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Frequency-modulated up-chirps produce larger evokedresponses than down-chirps in the big brown bat auditorybrainstem
Jinhong Luoa)
Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21218,USA
Andrea Megela SimmonsDepartment of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence,Rhode Island 02912, USA
Quincy M. BeckDepartment of Neuroscience, Brown University, Providence, Rhode Island 02912, USA
Silvio Mac�ıasb) and Cynthia F. MossDepartment of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland 21218,USA
James A. Simmonsc)
Department of Neuroscience, Brown University, Providence, Rhode Island 02912, USA
(Received 26 November 2018; revised 12 August 2019; accepted 22 August 2019; published online23 September 2019)
In many mammals, upward-sweeping frequency-modulated (FM) sounds (up-chirps) evoke larger
auditory brainstem responses than downward-sweeping sounds (down-chirps). To determine if simi-
lar effects occur in FM echolocating bats, auditory evoked responses (AERs) in big brown bats in
response to up-chirps and down-chirps at different chirp durations and levels were recorded. Even
though down-chirps are the biologically relevant stimulus for big brown bats, up-chirps typically
evoked larger peaks in the AER, but with some exceptions at the shortest chirp durations. The up-
chirp duration that produced the largest AERs and the greatest differences between up-chirps and
down-chirps varied between individual bats and stimulus levels. Cross-covariance analyses using the
entire AER waveform confirmed that amplitudes were typically larger to up-chirps than down-chirps
at supra-threshold levels, with optimal durations around 0.5–1 ms. Changes in response latencies
with stimulus levels were consistent with previous estimates of amplitude-latency trading. Latencies
tended to decrease with increasing up-chirp duration and increase with increasing down-chirp dura-
tion. The effects of chirp direction on AER waveforms are generally consistent with those seen in
other mammals but with small differences in response patterns that may reflect specializations for
FM echolocation. VC 2019 Acoustical Society of America. https://doi.org/10.1121/1.5126022
[JJF] Pages: 1671–1684
I. INTRODUCTION
Specializations of the central auditory nervous system
that aid in biosonar signal processing have been identified in
numerous bat species (Covey, 2005; K€ossl et al., 2015;
Suga, 2015), both those species that emit constant-frequency
(CF) and also those species that emit frequency-modulated
(FM) echolocation calls. Identifying specializations for bio-
sonar in the bat’s auditory periphery has been more challeng-
ing, largely due to technical difficulties in accessing the
cochlea and eighth nerve in living animals. In several species
of CF bats, such as the horseshoe bat (Rhinolophus ferrume-quinum), the basilar membrane supports an acoustic fovea
that features overrepresentation of those ultrasonic frequen-
cies crucial for Doppler shift compensation (Schnitzler,
1968) and reception of echoes within the bats’ frequency
range of best hearing sensitivity (Vater and K€ossl, 2011;
K€ossl et al., 2015). In FM bats, understanding of cochlear
organization and function is based on recordings of acute
cochlear microphonics (Dalland et al., 1967) and distortion
product otoacoustic emissions (Mac�ıas et al., 2006), or are
inferred from extracellular responses of second-order neu-
rons in the cochlear nucleus (Haplea et al., 1994; Vater and
Siefer, 1995). There is currently no direct evidence for an
acoustic fovea, i.e., an expanded frequency representation, in
FM bats (Mac�ıas et al., 2006). It is, however, noteworthy
that the midbrain inferior colliculus (IC) of the big brown
bat, Eptesicus fuscus, shows an expanded representation of
frequencies between about 20 and 30 kHz, corresponding to
the dominant frequency of their search-phase echolocation
calls (Casseday and Covey, 1992; Ferragamo et al., 1998).
a)Present address: School of Life Sciences, Central China Normal
University, Wuhan, Hubei 430079, China.b)Present address: Biological Sciences, Texas A&M University, College
Station, TX 77843-3258, USA.c)Electronic mail: James_Simmons@brown.edu
J. Acoust. Soc. Am. 146 (3), September 2019 VC 2019 Acoustical Society of America 16710001-4966/2019/146(3)/1671/14/$30.00
This enlarged frequency representation could originate in
cochlear specializations that have yet to be specified.
The basilar membrane in the mammalian cochlea is orga-
nized along a tonotopic gradient such that high sound frequen-
cies produce maximal vibration displacement near the base,
and low frequencies produce maximal displacement near the
apex (von B�ek�esy, 1960). Because sound enters the oval win-
dow at the base, where high frequency tuning is located, the
traveling wave from base to apex varies in speed along the
cochlear partition, being more rapid near the base but slower
near the apex (Robles and Ruggero, 2001). In consequence,
frequency-tuned receptors along the basilar membrane are
associated with different traveling wave delays, from short
delays at high frequencies to long delays at low frequencies.
Modeling these frequency-dependent differential delays is cru-
cial for understanding coding of complex sounds at higher lev-
els of the auditory system.
In species for which direct invasive recordings from the
cochlea are not feasible, it has been proposed that the veloc-
ity of the traveling wave can be inferred indirectly by com-
paring scalp-recorded auditory brainstem responses (ABRs)
to clicks, upward-sweeping FM chirps (up-chirps), and
downward-sweeping FM chirps (down-chirps; Dau et al.,2000; Fobel and Dau, 2004; Elberling et al., 2007; Elberling
et al., 2010). Because up-chirps stimulate the low frequency
then high frequency regions of the basilar membrane in suc-
cession, at appropriate sweep rates they can compensate for
cochlear delays and produce simultaneous displacement
maxima in all regions. This leads to an increased synchrony
of neural activity and thus larger amplitude ABRs compared
to those to clicks or to down-chirps. The “optimal” sweep
rate or duration (the time separation between the lowest and
the highest frequencies) of the up-chirp evoking this
strengthened response is predicted to counteract the velocity
of the traveling wave in the other direction (Elberling et al.,2010). In contrast, clicks and down-chirps evoke responses
with decreased synchrony because these stimuli do not com-
pensate for, but instead enhance, traveling wave dispersion.
Analysis of responses to up-chirps, down-chirps, and clicks
has been used to model basilar membrane frequency disper-
sion and infer traveling wave velocity in guinea pigs (Caviasp.; Shore and Nuttall, 1985), humans (Dau et al., 2000;
Fobel and Dau, 2004; Elberling et al., 2007; Elberling et al.,2010), and two echolocating marine mammals—bottlenose
dolphins (Tursiops truncatus; Finneran et al., 2016; Finneran
et al., 2017) and killer whales (Orcinus orca; Houser et al.,2019). Results from these species agree in showing that up-
chirps produce larger ABRs than down-chirps or clicks.
In this experiment, we adapted this paradigm to deter-
mine if it can be used to estimate the velocity of the traveling
wave in echolocating big brown bats, animals in which no
direct measurements of basilar membrane mechanics have
been reported. The rationale for our experiment is dia-
grammed in Fig. 1. Big brown bats produce downward-
sweeping FM biosonar chirps with two harmonics: FM1
sweeps from approximately 50 to 22 kHz and FM2 sweeps
from 100 to 44 kHz (Surlykke and Moss, 2000). Chirps have
durations of 10–15 ms during the search phase for a target,
shortening to 2–10 ms during tracking of a target, and then
shortening further to 0.3–2 ms as the bat makes its final inter-
ception of the target. Bats emit chirps with durations of 5 ms
or less when actively engaged with a target. Assuming that
the bat’s basilar membrane operates in the same manner as
that of non-specialized mammals (that is, assuming that
there is no acoustic fovea), down-chirps will produce two
harmonically related patterns of excitation that sweep along
the basilar membrane in the same direction as that of the
traveling wave, i.e., from base to apex. Transduction of low
frequencies toward the apex of the basilar membrane, and
thus responses of low frequency tuned eighth nerve fibers,
are delayed relative to those of high frequencies at the base
with the amount of delay depending on sweep rate. This
delayed activation of eighth nerve fibers at lower sound fre-
quencies disperses their responses in time relative to
responses at higher sound frequencies, which decreases syn-
chronous neural firing so that summed electrophysiological
responses are weaker than if there were no delayed activa-
tion (that is, no traveling wave), and all frequencies arrived
at their maximal place of stimulation simultaneously. It is
worth noting that summed electrophysiological potentials
may be reduced in this case because of a decrease in
response synchronization, but that underlying synchronized
responses on a smaller scale may not. In contrast, up-chirps
consist of low frequencies followed by high frequencies,
thus allowing the low frequencies to travel to their maximal
place of excitation toward the apex of the basilar membrane
FIG. 1. Diagram of the rationale for the experiments described here. Stimuli
are up-chirps and down-chirps, each of which contains two harmonics (FM1
from 50 to 20 kHz, FM2 from 100 to 40 kHz; black arrows). The bat’s natu-
ral biosonar emission is a downward-sweeping two-harmonic chirp with
durations around 15 ms when searching for prey and 0.5–5 ms when in the
final stages of prey capture (Surlykke and Moss, 2000). In response to the
down-chirp, traveling waves simultaneously move from base to apex (light
gray rightward-pointing arrows) along the basilar membrane in the two har-
monic frequency bands, effectively delaying transduction of low frequencies
relative to high frequencies in each harmonic. In effect, the basilar mem-
brane creates two downward FM sweeps at a sweep rate corresponding to
the velocity of the traveling wave. In the up-chirp, the direction of the gray
arrows is reversed, showing that low frequencies are presented before high
frequencies. This stimulus compensates for the temporal dispersion along
the basilar membrane. Different sweep rates (chirp durations; illustrated by
the length of the gray arrows) introduce their own dispersion of frequencies,
but titrating different sweep rates should generate auditory evoked responses
(AERs) with different amplitudes. As sweep rates change, response strength
should converge on the highest amplitude for sweeps that most effectively
counteract the dispersion of frequencies due to the traveling wave.
1672 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
before the higher frequencies evoke their maximal place of
excitation toward the base. Up-chirps can thus counteract the
dispersion of frequencies along the basilar membrane, so
that neural responses at all frequencies are brought into
alignment and summate to produce a stronger, more syn-
chronous response.
We recorded auditory evoked responses (AERs) inva-
sively from the IC of four big brown bats in response to up-
chirps and down-chirps of different durations and stimulus
levels. Chirps were synthesized to match the bat’s natural
biosonar chirps in frequency composition and number of har-
monics. The chirp duration that best reflects temporal disper-
sion and traveling wave velocity along the basilar membrane
should elicit maximal synchronous activity and the largest
AER amplitude. Thus, if the big brown bat’s basilar mem-
brane is organized along the general mammalian pattern,
then up-chirps would be expected to evoke larger AERs than
down-chirps at all chirp durations. Instead, if the big brown
bat’s basilar membrane supports an acoustic fovea or another
specialization for echolocation, then we would expect to see
systematic deviations from this general trend.
II. METHODS
Experiments were conducted at both Brown University
(Brown) and Johns Hopkins University (JHU). Methods for
recording AERs differed at the two locations. Below, we first
describe the methods used at the two institutions, and then
provide an explicit comparison.
A. Animals
Four big brown bats (two at Brown and two at JHU) served
as subjects in this study. All bats were wild-caught under collec-
tion permits issued by the states of Rhode Island and Maryland,
respectively. They were socially housed in colony rooms main-
tained at temperatures of 22–24 �C and 40%–60% relative
humidity and on a reversed 12:12 day:night cycle. Bats were fed
live mealworms (Tenebrio larvae) fortified with vitamins and
had free access to water. All four bats emitted biosonar and
communication calls while housed in the laboratory. Although
detailed audiograms were not collected on these animals, their
spontaneous behaviors and physiological thresholds to sounds
indicated that they were not hearing-impaired (Haplea et al.,1994; Simmons et al., 1990; Ferragamo et al., 1998).
Husbandry and experimental protocols were approved by the
Institutional Animal Care and Use Committees of both institu-
tions and comply with federal guidelines.
B. Surgical methods
At Brown, bats (one male, one female; herein denoted
as bats A and B, respectively) were anesthetized by intraper-
itoneal injection of a mixture of medetomidine/midazolam/
fentanyl (MMF; 0.4/4.0/0.04 lg/g body weight). After the
animals reached a surgical plane of anesthesia, they were
lightly wrapped in a cotton towel with chemical hand-
warmers placed onto their backs to maintain body tempera-
tures in the range of 29 �C–32 �C. Head fur was shaved, head
muscles were retracted, and a micro-craniotomy was made
over the IC using a sharpened needle. The anesthetized bats
were placed on a modified stereotaxic device in a sound-
attenuating recording chamber (Industrial Acoustics Corp.,
North Aurora, IL). Supplemental dosages of MMF were
administered as needed during surgery and electrophysiolog-
ical recordings to maintain anesthesia. Fresh chemical hand-
warmers were applied as necessary to maintain body temper-
ature in the range of 29 �C–32 �C. during experiments. At
the end of the experiments, the bats were euthanized by
intraperitoneal injection of Beuthanasia (250 mg/kg).
At JHU, neural recordings were made in the IC from two
awake female big brown bats (bats C and D, respectively).
Several days prior to neural recordings, the bats were anesthe-
tized with isoflurane anesthesia. Part of the skin and the tem-
poral muscles overlying the IC were removed and a small
custom-made head-post was attached to the front midline of
the skull using cyanoacrylate gel. On the day of recording,
the awake bat was placed into a custom-made bat holder, its
head was immobilized via the head-post, and a small craniot-
omy was made above the IC using a surgical microscope. The
bat was secured in its holder and then placed on a platform in
a sound-attenuating chamber (Industrial Acoustics Corp.) for
experimentation.
C. Acoustic stimulation
Up-chirps and down-chirps were generated as digital wav
files using Adobe Audition v1.5 (Adobe Corporation, San
Jose, CA) at a sampling rate of 500 kHz. Chirps consisted of
two exponentially sweeping harmonics, FM1 (20–50 kHz),
and FM2 (40–100 kHz), whose collective frequency range
encompasses those in the big brown bat’s biosonar chirps
(Surlykke and Moss, 2000) and overlaps their most sensitive
range of hearing (from 20 to 60 kHz; Dalland et al., 1967;
Koay et al., 1997). In one experiment, single harmonic chirps
(either FM1 or FM2) were presented in order to bridge com-
parisons with previous ABR studies. Chirp directions were
either up or down, and chirp durations were set at 5, 2, 1, 0.5,
0.3, 0.2, and 0.1 ms (Fig. 2). They had cosine envelopes with
50% rising and falling shapes. This gradual envelope onset
and offset was chosen to prevent an abrupt onset transient
from swamping out the response to the longer duration up-
chirps and down-chirps, and is close to the rise-fall profile in
the bat’s own biosonar chirp. The stimulus set assumes a con-
stant relative traveling wave velocity in the bat’s hearing
range, although it is not known if traveling wave velocity in
bats varies at different locations along the basilar membrane.
All chirp amplitudes are expressed as peak equivalent sound
pressure level (peSPL) re 20 lPa. The same stimulus set was
used at both Brown and JHU.
We chose to stimulate primarily with two harmonic
rather than one harmonic chirps for several reasons. First,
two harmonic chirps duplicate the bat’s own biosonar calls,
and each harmonic plays an important role in echolocation.
The absence of FM2 incrementally reduces the acuity of
echo delay discrimination (Simmons et al., 2004), but
removal of FM1 completely prevents echo delay discrimina-
tion (Moss and Schnitzler, 1989; Stamper et al., 2009; Bates
and Simmons, 2010). Second, a single FM sweep (from
J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al. 1673
100 kHz down to 20 kHz or from 20 kHz up to 100 kHz)
exposes the bat to differences in sound onset due to the greater
loudspeaker roll-off at 70–100 kHz compared to 20–30 kHz
(Fig. 3). These differences affect the amplitude ramp at chirp
onset, making it difficult to determine whether changes in
AERs are due to the onset ramp of sound pressure or to the
direction of the chirp itself. Use of two harmonics ensures that
the sounds’ onset always approaches the frequency region of
the big brown bat’s best hearing sensitivity at about the same
rate. In the case of one harmonic chirps, direction-dependent
variations in the onsets of the up-chirps and down-chirps could
have made it more difficult to assess the role of chirp direction
for determining response strength. The disadvantage of using a
two harmonic chirp is that the responses to these stimuli might
be more difficult to interpret in light of the literature from other
mammals, which presented one harmonic chirps exclusively
(Shore and Nuttall, 1985; Dau et al., 2000; Fobel and Dau,
2004; Elberling et al., 2007; Elberling et al., 2010; Finneran
et al., 2017; Houser et al., 2019). For comparison, we per-
formed one experiment using one harmonic chirps to ascertain
whether one harmonic and two harmonic chirps produce a dif-
ferent pattern of results.
At Brown, acoustic stimuli were broadcast through a
digital data acquisition system (Measurement Computing
DAC 1208HS-2AO and DASYLab software, Measurement
Computing, Norton, MA) running in a Lenovo Thinkpad laptop
computer (Lenovo Group Limited, Beijing). Sound files were
output at a 500 kHz sampling rate into a stereo power amplifier
(Harman Kardon P645, Harman Kardon, Stamford, CT) driving
a high frequency tweeter (JVC Kenwood KFC-XT15ie, JVC
Kenwood, Yokohama, Japan) positioned 40 cm directly in front
of the anesthetized bat. The frequency response of the tweeter
varied 6 dB across the frequency range of 20–60 kHz, decreased
by 3 dB at 80 kHz and by 25 dB at 100 kHz (Fig. 3). Stimulus
levels were calibrated by placing a Br€uel and Kjaer model 4135
( 14
in., Bruel & Kjaer, Naerum, Denmark) condenser micro-
phone at the position occupied by the bat’s head in experiments
(normal incidence, windscreen off). Sounds were presented
through the acoustic system while the microphone’s output,
amplified by 60 dB and filtered from 10 to 100 kHz (Wavetek-
Rockland model 442), was recorded with one analog input
channel of the DAC at a sampling rate of 200 kHz.
At JHU, acoustic stimuli were synthesized and presented
through a National Instrument AD-DA card (PXIe 6358) and
custom-written LabVIEW script (National Instrument, Austin,
TX). For bat C, sound files were output through a custom-
made electrostatic loudspeaker (design by Lee A. Miller,
University of Southern Denmark, Odense; 1 cm diameter) pow-
ered by a Krohn-Hite amplifier (Model 7500; Krohn-Hite,
Brockton, MA). For bat D, sounds were output through an
ultrasonic loudspeaker and amplifier (Ultra Sound Advice,
model S56, Ultra Sound Advice, London). Synchronization
between sound broadcast and neural recording was achieved as
described previously (Mac�ıas et al., 2018).
The spectra of the 5-ms up-chirps delivered to the bats
from all three loudspeakers are shown in Fig. 3. The fre-
quency responses of the loudspeakers caused the largest
deviation from the input electrical signal (black curve) in the
frequency region above 60 kHz.
D. AER recordings and data processing
At Brown, AERs were recorded using a low impedance
(�200 kX) Elgiloy (cobalt-based alloy wire, Elgiloy, Elgin,
IL) microelectrode, sharpened at the tip and insulated along
its shank. The microelectrode was placed onto the exposed
FIG. 2. Spectrograms of FM chirps with different durations and directions, recorded at the location of bats A and B using a calibrated Br€uel and Kjaer model
4135 microphone. These spectrograms show sweep shape, frequency range, and the two-harmonic structures of FM1 and FM2. The critical feature is the up or
down symmetric approach from each chirp’s onset to the big brown bat’s most sensitive hearing regions, which correspond to the middle of the two harmonics.
AER latencies are specified from simultaneous occurrence of 40 kHz in FM1 and 80 kHz in FM2, a compromise chosen to bracket the 60-kHz center of the
spectrum for the bat’s FM biosonar broadcasts.
FIG. 3. Representative stimulus spectra for the three different loudspeakers
used in these experiments (labeled by bat name). The input signal is a 5 ms
up-chirp (solid black curve), and the spectra (gray dashed lines) are normal-
ized to show their spectral level differences across frequencies. The loud-
speakers differed mainly in their output levels from 60 to 100 kHz, which is
above the big brown bat’s most sensitive range of hearing.
1674 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
surface of the IC and then lowered to a depth of 50lm using a
hydraulic microdrive (Narishige PC5N, Narishige International
USA, Amityville, NY). The search stimulus was a linear (one
harmonic, 100–20 kHz) down-chirp of 3 ms duration presented
at 90 dB peSPL at a rate of 10/s for 50 presentations. For data
collection, chirps were presented in two groups depending on
sweep direction. For bat A, all up-chirps were presented first,
followed by all down-chirps; up-chirps were presented in a
decreasing order of durations while down-chirps were pre-
sented in an increasing order of durations. For bat B, all down-
chirps were presented first, followed by all up-chirps; within
these two groups, chirps were presented in a randomized order
of durations. For both bats, chirps were presented at a fixed
descending sound pressure level, from 90 dB to 30 dB peSPL
in steps of 10 or 20 dB. All chirps were presented at a rate of
10/s for 50 presentations. AERs were filtered (0.3–3 kHz;
Wavetek Rockland model 442 band-pass filter) and recorded at
a sampling rate of 20 kHz for a 20-ms epoch triggered by stim-
ulus presentation. Responses were averaged on-line during
sound presentation so that the average evoked waveform was
automatically computed and displayed at the end of a given
series of 50 presentations. Individual and averaged (n¼ 50) tri-
als were saved as digital bin files using the same Measurement
Computing DAC device employed for stimulus generation,
converted into.wav files for individual display to examine the
responses for possible artifacts, and then processed by custom
Matlab R2017a (Mathworks, Natick, MA) scripts. We used a
threshold criterion of three times the noise level (root mean
square of the signal in the first millisecond) and a minimum
temporal distance from a neighboring peak of 0.5 ms for auto-
matic identification of AER peaks. Amplitudes and latencies of
all positive and negative peaks whose amplitude exceeded this
threshold criterion were automatically identified by the Matlab
script. For initial analyses, we quantified the amplitude of the
largest positive peak with latencies in the range of 4–6 ms at
the highest stimulus level presented (90 dB peSPL). This peak
was present in the AERs from both bats in response to up-
chirps (Fig. 4). Latencies were calculated using as a reference
the time at which the instantaneous frequency of the second
harmonic reached 80 kHz, which is simultaneous with the time
at which the instantaneous frequency of the first harmonic
reached 40 kHz. This latency reference was chosen because
80 kHz is above the bat’s most sensitive range of hearing
(Koay et al., 1997) and represents the point above which the
loudspeakers decreased most sharply in their frequency
response; moreover, this reference is consistent with the use of
offset latency in ABR studies in humans (Fobel and Dau,
2004; Elberling et al., 2010). For quantifying peak-to-peak
amplitudes, we used the amplitude of the largest negative peak
preceding the identified positive peak, because this preceding
FIG. 4. AERs recorded from all four bats in response to 1 ms up-chirps (left column) and down-chirps (right column) presented at a stimulus level of 70 dB
peSPL. The horizontal axis is response latency referred to an instantaneous frequency of 80 kHz, and the vertical axis is relative response amplitude. In
response to up-chirps, a positive peak in the latency range of 4–6 ms is present in the AERs from all bats (left column).
J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al. 1675
peak was more prominent and more consistent in AER wave-
forms than the subsequent negative peak. To evaluate overall
response strength of the entire AER waveform without needing
to identify individual peaks (which becomes more difficult as
chirp intensity decreases), we computed a series of unnormal-
ized cross-correlations (i.e., cross-covariances) between the
response evoked by the highest sound pressure and each
response from the highest to the lowest. The first of these was
the autocorrelation of the response to the highest sound pres-
sure with itself. Response amplitude was determined from
peak cross-covariance amplitude, and overall response latency
was determined from the time of this peak.
At JHU, neural recordings were acquired with a 64-
channel Plexon system (Plexon Inc., Dallas, TX) at a sam-
pling rate of 40 kHz/channel. For Bat C, we used a silicon
probe from Neuronexus (25 lm probe thickness, 1� 16
arrangement of recording sites, 50 lm inter-site separation,
and 703 lm2 site area; NeuroNexus Technologies, Inc., Ann
Arbor, MI). For Bat D, we used a tungsten electrode with an
estimated impedance <100 kX. The electrode was advanced
into the IC along the dorsoventral axis up to a depth of
800 lm (Bat C) and to a depth of 565 lm (Bat D) using a
hydraulic microdrive (FHC). Chirps were broadcast at stimu-
lus levels between 80 and 20 dB peSPL in 10 dB steps, for
40 repetitions. The order of stimulus presentation was ran-
domized for both chirp duration and level. Data were batch-
processed with custom-written Matlab (R2015a) scripts.
Neural responses were band-pass filtered (300–3000 Hz)
with the Elliptic filter from the Wave_Clus algorithm
(Quiroga et al., 2004). Then, for each chirp direction and
level, 40-ms-long segments were cut from each trial and
averaged over 40 repetitions. For Bat C in which the neural
data were recorded with a 1� 16 silicon probe, the cross-
trial averaged data were further averaged across the 16
recording channels. An adaptive threshold, two times the
background noise level, based on the same equation from
Quiroga et al. (2004), was applied to identify positive peaks
in the averaged response (corrected for the transmission
time) for a 40 ms time window. Peak amplitudes and laten-
cies were identified, with the reference latency again chosen
as corresponding to the time at which chirp instantaneous
frequency reached 80 kHz. For consistency with the analyses
from Bats A and B, we identified the prominent positive
peak with latencies around 4–6 ms, as this peak was present
in the responses from both bats in response to up-chirps (Fig.
4). For peak-to-peak measurements, we calculated the ampli-
tude of the preceding negative peak; if no negative peak was
present, then we used the baseline response as the reference.
Cross-covariances of the entire AER waveform were calcu-
lated as described above.
E. Comparison of methods
There are methodological differences in experiments
conducted at the two institutions that might complicate com-
parison and interpretation of results. One difference is the
use of anesthesia; bats at Brown were anesthetized with
MMF for AER recordings while bats at JHU were awake.
Anesthesia and potentially lower body temperatures
associated with its use could decrease neural responsiveness
and affect synchronous activity. Three electrode types were
used to record AERs – low impedance Elgiloy electrodes at
Brown, and either silicon probes or low impedance tungsten
electrodes at JHU. These electrodes might have picked up
activity from different neural generators. The Elgiloy and
the low impedance tungsten electrodes are expected to
record synchronous activity in the ascending auditory path-
way up to the IC, while the silicon probe is expected to
record more localized activity within the IC. Three different
loudspeakers were used to present sounds (Fig. 3), although
all had similar characteristics in the frequency region below
60 kHz. Finally, although the same stimulus set was used at
both institutions, stimulus levels varied from 90 to 50 dB
peSPL at Brown and from 80 to 40 dB peSPL at JHU. Given
these methodological differences, we focus on within-animal
stimulus-response comparisons.
III. RESULTS
A. Peak amplitudes and latencies
Figure 4 illustrates representative AERs recorded from
all four bats in response to 1 ms up-chirps (left column) and
down-chirps (right column) at a stimulus level of 70 dB
peSPL. Most clearly in the responses to up-chirps, AERs
from bats A and B contain three to four prominent positive
and negative peaks in the latency range of 2–8 ms.
Responses from bat A contain a prominent positive peak in
the latency range of 2–3 ms, while those from bat B contain
a prominent positive peak in the latency range of 4–6 ms.
There is little activity at latencies longer than 8 ms, sugges-
ting that successive peaks reflect synchronous activity in the
ascending auditory pathway from the eighth nerve up to the
nucleus of the lateral lemniscus (Grinnell, 1963; Haplea
et al., 1994; Ferragamo et al., 1998). AER waveshapes are
similar to those recorded in previous studies in FM bats
(Simmons et al., 1990; Burkard and Moss, 1994; Boku et al.,2015; Smotherman and Bakshi, 2019). AERs from bat C are
noisier than those from bats A and B, likely because they
reflect responses averaged across 16 channels of the silicon
probe. These waveforms contain a negative peak around
4 ms, a positive peak around 6–8 ms, and a later positive
peak around 12 ms, which likely reflects activity from the IC
(Haplea et al., 1994; Ferragamo et al., 1998). Waveforms
from bat D include a single prominent positive peak at
around 5–6 ms, with less prominent negative peaks. In these
responses, peaks with latencies shorter than about 5 ms are
difficult to identify.
For consistency of analysis based on isolation of indi-
vidual peaks, in all bats we quantified the amplitudes of the
positive peak with latencies around 4–6 ms (with the caveat
that these peaks may not represent output of the same neural
generator in all bats). These data are plotted in Fig. 5 as
peak-to-peak amplitudes across chirp directions, durations,
and levels. The pattern of change with chirp direction is sim-
ilar in all four bats—AER amplitudes are typically larger in
response to up-chirps compared to down-chirps, particularly
at the longer chirp durations. Differences in amplitudes
between up-chirps and down-chirps are plotted in Fig. 6.
1676 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
These plots show that at short chirp durations, amplitude dif-
ferences between up-chirps and down-chirps are small, and
in a few cases, down-chirps evoked larger responses than
up-chirps.
Two-tailed paired t-tests were calculated to quantify dif-
ferences in peak-to-peak amplitudes at each chirp duration at
equivalent levels (i.e., amplitudes at 1 ms up compared to
1 ms down at 70 dB peSPL). In the data from bat A (Figs. 5
and 6, top left), amplitudes were significantly larger
(Bonferroni-corrected P value ¼ 0.0027) in response to up-
chirps compared to down-chirps of all chirp durations at a
stimulus level of 90 dB peSPL. At 70 dB peSPL, responses
to the 0.2 ms and 0.1 ms down-chirps were significantly
larger than to the 0.2 ms and 0.1 ms up-chirps, although the
absolute magnitudes of the differences were small. Response
peaks were below our criterion threshold at 50 dB peSPL for
three chirp durations (at 5 ms and 0.1 ms down and 0.2 ms
up), so no statistics could be calculated for up/down compar-
isons. At 0.5 ms duration, the response to the down-chirp
was significantly larger than that to the up-chirp, but again
the absolute magnitude of the difference was small. The up-
chirp duration producing the largest absolute magnitude of
response and the greatest amplitude difference between up-
chirps and down-chirps was 1 ms at 90 and 70 dB peSPL and
2 ms at 50 dB peSPL.
AERs from bat B (Figs. 5 and 6, top right) were similar
in absolute amplitude to those from bat A. As in bat A,
decreases in stimulus level produced a decreased amplitude
of response. Statistical results (Bonferroni-corrected P value
¼ 0.0024) showed that up-chirps produced significantly
larger responses than down-chirps at all stimulus levels and
chirp durations. The up-chirp duration producing the abso-
lute largest response was 1 ms in duration at all stimulus lev-
els. The magnitude of the amplitude difference between up-
chirps and down-chirps was largest at 5 ms duration at 80 dB
peSPL, 1 ms duration at 70 dB peSPL, and 0.5 ms duration at
50 dB peSPL.
AERs from bat C were smaller in peak-to-peak amplitude
than those from bats A and B, likely because these reflect
averaged single neuron activity. In addition, responses did not
decrease consistently with stimulus level; responses were
larger at 60 dB peSPL than at 80 dB peSPL, and responses at
80 and 40 dB peSPL were of similar amplitude. Peak-to-peak
amplitudes were significantly larger (Bonferroni-corrected P
FIG. 5. Mean peak-to-peak amplitude of comparable peaks (positive wave in the latency range 4–6 ms) in the AER in response to up-chirps and down-chirps
of different durations and stimulus levels. Down-chirp durations are plotted as negative numbers and up-chirp durations are plotted as positive numbers on the
x axis. Standard deviations are not plotted for clarity. In all four plots, peak amplitude is typically larger in response to up-chirps compared to down-chirps,
with some exceptions at the shortest chirp durations. Largest peak-to-peak amplitude is evoked by 1 ms up-chirps in the data from bat A (at 90 and 70 dB
peSPL) and bat B (at all stimulus levels). AERs from bat C exhibit largest peak amplitudes to 5 ms up-chirps at all stimulus levels. For bat D, the largest peak-
to-peak amplitude is evoked by 2 ms up-chirps at 80 and 40 dB peSPL, and 1 ms up-chirps at 60 dB peSPL.
J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al. 1677
value ¼ 0.0024) to up-chirps compared to down-chirps at
80 dB peSPL. The magnitude of the difference remained rela-
tively constant at all chirp durations (Fig. 6). At 60 dB peSPL,
peak amplitudes were larger to up-chirps than to down-chirps
at all durations except at 0.1 ms, where responses were larger
to down-chirps. The greatest difference in response amplitude
occurred at 1 ms duration. At 40 dB peSPL, peak amplitudes
were larger to 0.3 ms down-chirps than to up-chirps, but were
larger to up-chirps at all other chirp durations. The up-chirp
duration evoking the largest response was 5 ms at all stimulus
levels. The largest difference in responses to up-chirps com-
pared to down-chirps occurred at 5 ms at 80 dB peSPL and
1 ms at both 60 and 40 dB peSPL.
AERs from bat D were similar to or somewhat larger in
absolute amplitude with those from bats A and B but were
larger than those from bat C (Fig. 5). As with the AERs from
bat C, those from bat D were similar in magnitude at 80 and
60 dB peSPL. Peak amplitudes increased with increasing chirp
duration, and were significantly larger (Bonferroni-corrected Pvalue ¼ 0.0024) in response to up-chirps compared to down-
chirps but with two exceptions. At a stimulus level of 80 dB
peSPL, up-chirps evoked larger responses than down-chirps at
all durations, and the difference in amplitude between up-
chirps and down-chirps increased with duration (Fig. 6). At
60 dB peSPL, up-chirps evoked larger responses than down-
chirps at all durations except 0.1 ms, where responses to down-
chirps were significantly larger. At 40 dB peSPL, down-chirps
evoked larger amplitude responses than up-chirps only at
0.3 ms. The up-chirp duration evoking the largest amplitude
response was 2 ms at 80 dB peSPL, 1 ms at 60 dB peSPL, and
2 ms at 40 dB peSPL. The largest difference in up-chirp com-
pared to down-chirp amplitude occurred at 5 ms at 80 dB
peSPL, 1 ms at 60 dB peSPL, and 2 ms at 40 dB peSPL.
Changes in peak latency with chirp direction, duration,
and level are plotted in Fig. 7. In general, latencies to up-
chirps decreased with increasing chirp duration, while laten-
cies to down-chirps increased with increasing chirp duration.
Latencies to up-chirps and down-chirps are comparable
within the chirp duration range of 0.1–0.5 ms and begin to
deviate at durations of 1 ms and above. Data from the awake
bats C and D show a greater response latency variability
than those from the anesthetized bats A and B with latencies
varying from 2 to 14 ms over a range of chirp durations and
stimulus levels. This wide range of latencies in the data from
bat C particularly is consistent with the wide spread of
response latencies of single neurons in the IC (Haplea et al.,
FIG. 6. Differences in peak-to-peak amplitudes between up-chirps and down-chirps at all chirp durations and levels. Data are calculated as up-chirp amplitude
minus down-chirp amplitude. The dashed line at 0 (y axis) demarcates the point of no amplitude difference. Most data points are above the 0 line, indicating that
up-chirps evoke larger responses than down-chirps at equivalent durations. Exceptions to this trend occur at the shortest chirp durations for three of the four bats.
1678 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
1994; Pollak, 1988; Ferragamo et al., 1998; Covey and
Casseday, 1999), again indicating that the silicon probe used
in that recording picked up considerable single neuron activity.
B. Patterns of change in AER waveforms
Heat maps of the entire AER waveform in response to
two-harmonic up-chirps and down-chirps from all four bats
at four stimulus levels (90, 70, 50, 30 dB peSPL for bats A
and B; 80, 60, 40, 20 dB peSPL for bats C and D) are dis-
played in Fig. 8. Response strength for all identified peaks in
the AERs, rather than only the individual peaks employed in
the above analyses, is illustrated by the color bars (brighter
colors indicate larger amplitude responses, individually
scaled for each plot). These plots demonstrate the variability
between bats in the number and latency of individual peaks
in the AER as well as in AER threshold. AERs decline in
amplitude and increase in latency as stimulus level
decreases, with thresholds at or close to 20 or 30 dB peSPL
across the different bats. The heat maps also highlight the
consistent finding that up-chirps (top half of each individual
plot) evoke stronger responses than down-chirps (bottom
half of each individual plot). The differences in overall
response strength between up-chirps and down-chirps are
clear at the two highest stimulus levels tested for each bat,
but they attenuate close to or at response threshold. For all
bats, responses are comparable at the boundary between up-
chirps and down-chirps (0.1–0.2 ms duration). A similar pat-
tern is observed for one harmonic chirps (Fig. 9).
Using the data from the heat map images, we used the
“xcorr2” function in MATLAB to cross correlate in two dimensions
the responses between stimulus levels. Three amplitude-latency
trading values (ls/dB) between 3 stimulus levels for each bat
and thus 12 values for the 4 bats were obtained (Table I). The
amplitude-latency trading estimations range between 8.1 ls/dB
and 17.5 ls/dB. On average, latencies increased by 11.9 6 3.2
ls/dB from the highest sound pressure level over a 40 dB range.
These values for amplitude-latency trading are within the range
of those reported previously for AERs recorded from the big
brown bat (Simmons et al., 1990; Burkard and Moss, 1994).
To quantify amplitudes of the entire AER waveform across
multiple component peaks for each chirp direction and duration,
responses were paired from the strongest to the weakest sound
level and the cross-covariance function between each pair
computed. Figure 10 compares AERs to two harmonic, 1 ms up-
chirps from bat A at 90 and 70 dB peSPL (left) with their corre-
sponding cross-covariance functions (right). The height of the
peak of each cross-covariance function is used as an index of
overall response magnitude. Figure 11 plots the heights of these
peaks for different durations of up-chirps and down-chirps
recorded from these two bats at different stimulus levels (10 dB
steps for bat A, 20 dB steps for bat B). Two aspects of the results
emerge from this new portrayal of the data. Note, first, that the
FIG. 7. Plots of peak latency by chirp duration and direction for all four bats. Latency is calculated as the time when the instantaneous frequency of the stimu-
lus reached 80 kHz, and plotted as means over all stimulus presentations. Standard deviations are not shown for clarity. UC, up-chirps; response latencies plot-
ted in gray symbols; DC, down-chirps; response latencies plotted in white symbols. Note that the y-axis latency scales differ in each plot. Latency variability
is greatest in the data from bats C and D.
J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al. 1679
0.5 and 1 ms up-chirps evoke the highest response covariance
values. But, while these stimuli yield the strongest response in
terms of the cross-covariance peaks, these peaks decline in
height more steeply for up-chirps than for down-chirps as the
sound level decreases, so that the estimate of response thresh-
olds is not lower for the up-chirps than the down-chirps. The
AERs indicate the same auditory sensitivity to the two stimuli in
spite of the higher amplitude of the responses to the up-chirps.
IV. DISCUSSION
A. Chirp direction affects AER amplitudes in bigbrown bats
Consistent with data reported for humans (Dau et al.,2000) and bottlenose dolphins (Finneran et al., 2017), AERs
from the big brown bat showed enhanced responses to up-
chirps compared to down-chirps at similar durations and
FIG. 8. (Color online) Heat maps of
the entire AER waveforms from all
four bats evoked by two harmonic
chirps of different directions and dura-
tions. In each heat map, durations and
directions range from 5 ms up (top of yaxis) to 0.1 ms up, followed by 0.1 ms
down to 5 ms down (bottom of y axis).
Stimulus levels are 90, 70, 50, and
30 dB peSPL for bats A and B, and 80,
60, 40, and 20 dB peSPL for bats C
and D, as labeled on each individual
plot. Each plot is normalized to its
individual maximum peak height (red)
so as to remove differences in AER
amplitudes across bats and stimuli to
emphasize the pattern of response
across different chirp directions and
durations. The general pattern is the
same in all bats: Responses to up-
chirps are typically stronger than those
to down-chirps.
1680 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
stimulus levels. This major finding is seen in analyses of
changes in amplitudes of individually identified peaks as
well as in analyses based on cross-covariance of the entire
AER waveform. The cross-covariance plots are particularly
powerful because they take into account the entire waveform
and do not depend on the identity or latency of specific indi-
vidual peaks or their neural generators. These plots indicate
that the actual thresholds of responses evoked by up-chirps
and down-chirps were similar, even though responses to up-
chirps are typically of higher amplitudes until sound pressure
levels declined to near threshold. In all bats, latencies to up-
chirps tended to decrease with increasing stimulus level,
while latencies to down-chirps tended to increase with
increasing stimulus level. Moreover, estimates of amplitude-
latency trading across chirp durations are consistent with
previous estimates from FM bats (Pollak, 1988; Simmons
et al., 1990; Burkard and Moss, 1994; Boku et al., 2015).
The basic result of larger AERs to up-chirps compared
to down-chirps was apparent in the data from all four bats at
the highest stimulus levels presented. At lower stimulus lev-
els, peak-to-peak amplitudes in the AERs from three of the
four bats were in a few instances larger to down-chirps than
to up-chirps at the shortest chirp durations. Because of the
small standard deviations of the averaged responses, these
differences were statistically significant although they were
small in magnitude and did not appear consistently at a par-
ticular chirp duration. Similar results were not reported in
other studies comparing ABRs to up-chirps and down-chirps
(Dau et al., 2000; Finneran et al., 2017). Further research
probing responses to short duration chirps and clicks at mul-
tiple stimulus levels in big brown bats is needed to under-
stand these apparent irregularities.
B. Optimal up-chirp duration and estimatesof traveling wave velocity
One aim of this experiment was to identify in the big
brown bat an optimal up-chirp duration that evoked the larg-
est amplitude AER, and so could be used as an estimate of
traveling wave velocity along the basilar membrane.
Although an optimal up-chirp duration could be identified in
AERs from all four bats at each stimulus level, the same
FIG. 9. (Color online) Heat maps for AER responses recorded from bat D
for one harmonic (top) and two harmonic (bottom) chirps at a stimulus level
of 80 dB peSPL. The use of two harmonic chirps that mimic the big brown
bat’s biosonar sounds does not depart from the response pattern seen with
one harmonic chirps.
TABLE I. Amplitude-latency trading relationships for the AER. Values (ls/
dB) were computed with the two-dimensional (2 D) cross correlation
method using the data plotted in the heat maps (Fig. 8), and so do not
depend on identification of a specific peak in the AER. For each bat, three
values were computed using data in the top three rows (the three highest
stimulus levels) of the heat map. The values for Bats A and B were obtained
by cross correlating the heat maps among the 90 dB, 70 dB, and 50 dB
peSPL levels, respectively, resulting in three values shown in the three rows
for each bat (see also Fig. 8). Similarly, the values for Bats C and D were
obtained by cross-correlating the heat maps between the 80 dB, 60 dB, and
40 dB peSPL levels.
Stimulus levels Bat A Bat B Stimulus levels Bat C Bat D
(Bats A and B) (ls/dB) (ls/dB) (bats C and D) (ls/dB) (ls/dB)
90 dB vs 70 dB 11.6 8.5 80 dB vs 60 dB 10 17.5
90 dB vs 50 dB 10.4 10.9 80 dB vs 40 dB 17.5 14.38
70 dB vs 50 dB 8.1 13.5 60 dB vs 40 dB 8.75 11.25
FIG. 10. Explanation of cross-covariance method. (Left) AER waveforms recorded from bat A in response to a 1 ms up-chirp at four stimulus levels (grayscale
lines). Waveforms contain multiple peaks, which complicates finding a single value for amplitude and latency from the individual responses. (Right) Cross-
covariance functions for the same waveforms, which yield a center peak height (cross-covariance peaks, arrowed lines) as the best estimate of response
strength and a peak latency as the most parsimonious estimate of latency (see Table I).
J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al. 1681
optimal duration could not be consistently identified across
all bats. Moreover, the optimal duration did not decrease sys-
tematically with increases in stimulus level, as would be
expected from results in other mammals (Elberling et al.,2010; Finneran et al., 2017). Finally, we constructed chirp
stimuli on the basis of the bat’s natural biosonar chirp with-
out reference to underlying cochlear structure or function,
instead of on estimates of basilar membrane group delays
(Dau et al., 2000; Fobel and Dau, 2004; Elberling et al.,2007; Elberling et al., 2010). These factors lend caution to
estimating traveling wave velocity in the big brown bat on
the basis of our data. The placement of the big brown bat’s
oval window roughly halfway up the first cochlear turn also
suggests that a straightforward translation of traveling wave
velocity to the expected countervailing up-chirp rate may
not be adequate (Ketten et al., 2014).
The 1 ms up-chirp evoked the largest AER in 6 out of
12 experiments (4 bats, 3 stimulus levels) and in the cross-
covariance data from 2 bats, so for heuristic purposes is here
estimated as the “near-optimal” duration. To estimate travel-
ing wave velocity based on this duration, we can take the
length of the basilar membrane in big brown bats to be
9.85 mm, midway between the 8.7 mm estimate based on
microCT scans from one big brown bat (Ketten et al., 2014)
and the 11 mm estimate based on an unpublished magnetic
resonance imaging dataset from another bat (data provided
by O. W. Henson and M. Henson, and discussed in
Rothholtz, 1999). Behavioral audiograms (Dalland et al.,1967; Koay et al., 1997) and neural tuning in the ascending
auditory pathway (Grinnell, 1963; Haplea et al., 1994;
Ferragamo et al., 1998) define the frequency range of hear-
ing in these bats as approximately a decade from 10 to
100 kHz. We assume that the basilar membrane is tonotopi-
cally organized on an exponential frequency scale and the
two harmonics in the FM stimulus chirps sweep down along
comparable lengths of the basilar membrane. If the basilar
membrane is divided in half along its exponential frequency
scale so that each of the two harmonics in the chirps
occupies roughly the same segment length, then FM1
(50–20 kHz) and FM2 (100–40 kHz) will stimulate succes-
sive places along approximately 5 mm of the membrane.
And if the 1 ms up-chirp countervails the basilar membrane’s
intrinsic down-chirp, then these data predict that the velocity
of the traveling wave in the big brown bat would be about
5 mm/ms for each harmonic. However, this estimate assumes
that traveling wave velocity is constant along the basilar
membrane, which is not a reasonable assumption, based on
work with other mammals. For example, Finneran et al.(2016) estimated traveling wave velocity in bottlenose dol-
phins to be 3–4 octave/ms at 14 kHz and 25–34 octave/ms at
108–113 kHz. One study reported that traveling wave veloc-
ity in humans ranges from 5.6 to 78.0 m/s at the base and
from 1.2 to 3.4 m/s at the apex (Donaldson and Ruth, 1993).
In addition, these calculations do not take into account the
unusual placement in big brown bats of stapedial input to the
basilar membrane through the oval window (Ketten et al.,2014). The irregularities in the pattern of AER change with
chirp direction, particularly at the shortest chirp durations,
and in the identity of the optimal up-chirp duration may
reflect some cochlear specializations for FM echolocation. In
particular, due to the apparently unique anatomy of the big
brown bat’s cochlea, there may be a fundamental difference
in traveling wave velocity in the bat’s two harmonic bands.
C. Limitations
Limitations of our study derive both from methodologi-
cal differences between experiments across individual bats,
and methodological differences between these experiments
FIG. 11. Data from bats A (filled symbols) and B (open symbols) showing the peak height of the cross-covariance functions for down-chirps and up-chirps at
all stimulus levels (x axis). Each plot shows a different chirp duration. The curves illustrate the differences in peak response between up-chirps and down-
chirps, particularly at high stimulus levels. As stimulus level decreases, the higher response amplitude for the up-chirps decays more steeply than for the
down-chirps, leading to similar threshold sensitivity for both chirp directions.
1682 J. Acoust. Soc. Am. 146 (3), September 2019 Luo et al.
and those done in other mammals. One important methodo-
logical difference is the use of MMF anesthesia for record-
ings. MMF, like other anesthetics used for recording ABRs
(Pypendop et al., 1999; Antunes et al., 2003; Stronks et al.,2010; Smotherman and Bakshi, 2019), might affect wave-
form amplitude and latency in a manner unrelated to stimu-
lus variables. In general, behavioral audiograms display
greater sensitivity than neural audiograms collected from
anesthetized animals (Heffner et al., 2008; Hoffmann et al.,2008; Linnenschmidt and Wiegrebe, 2019), indicating that
anesthesia might decrease AER responses. There are no pub-
lished data that explicitly compare AER waveforms in the
same animals when anesthetized with MMF and awake.
Related to the possible impact of anesthesia is the impact of
body temperature. The body temperatures of the anesthetized
bats in our study were below the normothermic range (Willis
and Brigham, 2003). There are no published data comparing
AERs in bats held at different body temperatures, so it is
unclear how body temperature affected our results. In other
mammals, decreased body temperatures are associated with
increased ABR latencies and increased ABR amplitudes
(domestic cats, Felidae, Rossi and Britt, 1984; California sea
lions, Zalophus californianus, Mulsow and Reichmuth,
2013). AERs from the awake, presumably normothermic
bats in our study had smaller overall amplitudes than those
from the anesthesized, hypothermic bats, and varied over a
larger latency range. Another complicating factor is the use
of three different electrodes to record AERs. It is clear from
the visualization of response waveforms that AERs recorded
with different electrodes differed in numbers and latencies of
component peaks, likely reflecting different neural genera-
tors. For this reason, we avoid labeling peaks with the
Arabic numbering system used in previous ABR studies
with bats (Burkard and Moss, 1994; Boku et al., 2015). In
this context, cross-covariance analyses become more impor-
tant because they do not rely on identification of any single
peak and make no assumptions about neural generators.
D. FM stimulus selectivity in central auditory neurons
Noting that up-chirps typically evoke larger AERs in the
big brown bat’s ascending auditory pathway than down-
chirps, we now turn to consider FM stimulus selectivity at
the level of the IC, as derived from single neuron recordings.
FM bats emit downward-sweeping biosonar calls, and popu-
lations of single neurons in the IC show a strong bias for
these down-chirps (Suga, 1968; Casseday and Covey, 1992).
For example, in a study of another FM bat, the pallid bat
(Antrozous pallidus), between 19%–31% of neurons in the
IC responded preferentially to downward-sweeping chirps
compared to tones and noise, while no neurons responded
preferentially to upward-sweeping chirps (Fuzessery et al.,2006). These preferences for down-chirps result from asym-
metry in the timing of inhibitory and excitatory inputs to the
IC (Suga, 1968; Fuzessery, 1994; Fuzessery et al., 2006).
Preference for down-chirps in IC single neurons may seem
at odds with the data reported here showing larger AERs to
up-chirps compared to down-chirps. AER peaks recorded in
this study reflect consecutive, summed activity from the
eighth nerve up to the IC, while single neuron responses
reflect local activity within the IC. Neurons in the IC respond
to their best (tonal) frequency within the FM sweep, so their
action potentials are triggered according to sweep parameters
(Bodenhamer and Pollak, 1981; Ferragamo et al., 1998).
Moreover, individual neurons have widely dispersed latencies
(4–20 ms; Haplea et al., 1994; Ferragamo et al., 1998) that
would not simply sum to create the AER. As noted above,
data from bat C were recorded using a 16-channel silicon
probe designed to record single neuron activity. Compared
with the data from the other bats, AER peaks from this bat
are dispersed over a wider latency range, and, particularly at
long latencies, strong responses to down-chirps appear. The
stronger responses to down-chirps recorded with the silicon
probe likely reflect FM selectivity in the IC itself and not in
the ascending auditory pathway. These data are consistent
with previous suggestions (Casseday and Covey, 1992;
Fuzessery, 1994) that the representation of sound frequency
and duration in the cochlea and ascending auditory pathway
is transformed in the IC to support specializations for process-
ing biosonar signals.
ACKNOWLEDGMENTS
This research was supported by the Office of Naval
Research MURI Grant No. N00014-17-1-2736 to J.A.S.,
C.F.M., and A.M.S.J.L. was supported by a long-term
postdoctoral fellowship from the Human Frontier Science
Program (Grant No. LT000279/2016-L). Q.M.B. was
supported by a Brown University Undergraduate Teaching
and Research Assistantship. We thank Abigail Kohler and
Chen Ming for assistance with data analysis, Chao Yu for
making the sound recordings used to characterize the
frequency response of the loudspeakers at JHU, and the
reviewers for their helpful comments.
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