency of 1.91 ms, at the end of the second harmonic. For the 14- to 24-µs two-glint stimuli, the primary notch then moves through the first harmonic starting from 0.547 to 1.981 ms (bottom dashed line). The secondary notch appears in the second harmonic of the stimuli for two-glint separations of 16-24 µs (solid line). For this recording site, the latency of the trough in the derived LFP approximately follows the timing of the primary and secondary spectral notch as they appear in the second harmonic (top dashed and solid line, respectively). Latency data from another site with a BF tuned to 23 kHz more closely match the timing of the primary spectral notch as it moves through the first harmonic (Fig. 3F, bottom dashed line). For these two cases, the latency to the peak of derived LFP showed greater variation because the complex nature of the waveform led to maximum values that could occur on either side of a trough. The point here is not to show strict transformation of the spectral notch timing in the peak or trough of the waveform but that the waveforms shift in a manner consistent with spectral notch timing. This is most obvious in the surface plots of Fig. 3, G and H. The overall amplitude of the derived LFPs are smaller in Fig. 3, H versus G, because the stimuli were presented at 68 dB SPL as opposed to 88 dB SPL in Fig. 3G.
The derived LFP qualitatively resembles a band-pass filtered, rectified, and low-pass-filtered version of the stimuli themselves. Because the notches occur at particular times in the FM sweeps, the envelopes of the signals undergo a systematic change as a function of notch frequency, and this change is reflected in the timing and amplitude of the evoked potential.
The N4 reflects a prominent source of input into
the IC (Suga 1969b), a nucleus that is an obligatory
site at which all ascending information from the lower brain stem
terminates. The output from the NLL feeds forward into IC where we
expected to see similar responses in IC single-unit activity but with
variable translations in onset time due to the IC delay lines
(Ferragamo et al. 1998
; Saitoh and Suga
1992
).
IC single-units
Two-glint stimuli with separations between 0 and 24 µs were presented to 74 single-units recorded in the IC of three bats. BFs were obtained for 65 of 74 units and ranged between 20.5 and 96 kHz (median 43 kHz, mean 48.2 ± 19.5 kHz). Single units discharged between one and two spikes for each presentation of a single FM sweep set ~20 dB above threshold. Some units preferentially responded to the longer FM sweeps (10 ms), and their responses are included with units which responded to short FM sweeps (2 ms).
Spike count
When presented with two-glint FM sweeps with separations between 0 and 24 µs, the majority (62/74, 84%) of single units showed decreased response strength for specific values of two-glint separation. An example PSTH in Fig. 4B shows that the neuron's response decreased for two-glint separations of 6 and 18 µs. Figure 4C plots a surface of all the spike counts for the 65 units with measured BFs. Spike-count functions from five representative units (BFs 22-93 kHz) are plotted in Fig. 4, E-I (left). The spike count data in Fig. 4, C and E-I, show that activity decreases for particular stimulus conditions as a function of BF.
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The local minima in spike-count functions occur for stimuli that have a
spectral notch that lies on or near BF. The local minima in the
spike-count function for all units with BF data are plotted in Fig.
5 (9 of 65 units did not have detectable
minima for the sound levels used here). Each point plots the response minimum to either the short- or long-duration sweep, whichever the unit
responded to best. No appreciable difference was observed when the data
were divided into separate plots based on FM sweep duration. The data
points are fit well by the lines for the FM signals' first and second
spectral notches ( and - - - respectively). High-frequency units
(BF >50 kHz) often show two local minima in their spike-count
functions. The first minimum corresponds to the first spectral notch of
two-glint FM stimuli (Fig. 5,
). The second minimum
corresponds to the second spectral notch of two-glint
stimuli (Fig. 5,
; see also Fig. 1F, 2-glint separations
16 µs, for the spectra of stimuli with 2 notches).
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Besides local minima, there were also local maxima in the spike-count functions of 29% of the units (19/65). This elevated response cannot be due to spectral peaks alone because the reference spike count is the two-glint signal with 0-µs separation that has the greatest spectral amplitude of all the stimuli. One example is plotted in Fig. 6A. This neuron has a BF of 31 kHz and did not respond to the 16-µs two-glint stimulus (this stimulus has spectral notches at 31.3 and 93.8 kHz). The 14-µs two-glint stimulus (notch at 35.7 kHz) reliably evoked about two spikes per trial, much higher than for any other stimulus condition.
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The maximum response in the spike-count function was widely distributed
across two-glint separation values for lower-frequency BFs (<50 kHz,
Fig. 6B). For units with BF >50 kHz, the maximum in the
spike count always occurred at a two-glint separation greater than the
delay that evoked the minimum spike count. This is due to the presence
of multiple notches in the spectrum for two-glint separations 16
µs; the data were replotted in terms of notch frequency nearest
to BF in Fig. 6C. The spike count minima fall near the
line with unity slope (
; n =18, one neuron had no local minimum in its spike-count function). These minima points are interpreted as in Fig. 5: when the spectral notch falls on or near BF,
excitatory drive to the neuron diminishes. The spike-count maxima are
presumably due to the spectral notch being aligned with an inhibitory
sideband. Thus a stimulus with a spectral notch positioned over the
inhibitory sideband would evoke less inhibitory input compared with
other two-glint stimuli with spectral notches positioned elsewhere.
Decreased inhibition coupled with excitatory drive to the BF region of
the frequency tuning area leads to higher spike counts than found with
a comparable "flat" spectrum signal, such as the 0-µs two-glint stimulus.
Intensity
Spike-count functions changed in two ways with increases in signal
level. First, overall spike counts increased with increases in signal
level (Fig. 7, A-D). Second,
the shape of the function around the minima changed (this will be
termed the "dip" in the spike-count function). At higher sound
levels, some spike-count functions no longer showed clear minima (Fig.
7, B and D). A short range of stimulus levels was
presented to any one unit, so only limited data were available to study
the effect of level on spike-count functions. Of those neurons for
which data were available from more than one stimulus level
(n = 27), 22 exhibited a minimum in the spike count
function at more than one stimulus level. The majority (19/22, 86%)
showed a decrease in the width of dip in the spike count function when
stimulus level increased (Fig. 7, A-C). For example the
width of the dip in the spike count functions of Fig. 7C
() decreased from 6.9 to 2.6 µs for a 10-dB increase in stimulus
level.
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Duration
Data were collected from 11 single units to examine responses to
both the 2- and 10-ms duration FM sweeps presented at the same level.
For most neurons (9/11), spike counts generally increased for the
longer duration stimuli (Fig. 8)
presumably due to the longer dwell time of signal energy in the
excitatory tuning area or tuning to specific sweep rates (Heil
and Irvine 1998). The additional number of spikes evoked per
trial by the 10- versus the 2-ms FM sweep was small: the largest
increase was by 1.71 spikes for the unit in Fig. 8A. The
consistent increase in spike count of Fig. 8A was found in
four neurons, three of which had BFs between 22 and 25 kHz and one with
a BF of 40 kHz. The remaining seven neurons, with BFs from 31 to 71 kHz, exhibited functions similar to that of Fig. 8B where
the increase in the number of spikes per stimulus for 10- versus 2-ms
FM sweeps was <1 (2 of these functions were ambiguous in that for
several 2-glint stimuli the 2-ms duration FM sweep evoked a greater
number of spikes than the 10- ms duration stimulus). This difference
may be due to the hyperbolic shape of the of the FM sweeps used in this
study (see Eq. 3). For a given duration, sweep rate in the
first and second harmonics is slowest (and thus signal energy per unit
time is greatest) for frequencies near 20 and 40 kHz, respectively.
|
Latency
The first-spike latencies for five units are plotted in Fig. 4
(E-I, right). Four of the five latency functions
(Fig. 4, F-I) show an increase in first-spike
latency for stimuli with a spectral notch near the BF (see Data
analysis). For the unit in Fig. 4I (BF 93 kHz), both
the 6- and 18-µs two-glint separations create a notch at 83.33 kHz,
and both stimuli evoke noticeable shifts in latency relative to
neighboring stimulus conditions. For the 6-µs two-glint stimulus,
first-spike latency decreased by ~2 ms relative to nearby two-glint
conditions. The data plotted here are representative of just over half
of the population used in Fig. 5 (33/55 units showed a shift in latency
for stimuli with 2-glint separations adjacent to the 2-glint separation
that evoked the minimum spike count; 1 neuron from Fig. 5 had a
spontaneous rate >10 spikes/s and was not included in the latency
analysis). The remaining 22 cells either showed no clear shift in
latency or the spike latency plots were incomplete and could not be
examined for a shift due to low spike counts (8 spikes were required
to measure mean 1st-spike latency). Increasing the number of stimulus trials would allow a more complete assessment of how these stimuli affect first-spike latency.
First-spike latency values were available for 25 of 27 neurons that
were presented with the stimulus protocol at different levels.
First-spike latency decreased with increasing stimulus levels for most
of these neurons (23/25). The latency shifts spanned a range from 52
to 500 µs/dB with a median value of 88 µs/dB.
In 8/11 cases where single units were presented with both 2- and 10-ms
duration stimuli at the same level, first-spike latency increased for
the longer FM sweep. This is a common finding for FM stimuli in which
only duration changes because the timing of effective frequency that
drives the neuron changes in proportion to signal duration
(Bodenhamer and Pollak 1981; Heil and Irvine 1998
).
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DISCUSSION |
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On a trial-by-trial basis, the majority of IC neurons discharge between one and two spikes per FM sweep. Although many features of an FM sweep govern discharge probability (intensity, sweep rate, etc.), this study focuses on how the delay between two FM signals affects spiking activity. The data in Fig. 5 indicate that spike count minima directly encode information about two-glint signals down to a 6-µs separation.
Spike train metrics: spike count
As a function of delay, each two-glint signal has one or more notches in its spectrum. A two-glint stimulus with a spectral notch near BF evokes little or no activity from IC units (Figs. 4 and 5). This is as expected, and Fig. 9 provides a schematic explanation for the responses. Figure 9A shows the spectrogram (left) and spectrum (right) of a two-glint single harmonic FM signal that sweeps from a high to low frequency. Figure 9B depicts how an FM signal at three different two-glint separations would interact with the excitatory tuning area of a neuron (gray area). The first signal (dotted line) has a two-glint separation that is long enough such that the spectral notch is below BF. Thus the cell responds when this signal enters its tuning curve above BF (marked by a small star which corresponds to effective frequency; see following text). The second signal (solid line) is of a shorter two-glint separation and accordingly has a higher frequency spectral notch. In this case, the center of the notch is at BF, and the shoulders of the notch straddle the tuning curve such that no portion of the signal sweeps though the tuning curve at a level high enough to elicit activity. The third signal (dashed line) has an even shorter two-glint delay, and its spectral notch is above BF. After sweeping through the notch frequency, the signal level increases as frequency decreases to the point where it runs over the upper edge of the tuning curve (the lower star).
|
The details of how intensity affects spike counts (Fig. 7) are
explained in Fig. 9C. The two main effects of stimulus level shown in Fig. 7 are a small shift in overall spike count and a change
in the width of the dip near a minima. Figure 9C shows stimuli 1 and 2 from Fig. 9B over several different levels.
Stimulus 1, however, has its notch just below BF so only the broad
high-frequency shoulder of the notch falls on the tuning curve. In this
case, a 30-dB stimulus may occasionally elicit a spike, and a 40- or 50-dB stimulus always will drive the cell. At 30 and 40 dB, stimulus 2 never excites the cell. Not until stimulus 2 is increased to 50 dB does
the frequency-amplitude course of the signal approach the tuning curve
area. Thus the width of the dip in the spike count function will narrow
with an increase in stimulus level (e.g., Fig. 7, A and
B). Because of the changing slope of the notch (steep near
BF, shallow away from BF), it takes a greater level change to get an
equal response from a signal with a notch over BF versus a signal with
a notch that is slightly offset. This effect also appears in a study by
Poon and Brugge (1993), who recorded responses in
auditory nerve fibers. The fibers were driven by broadband continuous
noise filtered so that successive 25-ms segments of the stimulus
contained a single spectral notch with a different frequency. This
stimulus protocol creates, in effect, a spectral notch that moves in
frequency over the duration of the stimulus. Although not explicitly
documented in their paper, they show that increasing the overall noise
amplitude leads to a decrease in the width of the dip in the spike
counts versus notch frequency (their Fig. 4, bottom).
In summary, any given two-glint echo will evoke activity across the tonotopic axis of the IC. A subpopulation of cells in one or more narrow frequency bands would be silenced due to a spectral notch reducing the driving energy in that frequency region (lightest pixels in Fig. 4C along a given vertical band). Alternatively, cells with inhibitory sidebands near the spectral notch(es) would be activated above their normal firing rate (this is not apparent in Fig. 4C because local maxima were present in slightly <1/3 of the population and not necessarily at the stimulus levels used in Fig. 4C).
Sideband inhibition
The data in Fig. 6 indicate that inhibitory mechanisms in the
lower brain stem and IC could be useful for enhancing the contrast of
spectral edges. The increase in spike count above that evoked by the
0-µs condition may be due to the presence of sideband inhibition in
the frequency tuning curves of IC units (Casseday and Covey 1992; Suga 1969a
; Suga and Schlegel
1973
). When a spectral notch falls on an inhibitory region of
the frequency tuning curve, excitatory drive to the cell is augmented
due the release from inhibition. For the neuron in Fig. 6A,
we assume that the elevated response to the 14-µs two-glint stimulus
is due to the spectral notch at 35.7 kHz falling on an inhibitory
region of the frequency tuning curve for the cell (BF 31 kHz,
inhibitory regions of the frequency tuning curves were not measured for
neurons in this study). Each neuron did not undergo an extensive
intensity protocol using two-glint stimuli. Therefore because
inhibitory sidebands often exhibit different thresholds and bandwidths
relative to excitatory regions of the tuning curve, the data in Fig. 6,
B and C, may undersample the larger IC
population, which does express inhibitory sidebands in their frequency
tuning curves. Nevertheless each
in Fig. 6C provides an
estimate of the location of the center frequency of an inhibitory
sideband relative to BF. Figure 6C shows that the spike
count maxima occur for two glint-separations with spectral notches
above or below BF.
Inhibitory sidebands in the auditory system are thought to sharpen
frequency tuning (Suga et al. 1997), create FM
directionality (Fuzessery 1994
; Heil et al.
1992
; Suga and Schlegel 1973
), or participate in
encoding spectral shape (Shamma et al. 1993
). With notched broadband stimuli, neural inhibition can create local "hotspots" of activity on the tonotopic axis, alongside the
position of any spectral notch. The schematic in Fig. 9D
provides a simple model for this phenomenon, using only one inhibitory
sideband, placed on the upper side of the frequency tuning curve. In
the case of the tuning curve shown in Fig. 9D, stimulus 3 evokes the strongest response because it only passes through the
excitatory region and never passes through the inhibitory area for this cell.
For clarity, the models depicted in Fig. 9 leave out many details.
Traditional two-tone frequency tuning curves (like that of the
schematic in Fig. 9D) delineate the regions of the
frequency-amplitude plane that provide inhibitory input with the caveat
that inhibition arrives with the same delay and duration as does
excitation. However, this need not be true. The duration of sideband
inhibition, as revealed by forward masking experiments in anesthetized
cat primary auditory cortex, can last up to hundreds of milliseconds
(Brosch and Schreiner 1997; Calford and
Semple 1995
). The timing of inhibitory inputs has been
estimated from extracellular responses of bat IC units in a study by
Gordon and O'Neill (1998)
. They report latency
differences between inhibitory and excitatory regions of the frequency
tuning curve ranging from 0 to 6 ms. The authors interpret this
dispersed timing of inputs as creating tuning to sweep rate and/or
sweep direction. In the case of two-glint stimuli, adding the dimension
of time to the response area in Fig. 9D makes for a cell
that can pass or reject a variety of FM signals depending on sweep
rate, direction, and spectral shape.
Spike train metrics: first-spike latency
A crucial task of the auditory system is to accurately register
when an acoustic event takes place. A fundamental perceptual dimension
of biosonar, range, is carried initially by the timing of spikes evoked
by the pulse and echo. By using a cross-correlation-like algorithm, the
bat's auditory system computes the delay between activity evoked by
the pulse and echo to estimate target range (see Simmons et al.
1996). Performing computations on these latency differences
requires some form of a delay line followed by coincidence detectors.
In the bat, delay lines with operating ranges in tens of milliseconds
are thought to be created in the IC (Ferragamo et al. 1998; Saitoh and Suga 1995
). The IC projects
mainly to the thalamus, where coincidence detecting cells appear in
high numbers [coincidence detecting cells also appear in the midbrain;
in the nucleus intercollicularis (Dear and Suga 1993
;
Feng et al. 1978
) and in the IC itself (Portfors and Wenstrup 1999
)].
Single units in the NLL can register the timing of acoustic events with
minimum variability of 30 µs in first-spike timing (Covey and
Casseday 1991). This timing information is passed on to the IC
with a corresponding increase in first-spike latency variability. The
first-spike latency of IC cells encodes when energy at a particular
frequency occurs with a variability on the order of hundreds of
microseconds to milliseconds (Ferragamo et al. 1998
;
Pollak et al. 1977
). Because the majority of IC cells spike only one or two times to an FM sweep, the variability and timing
of the first spikes bears on how neurons in the thalamus will integrate
activity from the IC and perform coincidence detection on delayed inputs.
Most of the echoes a bat hears will have one or more notches in their
spectra. Spectral notches retard the timing of these spikes for cells
tuned to and nearby the notch center frequency (see Fig. 4,
F-I, right). Assuming that coincidence detecting cells in the thalamus average a large number of inputs from the IC,
those cells tuned to regions of the spectrum near a notch could provide
biased pulse-echo delay estimates. Behavioral experiments have shown
that small changes in overall signal level can advance or retard the
bat's perceptual estimate of pulse-echo delay (Simmons et al.
1990, 1998
). These results are explained by the
amplitude-latency trading effect, which biases coincidence detector
estimates to earlier or later delay values, depending on echo level
(Simmons et al. 1990
). However, the presence of spectral
notches themselves does not appear to affect the bat's perception of
the delay to the near-glint of a two-glint target (Simmons et
al. 1990
, 1998
), suggesting that knowledge about the notch is
applied to segregating responses from the overall delay estimation.
Latency, effective frequency, and FM sweeps
In general, first-spike latency increases with decreasing RMS
stimulus amplitude (Aitkin et al. 1970; note, however,
the exception of paradoxical latency shift observed first by
Sullivan 1982
). Heil (1997a)
showed that
a single unit's response latency to pure tones more closely correlates
with the second derivative of the onset envelope, not steady-state SPL.
For pure tone stimuli, this means that the duration and shape of signal
rise time dictates response timing (and spike discharge probability)
(Heil 1997b
). How to analyze responses to FM stimuli of
in light of Heil's work (1997a
,b
) is unclear because
the experimenter does not have direct access to the signal envelope
that is driving a neuron. Nevertheless it would be expected that a
unit's latency increases for a signal with a spectral notch near BF
because the amplitude of the signal's spectrum near BF decreases. Thus
first-spike latency should loosely covary with spike probability (see
Fig. 4, E-I).
When driven by FM signals, auditory neurons usually respond to a
frequency above or below BF depending on the direction of the sweep
("effective frequency") (Heil and Irvine 1998). The effective frequency varies as a function of direction, start and stop
frequency, and intensity in relation to the frequency tuning curve of
the cell in question. In general, effective frequency does not vary
with sweep rate but is closely correlated with the position at which it
enters the frequency tuning curve (Heil and Irvine 1998
)
(depicted as small stars in Fig. 9 where descending sweeps 1 and 3 enter the tuning curve area). IC neurons in Eptesicus respond ~2 kHz above BF in response to typical signals such as those
employed in this study (Bodenhamer and Pollak 1981
).
Thus it might be expected that the data points in Fig. 5 should fall to
the right of the curves for notches. However, Fig. 5 plots the stimuli that evoked the minimum spike count; this ensures alignment
of the data points with BF and not effective frequency. Figure
9A predicts that this stimulus will always be centered over
BF (signal 2), whereas responses to signals 1 and 3 would be at
slightly higher frequencies.
LFPs and population activity
Many single-unit recordings showed clear changes in response strength and latency as a function of two-glint separation (e.g., Fig. 4, F-I). The LFP reflects the input to the IC and correlates well with spike-count data (compare Figs. 3B and 4H, left, 2 separate recordings from sites with similar BF). The latency of the peak in the derived LFP correlates with the timing of IC single-unit responses (compare Figs. 3C and 4H, right).
Summarizing the activity of a neural population usually requires
collapsing data recorded from many single units, recorded in different
animals on different days, into one representation. Alternative
approaches use multi-electrode arrays, which can uncover temporal
relationships between neurons not apparent in a serial reconstruction
(e.g., Abeles et al. 1993). We have employed LFPs to
assay auditory brain stem population activity. LFPs reflect summed
electrical activity consisting of action and synaptic potentials, and
interpreting peaks and valleys in a field potential record is only
possible given some information about the underlying anatomic arrangement and functional dynamics of cell bodies and fiber bundles. Because the N4 has been identified as the
"ascending lateral lemniscal evoked potential" (Suga
1969b
), the data in Figs. 2 and 3 provide an idea of the
synchronous input to IC cells. However, IC responses themselves
constitute the IC's output, which is much more dispersed in time.
Summarizing the population response of the IC from serial single-unit
recordings of IC cells naturally emphasizes spike count (see Fig.
4C) due to the widely differing, dispersed latencies and the
heterogeneous response properties of IC cells. However, such a summary
is possible if one keeps track of response latencies (Bodenhamer
and Pollak 1982
; Ferragamo et al. 1998
).
Models
A simple spectral model of echolocation using elements like those depicted in Fig. 9 predicts a sensitivity to two-glint separations of ~5 µs. In the case of standard Eptesicus echolocation signals, as long as there are a reasonable number of neurons tuned to frequencies between 90 and 100 kHz, and their activity can be compared with other frequency bands, then a spike-count "spectral representation" in the IC could code for two-glint echoes down to ~5 or 6 µs (Fig. 5).
Spectral models that do not explicitly incorporate time (spectral
dissimilarity model of Schmidt 1992; filter bank model
of Johnson 1980
) and spectrogram-like models
(Beuter 1980
) have been used to discriminate between
different two-glint target echoes. These models do not describe minimum
resolvable glint separation as compared with a 0-µs delay. The
spectrogram correlation and transformation (SCAT) model proposed by
Saillant et al. (1993)
elaborates on the spectrogram
approach by adding a processing step that transforms spectral shape
information directly into a time estimate of the secondary glints. The
SCAT model gives biased estimates of the first and second components of
a two-glint echo when glint separation is less than ~20 µs
(Peremans and Hallam 1998
; Saillant et al.
1993
). Straightforward analysis of the Eptesicus signals using temporal information alone (cross-correlation) only allows a resolution down to ~6 µs, due to overlapping correlation peaks of the first and second echo.
Conclusions
A perceptual confound may occur for the bat when notches caused by
certain elevations are similar to those caused by closely spaced
reflecting surfaces. For example, the external ear imposes a notch at
~35 kHz on the spectrum of echoes returning from point-like objects
at elevations ~30° below horizontal (Wotton et al.
1995). A 16-µs two-glint echo (double the travel time from
1st to 2nd glint) corresponds to a spacing of 2.75 mm between two
reflecting points in space. If this echo returns from anywhere above
the horizontal, no significant external ear filtering occurs
(Wotton et al. 1995
). This two-glint signal has a
spectral notch at 35.71 kHz (the shoulder of the secondary notch at 107 kHz attenuates high frequencies from 70 to 100 kHz). If the bat attends
primarily to the region of the spectrum containing the first harmonic
(50-20 kHz) (Mogdans and Schnitzler 1990
), then these
two signals (1 from a 2-glint target above the horizontal and the other
from a single-glint target below 0° elevation) may appear very
similar in their spectra, and associating the echo with a particular
elevation or fine-structure would be ambiguous.
Because of the association between spectral notches and two aspects of the spatial images of echolocating bats (2-point resolution, elevation), physiological studies combined with psychophysical data of Eptesicus' auditory system can yield insight into how a sensory system combines spectral and temporal information to build a spatial model of its acoustic environment.
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ACKNOWLEDGMENTS |
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We thank J. H. Casseday and E. Covey for extensive advice during this research.
This work was supported by National Science Foundation Predoctoral Fellowship to M. I. Sanderson and by Office of Naval Research Grant N00014-95-I-1123 and NSF Grant BES-9622297.
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FOOTNOTES |
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Address for reprint requests: M. Sanderson, Box 1953, Dept. of Neuroscience, Brown University, Providence, RI 02912.
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 17 May 1999; accepted in final form 9 November 1999.
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REFERENCES |
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