J Neurophysiol
88: 1252–1262, 2002; 10.1152/jn.00077.2002.
Frequency Response Properties of Lateral Line Superficial
Neuromasts in a Vocal Fish, With Evidence for Acoustic Sensitivity
MATTHEW S. WEEG AND ANDREW H. BASS
Department of Neurobiology and Behavior, Cornell University, Ithaca, New York 14853
Received 4 February 2002; accepted in final form 30 May 2002
The mechanosensory lateral line system of fish is used to
detect water motion relative to the surface of the animal and
mediates such varied behaviors as prey detection, predator
avoidance, hydrodynamic imaging, rheotaxis, schooling, and
courtship communication (Coombs and Montgomery 1999).
The lateral line system has two receptor types: superficial
neuromasts, which lie on the surface of the skin, and canal
neuromasts, which are found in subdermal canals that open to
the external environment via a series of pores. Physiological
investigations of the anterior and posterior lateral line nerves,
which innervate neuromasts on the head and trunk respectively, have identified two classes of primary afferents based
on their physiological characteristics in response to a small
vibrating sphere. One class of fibers is sensitive to acceleration
and responds to frequencies ranging from 30 to 200 Hz, while
a second class is sensitive to velocity and responds best to
frequencies ⬍30 Hz (Coombs and Janssen 1990; Kroese and
Schellart 1992). Although theoretical studies suggest that acceleration-sensitive, high-pass units innervate canal neuromasts and velocity-sensitive, low-pass units innervate superficial neuromasts (Denton and Gray 1983; Kalmijn 1988, 1989),
there remains to be a direct demonstration of this functional
dichotomy in fish (but see Kroese et al. 1978 for frogs).
In contrast to most teleost fish with both head and trunk
canal neuromasts, the plainfin midshipman, Porichthys notatus, lacks a trunk canal and instead has several rows of superficial neuromasts (Greene 1899). The posterior lateral line
nerve (PLLn) therefore innervates superficial neuromasts exclusively and offers a unique physiological advantage to examine the responses of this class of lateral line receptors to a
dipole stimulus. The first goal of this study, then, was to
examine the frequency response properties of midshipman
superficial neuromasts and test the hypothesis that they can be
modeled as velocity detectors.
The organizational pattern of the midshipman trunk lateral
line also afforded us the opportunity to examine the heterogeneity of superficial neuromast responses without the presence
of canal neuromasts confounding our interpretations. Indeed,
most studies have focused on the differences between superficial and canal neuromasts with little emphasis placed on the
range of responses within a single class of endorgan (although
see Coombs and Montgomery 1994). Kroese and van Netten
(1989) suggested that structural features could affect neuromast response properties; thus variation in the physical characteristics of hair cells could produce a range of frequency
responses within a single class of neuromast. Therefore a
second major goal of this study was a detailed examination of
the variability of response properties for a receptor population
composed only of superficial neuromasts.
The third major goal of this study was to examine peripheral
lateral line responses in the context of behaviorally relevant
vocalizations. Underwater sound sources produce a near field
component of particle motion that, given the appropriate frequency composition, is theoretically detectable by the lateral
line when the fish is close to the source (Coombs and Montgomery 1999; Denton and Gray 1983; Harris and van Bergeijk
1962; Kalmijn 1988, 1989). Midshipman produce vocaliza-
Address for reprint requests: M. S. Weeg, Dept. of Neurobiology and
Behavior, Seeley G. Mudd Hall, Cornell University, Ithaca, NY 14853-2702
(E-mail: [email protected]).
The costs of publication of this article were defrayed in part by the payment
of page charges. The article must therefore be hereby marked ‘‘advertisement’’
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
INTRODUCTION
1252
0022-3077/02 $5.00 Copyright © 2002 The American Physiological Society
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Weeg, Matthew S., and Andrew H. Bass. Frequency response
properties of lateral line superficial neuromasts in a vocal fish, with
evidence for acoustic sensitivity. J Neurophysiol 88: 1252–1262,
2002; 10.1152/jn.00077.2002. The mechanosensory lateral line of fish
is a hair cell based sensory system that detects water motion using
canal and superficial neuromasts. The trunk lateral line of the plainfin
midshipman fish, Porichthys notatus, only has superficial neuromasts.
The posterior lateral line nerve (PLLn) therefore innervates trunk
superficial neuromasts exclusively and provides the opportunity to
investigate the physiological responses of these receptors without the
confounding influence of canal organs. We recorded single-unit activity from PLLn primary afferents in response to a vibrating sphere
stimulus calibrated to produce an equal velocity across frequencies.
Threshold tuning, isovelocity, and input/output curves were constructed using spike rate and vector strength, a measure of phase
locking of spike times to the stimulus waveform. All units responded
maximally to frequencies of 20 –50 Hz. Units were classified as
low-pass, band-pass, broadly tuned, or complex based on the shapes
of tuning and isovelocity curves between 20 and 100 Hz. A 100 Hz
stimulus caused an increase in spike rate in almost 50%, and significant synchronization in ⬎80%, of all units. Midshipman vocalizations contain significant energy at and below 100 Hz, so these results
demonstrate that the midshipman peripheral lateral line system can
encode these acoustic signals. These results provide the first direct
demonstration that units innervating superficial neuromasts in a teleost fish have heterogeneous frequency response properties, including an upper range of sensitivity that overlaps spectral peaks of
behaviorally relevant acoustic stimuli.
SUPERFICIAL NEUROMAST RESPONSE PROPERTIES
tions with fundamental frequencies near or ⬍100 Hz (Bass et
al. 1999; Brantley and Bass 1994). While peripheral and central auditory neurons encode the frequencies of these vocalizations (Bodnar and Bass 1997, 1999, 2001a,b; McKibben and
Bass 1999, 2001b), it is unknown what role, if any, the lateral
line system might play in the detection and processing of these
sounds. These vocalizations are within the sensitivity range of
the lateral line of other teleosts (Coombs and Montgomery
1999), and presumably produce a considerable near field component capable of stimulating lateral line receptors (Denton
and Gray 1983; Harris and van Bergeijk 1962; Kalmijn 1988,
1989). It is reasonable to hypothesize that midshipman could
be using the lateral line system in addition to the auditory
system to process these acoustic signals when close to the
source.
Portions of this work have been reported in abstract form
(Weeg and Bass 2000b, 2001).
Sixteen adult type I male plainfin midshipman fish, ranging in body
size from 24.6 to 82.9 g (standard length, 13.2–18.4 cm) were included in this study. Midshipman were collected from nest sites in
Tomales Bay, CA, and maintained in artificial seawater tanks at 16°C
on a regular diet of perch minnows and goldfish. All experiments were
conducted under the guidelines of the Cornell University Institutional
Animal Care and Use Committee and the National Institutes of
Health.
Prior to surgery, fish were anesthetized by immersion in 0.2% ethyl
p-amino benzoate (Sigma, St. Louis, MO) in seawater from their
home aquaria until gilling movements ceased. Animals were then
placed in a recessed dish filled with cold seawater. This allowed the
trunk of the animal to remain submerged during surgery, leaving only
the head exposed to air. Apart from the surgical area, all exposed
surfaces were covered with moist tissue. The PLLn was exposed
dorsally, just distal to the point at which it enters the brain stem.
Excess fluid was blotted from the cranial cavity and replaced with an
inert fluid (Fluorinert, 3M, St. Paul, MN). Following surgery, fish
were transferred to a 32 cm diam tank filled with 16°C artificial
seawater and clamped by the head onto a small plastic platform. The
tank was on a vibration isolation table inside an acoustic isolation
chamber (Industrial Acoustics, New York, NY). A small plastic tube
inserted into the mouth of the animal provided a constant stream of
recirculated seawater across the gills. Fish were given intramuscular
injections of pancuronium bromide (0.5 mg/kg body wt, Astra Pharmaceutical Products, Westborough, MA) for immobilization and fentanyl (1.0 mg/kg body wt, Sigma) for analgesia. Condition of the
animal was monitored by watching blood flow in the dorsal vasculature of the brain.
Hydrodynamic stimuli were delivered via a small plastic sphere (12
mm diam) attached by a metal rod to a minishaker (Brüel and Kjær).
Stimuli were synthesized using a customized signal-generation/dataacquisition software package (CASSIE, designed by Julian Vrieslander, Cornell University), attenuated (PA4, Tucker-Davis Technologies, Gainesville, FL) and amplified (NAD 3220PE, NAD
Electronics, Boston, MA) before being delivered through the minishaker. The minishaker was placed in a large metal clamp inserted
into the chuck of a drill press such that the sphere’s axis of motion was
in the horizontal plane, parallel to the long axis of the fish. The sphere
was positioned at the midpoint of both the rostrocaudal and dorsoventral planes of the fish, 15–25 mm from the body surface. The drill
press was isolated from the vibration isolation table on which the preparation was positioned to minimize transfer of substrate vibrations.
Movement of the sphere was equalized to produce a constant
maximum source velocity across the range of stimulation frequencies
J Neurophysiol • VOL
(20 –200 Hz). Peak-to-peak displacement of the sphere was measured
under a dissecting microscope (resolution, 15 ␮m) and adjusted to
produce a constant velocity across frequencies according to the flow
field equations for a dipolar sound source (Kalmijn 1988). To ensure
that velocity measured at the source was an adequate predictor of
particle velocity in the flow field (i.e., that the experimental tank did
not greatly distort flow fields), we measured the sound pressure at
various points around the source using a mini-hydrophone (4130,
Brüel and Kjær) whose output was fed into a spectrum analyzer
(SR780, Stanford Research Systems, Sunnyvale, CA). Measured pressure at these points was compared with predicted pressure, which was
calculated using flow field equations for a dipolar sound source in an
unbounded medium (Kalmijn 1988). Measured and predicted pressure
showed similar gradient patterns, so we feel confident that equalizing
for velocity at the source translated into equalized particle velocity in
the stimulus flow field.
Single-unit extracellular potentials were recorded from the posterior lateral line nerve using glass microelectrodes filled with 4M NaCl.
Electrodes were visually placed on the surface of the nerve and
advanced using an hydraulic microdrive (Kopf Instruments, Tujunga,
CA). Signals were amplified, filtered through 300-5000 Hz (A-M
Systems 1700, A-M Systems, Everett, WA) and digitized onto a
Macintosh Centris running CASSIE. Visually identified single units
were discriminated using a pattern matching algorithm within
CASSIE.
On isolation of a single unit showing a robust response to a pulsed
40 Hz search stimulus, the approximate location of the receptive
neuromast was identified by monitoring spike rate in response to a
water jet directed against the side of the fish. After localization of the
receptive neuromast, the following stimulus protocol was initiated.
Stimuli consisted of 10 repetitions of a 700 ms tone burst at 1 s
intervals. Threshold was determined for each unit and a random series
of 20 to 100 Hz tones in 10 Hz steps was presented in an ascending
series of 6 dB increments from threshold level to a level 30 dB above
threshold. The stimulus level was then decreased by 3 dB, and a
descending series of 6 dB increments was presented. At each stimulus
level, spontaneous activity was measured by recording 10 1s repetitions in which no signal was sent to the minishaker. If the unit was still
viable, data at additional stimulus levels, as well as at frequencies
ⱕ200 Hz were collected.
Neural responses were measured in terms of average evoked spike
rate and vector strength of synchronization (VS). Spike rate was
measured for each repetition and averaged across the 10 repetitions
for each stimulus. Threshold was determined as the level at which
evoked spike rates were ⬎2 SDs above spontaneous levels. VS is a
measure of phase locking of the neural response to the stimulus
waveform (Goldberg and Brown 1969). Typically, all repetitions of a
given stimulus are collapsed into a single cycle’s worth of time, and
a single VS value (R) is measured. The Rayleigh statistic (Z) is then
calculated using this VS and the total number of spikes (n) occurring
throughout all stimulus repetitions (Z ⫽ R2*n) (Batschelet 1981). We
measured VS for each repetition individually, using the number of
spikes in each repetition to calculate Z. Threshold was arbitrarily
defined as the stimulus level at which Z ⬎ 3 (which corresponds to
P ⬍ 0.05) for at least 50% of the repetitions. We chose this criterion
because it corresponded to a robust response that was well below
saturation, and proved to be more conservative than traditional methods of calculating the Rayleigh statistic.
Phase of the neural response was calculated from the period histograms and expressed relative to the displacement of the sphere. Sphere
displacement was measured using a dissecting microscope and a
strobe that could be adjusted to match the stimulus frequency. The
timing of the strobe that corresponded to the maximum displacement
of the sphere was recorded by the data-acquisition software, and the
difference between the peak in the stimulus waveform from the
computer and the peak displacement of the sphere was measured. This
procedure was done at each stimulus frequency to account for fre-
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METHODS
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M. S. WEEG AND A. H. BASS
quency dependent delays between the signal from the computer and
the output of the sphere.
RESULTS
The data reported come from 81 single units that were held
long enough to obtain nearly complete data sets consisting of
threshold tuning curves and isovelocity curves. Some units
were lost before complete data sets could be obtained, which
accounts for the differences in sample sizes.
Spontaneous activity
FIG. 2. Raster plots (top left), peristimulus time (PST) histograms (bottom
left), and period histograms (right) of evoked activity in response to 20 Hz (A),
40 Hz (B), or 100 Hz (C) at a stimulus level of 30 dB above best sensitivity.
Primary afferents responded to single tones with nonadapting, phase-locked
activity throughout the duration of stimulus presentation. At high stimulus
levels, units often responded with a burst of activity at stimulus onset (e.g., C).
peak. Although variable ISI distributions were only seen in
cells with low spontaneous activity (1.8 –18.2 spikes/s), spontaneous rates for bursting (26.4 –104.8 spikes/s), irregular
(19.5– 81.2 spikes/s), and regular (35.9 –55.5 spikes/s) ISI distributions were overlapping.
Spike rate sensitivity
FIG. 1. Spontaneous activity of primary afferents in the posterior lateral
line nerve. A: distribution of spontaneous rates. Units were classified as either
bursting (B), irregular (C), regular (D), or variable (E) based on the shape of
interspike interval histograms of spontaneous activity.
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All units showed nonadapting, phase locked responses
throughout the duration of the stimulus (Fig. 2). At lower
stimulus frequencies, most units fired multiple spikes per cycle
of the stimulus waveform as can be seen in the multiple peaks
of the period histograms in Fig. 2, A (in response to a 20 Hz
stimulus) and B (40 Hz stimulus). At higher frequencies, units
typically fired one or fewer spikes per cycle (Fig. 2C, 100 Hz
stimulus). Peristimulus time (PST) histograms showed that
units responded with sustained firing throughout the duration
of the stimulus, although many units fired a burst of spikes at
stimulus onset followed by a tonic response (e.g., Fig. 2C). We
did not examine this onset response systematically, but it
tended to occur more often at frequencies above 50 Hz than
below, and at high stimulus amplitudes.
The phase of the neural response relative to the phase of the
vibrating sphere was extremely variable both between units at
a given frequency and between frequencies within a given unit
(Fig. 3). For a given unit, response phase shifted as frequency
increased by an average of ⫹62.5° ⫾ 16.1°/10 Hz (mean ⫾
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The distribution of mean spontaneous rates ranged from 1.8
to 104.8 spikes/s (Fig. 1A). Interspike interval (ISI) histograms
of spontaneous activity revealed four patterns of resting discharge activity (Fig. 1, B–E). Bursting units (71.6%; Fig. 1B)
were most commonly seen, with ISI distributions characterized
by a sharp peak at low ISIs, representing ISIs within bursts,
followed by a smaller secondary peak at higher ISIs, representing interburst intervals. Irregular units (12.3%; Fig. 1C)
showed a peak at low ISIs followed by a broad tail out to
higher ISIs. Regular units (3.7%; Fig. 1D) were characterized
by a normal distribution of ISIs, while variable units (12.3%;
Fig. 1E) had a broad distribution of ISIs that lacked a robust
SUPERFICIAL NEUROMAST RESPONSE PROPERTIES
FIG.
3. Phase of the neural response of 3 primary afferents (represented by
●, or ƒ) relative to displacement of the sphere. All units showed a characteristic phase shift with increasing frequencies, but units did not respond at a
consistent phase at any given frequency. Phase of response was not correlated
with the location of the receptive neuromast on the fish.
䊐,
Threshold tuning curves
Threshold tuning curves were constructed based on both
spike rate (n ⫽ 60) and VS (n ⫽ 80) criteria (Fig. 4). Both
measures resulted in similarly shaped tuning curves. However,
spike rate curves were often sharper than VS curves, whereas
VS thresholds were usually lower than spike rate thresholds.
Best sensitivity was defined as the lowest stimulus level that
elicited a threshold response. Characteristic frequency (CF),
defined as the frequency at best sensitivity, ranged from 20 to
50 Hz for both VS and spike rate (Fig. 4A). To provide an
objective measure of tuning curve shape, we categorized tuning curves based on whether bandwidth between 20 and 100
Hz could be measured at 12 dB above best sensitivity. Units for
which a 12 dB bandwidth could be measured were classified as
band-pass. Units for which a 12 dB bandwidth could not be
measured were classified as either low-pass or broadly tuned,
depending on the unit’s sensitivity to 100 Hz stimuli. Some
units exhibited large notches in the tuning curve and were
classified as complex. Using these criteria, 66.7% of the units
based on spike rate and 56.3% of the units based on VS were
categorized as low-pass (Fig. 4B), 23.3% (spike rate) and
13.8% (VS) were categorized as band-pass (Fig. 4C), 5.0%
(spike rate) and 21.3% (VS) were categorized as broadly tuned
(Fig. 4D), and 5.0% (spike rate) and 8.8% (VS) were categorized as complex (Fig. 4E). There was no systematic relationship between tuning curve category and location of the receptive neuromast.
Of 59 units for which both spike rate and VS tuning curves
were constructed, 62.7% (n ⫽ 36) of the units were categorized
the same for both measures, while 37.3% (n ⫽ 23) of the units
were categorized differently. Of those units that were categorized differently, the shapes of spike rate and VS tuning curves
were generally fairly similar, and the differences in categorization were largely due to spike rate tuning curves being
sharper than VS curves (Fig. 4F). Both the low- and highfrequency slopes of spike rate tuning curves were steeper than
those of VS tuning curves (low: P ⫽ 0.0061; high: P ⫽ 0.0035,
Mann-Whitney U test). This resulted in an increased probability that the low, high, or both ends of the spike rate tuning
curves would be ⬎12 dB above best sensitivity, while those of
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the VS tuning curves were more likely to be ⬍12 dB above
best sensitivity (Fig. 4F).
Low- and high-frequency slopes were measured for lowpass, band-pass, and broadly tuned tuning curves. Low-frequency slopes were measured between 20 Hz and characteristic
frequency, and high-frequency slopes were measured between
characteristic frequency and 100 Hz or, if the unit was not
responsive to 100 Hz, the highest frequency that elicited a
threshold response. Both low- and high-frequency slopes were
highly variable and widely distributed (Table 1). Low-pass and
broadly tuned units had shallower low frequency slopes than
band-pass units, and broadly tuned units had shallower highfrequency slopes than low-pass and band-pass units. Tuning
curve slopes were independent of neuromast location.
Isovelocity curves
Spike rate (n ⫽ 71) and VS (n ⫽ 80) isovelocity curves were
constructed at all stimulus levels above best sensitivity, but
only curves at 12 dB above best sensitivity (rate: n ⫽ 55; VS:
n ⫽ 78) were used to determine best excitable frequency and
response categories (Fig. 5). Best excitable frequency, defined
as the frequency that elicited the greatest response, ranged
from 20 to 50 Hz for spike rate and 20 to 80 Hz for VS (Fig.
5A). We again recognized four categories based on the shapes
of isovelocity curves and whether the response at 20 and 100
Hz fell ⬍70% of the response at best excitable frequency.
Using spike rate as a measure, isovelocity curves were categorized as either low-pass (43.6%; Fig. 5, B and D) or bandpass (56.4%; Fig. 5, C, E, and F). Using VS as a measure,
isovelocity curves were categorized as low-pass (44.9%; Fig.
5, B and F), band-pass (23.1%; Fig. 5C), broadly tuned (17.9%;
Fig. 5D), or complex (14.1%; Fig. 5E). As with threshold
tuning curves, there was no relationship between isovelocity
curve category and the location of the receptive neuromast.
The shapes of isovelocity curves varied slightly with stimulus intensity. Spike rate isovelocity curves generally became
more sharply tuned as stimulus intensity was increased, while
VS curves became more broadly tuned. Best excitable frequency remained constant as stimulus intensity increased for
40.8% of spike rate curves and 22.5% of VS curves. The
remaining units showed either a consistent increase (spike rate,
22.5%; VS, 21.3%), consistent decrease (spike rate, 8.5%; VS,
6.3%), or random fluctuations (spike rate, 28.2%; VS, 50.0%)
in best excitable frequency as stimulus intensity increased. The
maximum variation was 20 Hz for all spike rate curves and
88.8% of VS curves. Variation ranged from 30 to 80 Hz for the
remaining VS curves, although this extreme variation was
related to either very high stimulus levels or low spike rates,
which tended to inflate VS values.
Of 52 units for which both spike rate and VS isovelocity
curves were constructed, 44.2% were categorized the same for
both measures while 55.8% were categorized differently. This
high percentage of units that were categorized differently was
largely due to the fact that none of the spike rate curves were
categorized as broadly tuned or complex. Of the 29 units that
were categorized differently, 21 had broadly tuned or complex
VS isovelocity curves (Fig. 5, D and E). The eight remaining
units were all classified as band-pass by spike rate criteria and
low-pass by VS criteria (Fig. 5F). Like threshold tuning
curves, these were generally similar in overall shape, although
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SD), but different units did not respond at a consistent phase at
any given frequency. Response phase for a given unit remained
fairly constant across stimulus levels and did not show a
systematic relationship with receptor location.
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FIG. 4. Representative threshold tuning curves constructed using spike rate and vector strength (VS) criteria. A: characteristic
frequency, defined as the frequency that elicited a threshold response at the lowest stimulus level, ranged from 20 to 50 Hz for both
criteria. Four categories of units were identified based on tuning curve shape: low-pass units (B), band-pass units (C), broadly tuned
units (D), and complex units (E). To facilitate comparisons between units, stimulus levels for all tuning curves were normalized
such that 0 dB corresponds to best sensitivity. Several units were classified differently depending on response criteria (F). This was
mainly due to spike rate tuning curves being slightly sharper than vector strength tuning curves.
the low-frequency slopes of the spike rate curves were steeper
than those of the VS curves.
Vector strength is sensitive to the number of spikes occurring during each cycle of the stimulus waveform. Multiple
TABLE
spikes per cycle broaden the distribution of spike times, thus
lowering VS values. Therefore low VS values could arise from
the neuron not encoding the stimulus or from the neuron firing
more than one spike per cycle. This distinction becomes par-
1. Mean tuning curve slopes from primary afferents in the posterior lateral line nerve
Low pass
Band pass
Broad
Low-Frequency Rate Slope
Low-Frequency VS Slope
High-Frequency Rate Slope
High-Frequency VS Slope
⫺6.0 ⫾ 4.3
⫺16.6 ⫾ 9.6
⫺8.3 ⫾ 3.3
⫺3.6 ⫾ 5.5
⫺17.9 ⫾ 8.1
⫺5.4 ⫾ 4.2
13.9 ⫾ 6.5
18.5 ⫾ 4.9
7.1 ⫾ 2.3
12.3 ⫾ 4.1
18.2 ⫾ 9.1
4.6 ⫾ 2.8
Values are means ⫾ SD. VS, vector strength.
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SUPERFICIAL NEUROMAST RESPONSE PROPERTIES
1257
ticularly important when considering low-pass versus bandpass units in which the differences between filter shapes arose
primarily from band-pass units having low VS values at 20 Hz
(Fig. 5). Visual inspection of raster plots suggested that bandpass units did not encode a 20 Hz stimulus as well as low-pass
units (Fig. 6, A and D). This was reflected in both PST (Fig. 6,
B and E) and period (Fig. 6, C and F) histograms. In general,
low-pass units fired multiple spikes per cycle within a limited
range of the stimulus waveform, whereas band-pass units fired
spikes throughout the entire waveform. In addition, there was
not a significant difference between the number of spikes per
cycle at 20 Hz for low-pass and band-pass units (P ⫽ 0.1066,
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Mann-Whitney U test). Thus the difference in 20 Hz VS
between low- and band-pass units appeared to be a function of
the degree of phase locking rather than band-pass units firing
more spikes per cycle than low-pass units.
Input/output curves
Input/output (I/O) curves were constructed for stimulus levels up to at least 18 dB above best sensitivity for 79 units (rate
n ⫽ 43; VS n ⫽ 78). I/O functions showed the greatest
differences between spike rate and vector strength measures.
Of 43 units, 53.5% did not show an increase in spike rate to a
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FIG. 5. Representative spike rate (left axis in B–F) and vector strength (VS, right axis in B–F) isovelocity curves constructed
at stimulus levels of 12 dB above best sensitivity. A: best excitable frequency, defined as the frequency that elicited the strongest
neural response, ranged from 20 to 80 Hz. Spike rate isovelocity curves were categorized as either low-pass (B and D) or band-pass
(C, E, and F), while vector strength curves were classified as either low-pass (B and F), band-pass (C), broadly tuned (D), or
complex (E). The majority of units were categorized differently because no spike rate curves were categorized as broadly tuned or
complex (D and E). The remainder of differently classified units were similar in overall shape, but spike rate curves had a steeper
low frequency slope than vector strength curves (F). E on left axis, spontaneous rate.
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M. S. WEEG AND A. H. BASS
100 Hz tone as stimulus intensity was increased (Fig. 7A),
while 46.5% did (Fig. 7C). In contrast, only 17.9% of units did
not show a significant increase in VS (Fig. 7B), while 82.1% of
units did significantly synchronize to a 100 Hz tone as stimulus
level was increased (Fig. 7D). The magnitude of the response
was also more robust for VS than for spike rate. While spike
rate for most units could be driven just above threshold levels
at 100 Hz, maximum VS at 100 Hz was often as high as that
at 20 and 40 Hz.
DISCUSSION
The PLLn of the plainfin midshipman innervates only superficial neuromasts, which provided us the unique opportunity
to investigate the response properties of this class of lateral line
receptors in a teleost fish. Specifically, we tested the hypothesis
that primary afferents innervating superficial neuromasts respond as velocity detectors, examined the variability in superficial neuromast responses, and asked whether the midshipman
lateral line system is capable of encoding behaviorally relevant
vocalizations. We have shown that the midshipman lateral line
system shares several basic features with that of other teleosts,
including best responses to frequencies at or ⬍50 Hz, and
sustained responses that usually consisted of an increase in
spike rate and synchronization of spike times to the stimulus
waveform. However, based on phase data and the slopes of
threshold tuning curves, we conclude that the responses of
primary afferents innervating midshipman superficial neuromasts do not match the predicted responses of a particle velocity detector. Midshipman superficial neuromasts also exhibit a large degree of variation in their frequency response
J Neurophysiol • VOL
properties, which has not been previously documented. This
includes sensitivity to frequencies ⬎100 Hz, suggesting that
these receptors are capable of detecting species specific vocalizations.
Spontaneous activity
Patterns of spontaneous activity were similar to those described for other teleosts (Coombs and Janssen 1990; Kroese
and Schellart 1992; Montgomery et al. 1988; Munz 1985;
Tricas and Highstein 1991; Wubbels et al. 1990). Unlike previous studies, we did not encounter silent units. However,
variable units typically had low spontaneous activity and may
be analogous to these silent units. There was no relationship
between spontaneous activity and the shape of threshold tuning
and isovelocity curves. Because the location of the sphere was
fixed and distance between the sphere and receptive neuromast
varied between units, we were unable to compare sensitivity,
which is often correlated with spontaneous rate (see Popper
and Fay 1999), between units.
Spike rate vs. vector strength
Midshipman superficial neuromasts exhibited sustained,
nonadapting responses to a vibrating sphere that consisted of
an increase in spike rate, synchronization of spike times to the
stimulus waveform, or, more commonly, a combination of the
two. These types of responses are a common feature of lateral
line primary afferents (Coombs and Janssen 1990; Coombs et
al. 1998; Mogdans and Bleckmann 1999; Mohr and Bleckmann 1998; Montgomery et al. 1988; Munz 1985; Wubbels
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FIG. 6. Raster plots (A and D), PST histograms (B and E), and period histograms (C and F) of evoked activity in response to
a 20 Hz stimulus at 12 dB above best sensitivity for a low-pass (A–C), and a band-pass (D–F) unit. Low-pass units fired phase
locked action potentials only during one-half of the stimulus cycle, while band-pass units fired throughout the stimulus cycle,
regardless of stimulus phase.
SUPERFICIAL NEUROMAST RESPONSE PROPERTIES
1259
1992). The sustained response provides information about
stimulus duration, while the degree of synchronization and
magnitude of the spike rate increase encode stimulus amplitude. Synchronization also provides a temporal code of frequency and thus a mechanism for frequency analysis at the
periphery.
We analyzed the responses of superficial neuromasts in
terms of spike rate and VS, as both measures likely play an
important role in encoding sensory information. Vector
strength usually started increasing at stimulus levels lower than
those that caused an increase in spike rate, and saturated at
lower stimulus levels than spike rate. For most units, we did
not present stimulus amplitudes large enough to cause saturated spike rate responses, so we were unable to compare
dynamic ranges of spike rate versus VS. However, the combination of the two measures clearly increased the overall dynamic range of the system, increasing the amplitude range over
which the neuromast was responsive. Moreover, at stimulus
levels that caused VS saturation, an increase in spike rate
caused an increase in the overall depth of modulation of neural
activity. Similar interplay between spike rate and VS is seen in
the midshipman VIIIth nerve (McKibben and Bass 1999). Thus
although we have presented data in terms of spike rate and VS
as separate measures, neural encoding probably employs a
combination of the two to reconstruct the features of the
stimulus waveform.
J Neurophysiol • VOL
Velocity detection
Superficial neuromasts are modeled as particle velocity detectors (Kalmijn 1988, 1989), which produces a set of predictions regarding their response to a vibrating sphere. A velocity
detector is expected to respond at a 90° phase lead relative to
the displacement maximum of the source. We found that the
response phase of midshipman superficial neuromasts at any
given frequency was highly variable across units. This contrasts with previous studies showing that the response phase of
superficial neuromasts is stable across units, suggesting either
velocity (Kroese and Schellart 1992; Kroese et al. 1978) or
acceleration (Kroese et al. 1980; Munz et al. 1984) sensitivity.
The difference in results is unclear, although Kroese and Schellart (1992) and Kroese et al. (1978) showed 90° phase leads
only at low stimulus amplitudes within the linear response
range of the system and at frequencies ⬍10 Hz. The variability
in response phase may have arisen from filtering mechanisms
(i.e., synaptic delays and neural propagation delays) that would
introduce a phase shift dependent on neuromast location. However, delay-induced phase shifts should occur in all species, yet
did not obscure the phase response in trout, which are larger
than midshipman and have receptors distributed over an even
greater area (Kroese and Schellart 1992). Phase shifts therefore
cannot solely explain the variability in our study. Regardless, it
is doubtful that neural response phase would prove useful to
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FIG. 7. Input/output (I/O) curves for 2 representative units (A and B, and C and D) in response to 20 Hz (●), 40 Hz (1), 100
Hz (‚), and 150 Hz ({). More than half of the units did not increase spike rate in response to a 100 Hz tone (A), while the remainder
showed moderate increases in spike rate above spontaneous levels (C). In contrast, ⬍20% of the units did not significantly
synchronize to a 100 Hz tone (B), while the rest showed strong synchronization, often reaching values as high as those obtained
in response to a 20 or 40 Hz stimulus (D). E on left axis, spontaneous rate (A and C). Stimulus level is normalized such that 0 dB
is equal to the lowest stimulus amplitude presented for a given unit.
1260
M. S. WEEG AND A. H. BASS
Frequency sensitivity
While characteristic frequency and best excitable frequency
of midshipman superficial neuromasts fell within a relatively
narrow frequency range, there was considerable variation in
the shapes of threshold tuning curves and isovelocity curves.
Comparisons with previous studies are difficult because most
have reported physiological responses in terms of displacement
J Neurophysiol • VOL
or acceleration rather than velocity, as we have done in this
study. The shapes of frequency response functions are dependent on the stimulus parameter to which data are referred
(Kalmijn 1988, 1989), and translating frequency response
curves between different components of the stimulus is not
appropriate if the responses are not linear (see Montgomery et
al. 1988). Low-pass units were similar in shape to velocitysensitive units in the antarctic fish, Trematomus bernacchii
(Coombs and Montgomery 1994; Montgomery and Coombs
1992), while band-pass units were more similar in shape to
acceleration sensitive units reported in these studies (see Fig. 7
in Coombs and Montgomery 1994). Although the low-frequency parts of the curves are similar, antarctic fish have much
steeper high-frequency slopes than midshipman. This might be
attributed to the much colder temperatures inhabited by Trematomus.
Complex units were characterized by a deep notch in either
the threshold tuning or isovelocity curves. In most units, this
notch occurred at 70 Hz, and the width of the notch was
variable. It is unclear whether this notch was biological in
nature or an artifact of our experimental setup. The fact that the
notch usually occurred at 70 Hz suggests that it may have
resulted from nonuniformities in the stimulus field at 70 Hz.
However, the majority of units did not contain a notch, implying that such nonuniformities only existed at particular points
in space around the stimulus. We were unable to map out the
velocity field around the stimulus, so we cannot determine if
this was the case.
Broadly tuned units were particularly interesting in that they
exhibited an almost flat response across a wider frequency
range than has been reported for superficial neuromasts in other
species. These responses extended into the frequency range
typically thought of as the domain of canal neuromasts
(Coombs and Montgomery 1999). Coombs and Montgomery
(1992) hypothesized that the high-frequency slope of canal
neuromasts is determined by hair cell and primary afferent
membrane time constants, which steepen the slope relative to
predicted responses. Higher-order filtering mechanisms may
also function in superficial neuromasts and might account for
the broad tuning of some units. In fact, the loss of a trunk canal
may have favored adaptations to increase the frequency range
of superficial neuromasts to compensate for what would otherwise result in a loss in functionality. It would be interesting
to examine the response properties of superficial neuromasts in
other teleosts that lack a trunk canal to see whether this is a
general phenomenon.
The frequency response properties of midshipman superficial neuromasts show a higher degree of variation than has
been previously reported for this class of lateral line receptors
(see Coombs and Montgomery 1994). Midshipman superficial
neuromasts have two dermal papillae on either side of the
cupula (Greene 1899). Within a given line of neuromasts, the
papillae are oriented along the same axis, although the size and
overall structure is variable both between and within lines.
These papillae may form a rudimentary canal (see also Coombs
et al. 1988) with some of the same filtering effects of intact
canals (Denton and Gray 1988) and could account for the
broadening of tuning exhibited by some primary afferents.
Alternatively, the response variation might arise from physical
variation in hair cells between neuromasts. Kroese and van
Netten (1989) suggested that the frequency responses of a
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midshipman in this situation, given the large variation in response phase across a spatially distributed array of receptors.
Along with the 90° phase lead, velocity detectors are predicted to respond to a constant velocity stimulus with a slope
of 0 dB/octave. While some low-pass units had low-frequency
threshold tuning curve slopes close to this theoretical value,
there was a wide range of variation across units. As with phase
measures, previous studies have measured the low-frequency
slopes of gain curves calculated using linear frequency analysis
and described units whose frequency responses matched those
of either a theoretical velocity detector (Kroese and Schellart
1992; Kroese et al. 1978) or a theoretical acceleration detector
(Munz 1985; Munz et al. 1984). The responses of lateral line
afferents in this study were clearly nonlinear, and thus linear
frequency analysis was inappropriate. However, Coombs and
Janssen (1990) did report low-frequency slopes close to theoretical velocity detector values using analyses similar to ours,
so it is unclear whether the wide range of low-frequency tuning
curve slopes reported here resulted exclusively from nonlinearities in the neural response.
These results suggest that, under the present experimental
conditions, midshipman superficial neuromasts do not follow
the predicted responses of velocity detectors. This is not to say
that these neuromasts do not respond in proportion to velocity
at the receptor level, but this information is obscured at the
level of the primary afferent. In addition to presenting stimulus
amplitudes beyond the linear range of the system, a key methodological difference between this and previous studies was
the placement of the stimulus relative to the receptive neuromasts. Most demonstrations of velocity sensitivity of the lateral
line have been done either in vivo with the vibrating sphere
located in the same position relative to each receptive neuromast (Kroese and Schellart 1992; Kroese et al. 1978) or in vitro
using isolated patches of skin containing superficial neuromasts (Harris and Milne 1966; Kroese et al. 1978; Strelioff and
Honrubia 1978). Our study was done in vivo, but the stimulus
was not in the same position relative to the receptive neuromast
for each unit because the sphere’s position was fixed. Variability in the distance between the stimulus and the receptor may
have caused the variability in phase and low frequency slope of
the response. It is possible that under experimental conditions
like those described in Kroese and Schellart (1992), midshipman superficial neuromasts would respond in a velocity sensitive manner. Our methodology, however, allows us to consider a more realistic situation in which an array of lateral line
organs is presented with a stimulus at some point in space
rather than with a stimulus that is equidistant from each receptor. Moreover, it is likely that natural stimuli will be of sufficient amplitudes to drive the lateral line system beyond the
linear response range. Thus in a more naturalistic context, the
responses of superficial neuromasts might be too complex to be
modeled solely in proportion to velocity.
SUPERFICIAL NEUROMAST RESPONSE PROPERTIES
Sound detection
The role of the lateral line system in sound detection has
long been debated (see Sand 1981 for review). The acoustic
field around an underwater sound source consists of a particle
motion dominated near field and a pressure wave dominated far
field (Kalmijn 1988, 1989). Because the effective stimulus to
the lateral line system is movement of the surrounding water
relative to the fish, near field particle motion is the only sound
component capable of directly stimulating the lateral line (Harris and van Bergeijk 1962). Further, the steep gradients of
particle motion confine direct stimulation to the innermost
region of the near field, within a few body lengths from the
source (Denton and Gray 1983, 1988; Kalmijn 1988, 1989). It
is therefore likely that acoustic stimulation of the lateral line
will occur in close proximity to the source.
Male midshipman produce loud (⬃125 dB re: 1 ␮Pa) acoustic signals with fundamental frequencies at or ⬍100 Hz, well
within the response range we have shown for superficial neuromasts. The near field dominates the sound field up to a
distance of ␭/2⌸ from the source, which is on the order of 2–3
m for a midshipman vocalization with a fundamental frequency
of 100 Hz (Bass and Clark 2002). During the breeding season,
nocturnally active males produce advertisement “hums” to
attract females to their nests and agonistic “grunts” to fend off
conspecific males during territorial defense (Bass et al. 1999;
Brantley and Bass 1994; McKibben and Bass 1998, 2001a).
These interactions often occur within several body lengths and
place the recipient within the inner regions of the near field of
the vocalizing fish, where stimulation of the lateral line system
would occur.
The lateral line system might also be stimulated indirectly
via the swim bladder. As an air-filled sac, the swim bladder
oscillates when placed within a pressure wave. This oscillation
re-radiates the sound, creating an indirect near field signal.
Displacement thresholds of the roach (Rutilus rutilus) lateral
line are below the particle displacements of re-radiated pressure waves, suggesting that indirect stimuli are detectable by
the lateral line system in these animals (Sand 1981). Thus it is
reasonable to predict that the midshipman lateral line could
indirectly detect acoustic signals at distances beyond the normal range of direct lateral line stimulation.
J Neurophysiol • VOL
This study clearly shows that midshipman superficial neuromasts are capable of encoding frequencies within the range
of natural vocalizations. The question remains then, what additional information might the lateral line provide that is not
available via the inner ear? It is unknown what a female
midshipman bases her decision on when choosing a mate, but
vocalizations likely provide information regarding male quality, especially in a nocturnal habitat where visual cues are
limited. While the auditory system is clearly used to guide a
female to a humming male’s nest (Brantley and Bass 1994;
McKibben and Bass 1998, 2001a), the final mating decision
may take place within the inner regions of the near field using
input from the lateral line and auditory systems. Similarly, the
lateral line system may be used to assess the size of a vocalizing rival male during close agonistic interactions. The behavioral data, together with the current study’s physiological results and our previous demonstration of anatomical overlap
between the ascending lateral line and auditory pathways (Bass
et al. 2000; Weeg and Bass 2000a), suggest an interaction between the lateral line and auditory systems that may be common
to all aquatic anamniotes, especially those that vocalize.
We thank D. Bodnar, J. Sisneros, B. Carlson, and two anonymous reviewers
for providing valuable feedback on an earlier version of this manuscript. S.
Coombs and D. Bodnar provided technical support and encouragement during
the many stages of stimulus calibration, and B. Land designed the strobe used
to measure sphere displacement. Logistical support was provided by the
University of California Bodega Marine Laboratory.
This research was supported by National Institutes of Health Grants (5-T32MH-15793) to M. S. Weeg and (DC-00092) to A. H. Bass.
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