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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