Integrative Physiology and Neurobiology Section, Department of Biology, E.P.O., University of Colorado at Boulder, Boulder, Colorado 80309-0334
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ABSTRACT |
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Casagrand, Janet L.,
Audrey L. Guzik, and
Robert C. Eaton.
Mauthner and Reticulospinal Responses to the Onset of Acoustic
Pressure and Acceleration Stimuli.
J. Neurophysiol. 82: 1422-1437, 1999.
We determined how the
Mauthner cell and other large, fast-conducting reticulospinal neurons
of the goldfish responded to acoustic stimuli likely to be important in
coordinating body movements underlying escape. The goal was to learn
about the neurophysiological responses to these stimuli and the
underlying processes of sensorimotor integration. We compared the
intracellularly recorded postsynaptic responses (PSPs) of 9 Mauthner
cells and a population of 12 other reticulospinal neurons to acoustic
pressure and acceleration stimuli. All recorded cells received both
pressure and acceleration inputs and responded to stimuli regardless of
initial polarity. Thus these cells receive acoustic components
necessary to determine source direction. We observed that the Mauthner
cell was broadly tuned to acoustic pressure from 100 to 2,000 Hz, with
a Q10dB of 0.5-1.1 over the best frequency range,
400-800 Hz. This broad tuning is probably due to input from S1
afferents and is similar to tuning of the behavioral audiogram. Our
data suggest that cells have relatively more sustained responses to
acceleration than to pressure stimuli, to which they rapidly adapted.
For a given cell, PSP latencies and amplitudes varied inversely with
stimulus intensity. For the entire population of cells studied, minimum onset latencies (i.e., those at the highest intensities) ranged from
0.7 to 7.6 ms for acoustic pressure and 0.7 to 9.8 ms for acceleration.
This distribution in minimum onset latencies is consistent with earlier
EMG and kinematic findings and supports our previous hypothesis that
escape trajectory angle is controlled, in part, by varying the
activation time of neurons in the escape network. While the Mauthner
cell latency did not differ to both onset polarities of pressure and
acceleration, this was not true of all cells. Also, the Mauthner cell
responses to pressure were ~0.6 ms faster than to acceleration; for
the other cells, this difference was 1.1 ms with some cells having
differences 3 ms. To both pressure and acceleration, the average,
minimum Mauthner cell latency was ~1 ms faster than the average of
the 12 other cells. These data are consistent with the hypothesis that
the Mauthner cell fires first, followed by other reticulospinal
neurons, which more finely regulate escape trajectory. Finally,
analysis of our results suggests that while pressure is more important in depolarizing the cell near threshold, high levels of acceleration, perhaps from fluid flow, may be very important in activating the system
in a directional manner.
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INTRODUCTION |
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The reticulospinal system is integral to the
production of coordinated movements in vertebrates (Grillner et
al. 1997; McClellan 1986
; Peterson
1984
). It is evolutionarily conserved across classes and is
derived from the segmentally organized embryonic hind brain (Kimmel 1993
). Inputs are from higher-order motor areas,
such as the mesencephalic locomotor region, and also sensory systems, such as auditory and vestibular nuclei. We are interested in the general topic of how the reticulospinal system uses sensory information to coordinate motor responses.
For this purpose, we have been studying the reticulospinal network
underlying the goldfish escape behavior. In response to an aversive
stimulus, such as a sudden sound or object dropped into the water near
the fish, the animal bends its body away from the stimulus and
accelerates from its starting position. This behavior is oriented
directionally away from the stimulus, with the angle of the initial
escape trajectory being a linear function of the stimulus angle
relative to the body (Eaton and Emberley 1991). The
escape response is initiated by the well-studied Mauthner cells, which
fire within 3-4 ms after stimulus onset (Canfield and Rose
1996
; Eaton et al. 1981
, 1988
; Zottoli
1977
). The response also involves the Mauthner homologues M2
(or MiD2cm) and M3 (or MiD3cm) (O'Malley et al. 1996
),
which available evidence suggests are part of a larger brain stem
escape network (BEN). Of the ~500 reticulospinal neurons, there are
~280 identified reticulospinal neurons, or small isomorphic groups
(Lee et al. 1993a
), of which the BEN is a subset. These
cells receive acoustic and other sensory inputs (Lee et al.
1993a
) and project to spinal pattern generating circuits that
cause the trunk musculature to contract (Fetcho 1991
).
To direct the escape response away from the stimulus, the BEN must
receive sensory information about the location of the stimulus. This
allows it to code its firing pattern as a function of stimulus
location. Thus the cells of the BEN serve as a "sensorimotor interface," to use the words of Lingenhoehl and Friauf
(1994)
.
What is the mechanism that produces this sensorimotor transformation?
Mauthner cells are the fastest-conducting BEN neurons and appear to
have a receptive field covering the entire hemisphere on one side of
the fish (Eaton and Emberley 1991; Eaton et al. 1981
). At the start of the escape response, one of the Mauthner cells produces a single action potential that excites the trunk musculature on the side opposite the stimulus. This initiates the
orientation away from the stimulus. Other members of the BEN, such as
M2 and M3, have narrower receptive fields and contribute to the initial
trajectory (O'Malley et al. 1996
). For rostral stimuli,
which cause large turns, M2 and M3 are activated (Fetcho et al.
1998
), whereas they are not for caudal stimuli, which cause small turns. These observations support the hypothesis in which escape
trajectory angle is controlled by the onset times of the BEN neurons
and the number of these cells activated (Foreman and Eaton
1993
). In concert with this, we know from electromyographic (EMG) recordings that larger turn angles are caused by muscle activation patterns that are more prolonged in duration and larger in
amplitude than during small turn angles (Foreman and Eaton 1993
). Although there is a fairly good understanding of how
interneurons regulate Mauthner cell activation (Faber et al.
1991
) and how the motor output of this system is organized
(Svoboda and Fetcho 1996
), we currently know very little
about how sensory information is processed to direct this motor output.
Acoustic stimuli are highly effective in eliciting escape responses,
and fish are able to determine the location of acoustic sources and
direct their escape trajectories accordingly (Blaxter et al.
1981; review, Canfield and Rose 1996
;
Eaton et al. 1995a
; Lewis and Rogers
1998
). It has been proposed that this localization for escape
involves a comparison of the phase of the acceleration and acoustic
pressure components at the stimulus onset (Eaton et al.
1995a
). This hypothesis is based on a derivation of a simple case of the phase model, which was proposed by
Schuijf (1981)
. In this case, the fish distinguishes the
location of the source at its onset when it is located close to the fish.
The necessary components can be detected by acoustic receptors in the
inner ear (Popper and Fay 1993; Rogers et al.
1988
). Although involvement of the lateral line cannot be
totally ruled out (for review, see Coombs et al. 1989
),
Canfield and Rose (1996)
used cobalt inactivation to
show that the lateral line was not important in directional
discrimination of the acoustic stimuli used in their experiment on
goldfish. Particle motion (acceleration) and pressure in the goldfish
are transduced by movements of dense calcareous structures, or
otoliths, present in the three otolithic endorgans of the inner ear:
the saccule, lagena, and utricle (for review, see Popper and Fay
1993
). Movement of an otolith activates hair cells in the
sensory epithelium of the endorgan. Acceleration is detected directly
via the inertial response of the otoliths to motion. Pressure is
detected indirectly via the swimbladder's fluctuating volume in the
acoustic pressure field. In goldfish, the swimbladder is coupled to the
saccule via a series of bones, the Weberian ossicles, that transduce
the pressure into a movement conveyed to the fluids of the inner ear.
Ultimately, both pressure and acceleration are detected by the same
type of sensory receptors, hair cells, in the inner ear. It is believed
that the saccule primarily responds to the pressure component of sound,
and the lagena and utricle to acceleration, though many experts
recognize that both components might be conveyed to some extent by any
given otolith (Rogers et al. 1988
; Schellart and
de Munck 1987
).
In this study we were interested in characterizing the initial
responses of different members of the BEN to the onset of acoustic pressure and acceleration stimuli presented separately. With underwater acoustic sources, it is technically difficult to accelerate the fish in
the absence of acoustic pressure. Therefore we presented these stimuli
separately with the fish suspended in air. Although there are caveats
to presenting pressure stimuli in air, this approach allowed us to
separate the two components. Furthermore in the computation of
directionality, theory predicts that the inputs from these two
components must interact nonlinearly (Eaton et al.
1995a). The advantage of our approach is that it allowed us to
compare independently the contributions of the two components in
activating the cell and to understand how the cell is using this information.
Because of the very fast response of the BEN cells, we were especially
interested in latency and amplitude of post synaptic potentials (PSPs)
to stimulus onset, rather than the details of their
responses to prolonged stimuli. We hypothesize that if directional orientation during escape results from differential sensory processing in reticulospinal neurons, then we should see differences among these
cells in measures of response latency to acoustic components. First, we
did intracellular recordings from the Mauthner axon in the medulla to
confirm that the Mauthner cells receive acoustic pressure and
acceleration input. It is not yet known to what extent these acoustic
components converge in the CNS of fish (Popper and Fay
1993). Second, we compared these responses to those seen in
other large, fast-conducting, reticulospinal neurons that we penetrated
in the same vicinity as the Mauthner axon. We did not attempt to
identify these "non-Mauthner" reticulospinal cells morphologically,
but we consider them to be putative members of the BEN because of their
position in the medial longitudinal fasciculus (MLF); size and fast
conduction velocities; and large-amplitude responses to acoustic
pressure and acceleration stimuli. All of the recorded cells received
both acoustic pressure and acceleration inputs, but we found that
non-Mauthner reticulospinal cells were on average slower than the
Mauthner cell in their response latencies. Moreover, in support of the
earlier hypothesis, they exhibited a distribution of minimum response
latencies consistent with previous EMG and kinematic findings. This
supports the idea that activation order of these cells could be an
important factor in determining the escape trajectory when the fish
responds to acoustic stimuli. Furthermore the natural acoustic stimuli
that trigger fish escape responses are not well understood or
characterized. Analysis of the responses of the Mauthner cell to the
individual acoustic components may provide insights into the nature of
the stimuli for which the system has evolved. Some of these findings
have been reported in abstract form (Casagrand et al. 1995
,
1997
).
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METHODS |
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Experimental animals
The basic experimental arrangement is illustrated in Fig.
1A. Experiments were performed
on 18-30 g adult goldfish [Carassius auratus (L.)]
maintained and surgically manipulated as described previously
(Canfield and Eaton 1990). Briefly, fish were
anesthetized in aquarium water containing 250 mg/l of 3-aminobenzoic
acid ethyl ester (MS 222, Sigma) for 12 min. The head was stabilized in
a rigid holder with brass pins, and the body was supported by Velcro straps. Throughout the experiment the fish were respirated through the
mouth with chilled aquarium water (8°C) containing anesthetic (100 mg/l MS-222). The anesthetic would be expected to affect spike
initiation in the reticulospinal neurons, but it is unclear the extent
to which it would influence synaptic potentials, if at all.
Nevertheless, we observed synaptic potentials near spike threshold at
intensities similar to those that activate the behavior, and with
appropriate latencies (see DISCUSSION). All procedures were
approved by the Institutional Animal Care and Use Committee of the
University of Colorado at Boulder.
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Electrophysiology
To record from the Mauthner axons and other reticulospinal
cells, an opening was made in the skull overlying the vagal lobes, and
fat and connective tissue were aspirated away. Care was taken to not
damage the semicircular canals. The plexus between the vagal lobes over
the fourth ventricle was removed to allow for microelectrode
penetrations of the Mauthner and other reticulospinal axons. The somata
of these cells reside in seven rhombomeric segments (1-7, Fig.
1B). The paired Mauthner cells (MC) are within the 4th
segment; their segmental homologues M2 and M3 are within the 5th and
6th segments. As illustrated in the cross-section shown in Fig.
1C, the axons of these cells course down the dorsal bundle of the MLF. Recording from the location shown, just posterior to the
facial lobe, allowed us to locate the Mauthner cell and prospective
members of the BEN with our micropipettes. MLF axons were stimulated
antidromically with a pair of bipolar, needle electrodes inserted on
either side of the spinal column just posterior to the dorsal fin
("Antidromic stimulus," Fig. 1A). We used
short-duration, low-voltage pulses (2-15 V, 0.05 ms) at one per second
to activate these cells. Initially the stimulus intensity was set to
just above the amplitude needed to give a reliable, characteristic "Mauthner reflex" (movement of eyes, operculum and mouth)
(Faber and Korn 1978; Nissanov et al.
1990
; Zottoli 1977
). We secured the stimulating
electrodes in place with a veterinary surgical glue (Vetbond; 3M) and
immobilized the fish with an intramuscular injection of a neuromuscular
blocking agent, either D-tubocurarine (Sigma) given at
0.001-0.002 g/g body wt, or diallyl-bis-nor-toxiferine dichloride
(Alloferin, Hoffman-LaRoche) given at 0.25 µg/g body wt (this drug is
no longer available, however).
After neuromuscular blockade, microelectrode penetrations were done
within 200 µm of the midline and within 300 µm of the surface (Fig.
1C). For the intracellular recordings, we used glass microelectrodes filled with 3 M potassium chloride and beveled to
resistances of 5-15 M. As in our previous studies, Mauthner axon
penetrations were identified by their characteristic position and
depth, antidromic spike latency from the spinal stimulus, high-amplitude action potential of characteristic shape, and
sensitivity to low level sounds (Canfield and Eaton
1990
; Eaton et al. 1982
). Here findings are
based on recordings from nine cells initially judged from physiological
criteria to be the Mauthner axon.
The axonal recording site allowed us to locate in the same vicinity other fast-conducting non-Mauthner reticulospinal cells that responded to both acoustic pressure and acceleration. Compared with the Mauthner axon, the non-Mauthner axons typically had a higher spinal stimulus threshold and an antidromic spike latency that was ~0.4 ms longer from the spinal stimulus. And, as described in RESULTS, the non-Mauthner axons had different response latencies to acceleration and acoustic pressure pulses. (PSP latency, onset time, was defined as the difference between the time of arrival of the stimulus at the fish, i.e., the 1st detectable deviation of the signal from baseline in the hydrophone or accelerometer recording, and the deviation from baseline of the PSP). We judged these cells to be large-diameter fibers, because they were in the MLF, they responded quickly to the spinal stimulus, and we could maintain their penetrations during the large acceleration stimuli that were used for the Mauthner axons (see RESULTS). Findings from 10 such cells are reported. Criteria were ambiguous as to whether two other cells were the Mauthner axon or not. (These may have been Mauthner segmental homologues.) These are included in the non-Mauthner category in RESULTS. In addition, in one case there was subsequent uncertainty about the identity of a cell initially categorized as Mauthner: our data analysis showed that its responses were different from the rest of the Mauthner population (this is the outlier in Fig. 8). Despite possible uncertainty, we did not change the designation of this cell. Its inclusion in the Mauthner group would support the null hypothesis (i.e., that the Mauthner and non-Mauthner populations were not different) for all statistical measures described in RESULTS, and therefore to be conservative, we did not modify our initial identifications. Finally, we did not systematically evaluate some cells we encountered that had no apparent responses to acoustic pressure, even though they had short-latency responses to antidromic stimulation.
Acoustic pressure and acceleration stimuli
With a mass of water around the fish, it proved to be difficult to accelerate at the levels we desired. Second, the movement also caused substantial acoustic pressure fields around the fish when it was in water. We were not able to eliminate these acoustic pressure fields either by physical alterations of the fish holder or by trying to negate them by presenting simultaneous acoustic pressures in the air above the fish. Therefore all stimuli were presented with the fish suspended in air. This had the benefit of allowing us to completely dissociate the contribution of acceleration and acoustic pressure to the activation of the cell. This is unlike the situation with underwater sources, where the two components are simultaneously present. Our main conclusions are based on latency measurements, which would be unaffected by having the fish in air. In addition, the tuning response of the Mauthner cell to acoustic pressure stimuli in air was similar to behavioral audiograms determined in water. Finally, the stimulus levels at which we saw maximal responses were similar to those that activate the response in behavioral studies (see DISCUSSION).
Our acoustic pressure pulses were produced by a loudspeaker (KEF Q10, 6 , 100 W) placed 28 cm in front of the fish. This pressure stimulus
is probably very good for activating the saccule and poor for the
utricle and lagena (Fay 1995
). Sinusoidal acoustic pressure pulses (100-3,000 Hz) were produced by a waveform generator and attenuator system (Tucker-Davis Technologies) and amplified with a
Techron 7520 power amplifier. These stimuli were calibrated without
fish in place. Acoustic pressure was measured using a small hydrophone
(Brüel and Kjær 8103, sensitivity 26.0 µV/Pa), which was also
designed for use in air, placed in the position that the fish's
swimbladder would be located during recordings. To record the
hydrophone output, we used a 2-stage voltage amplifier (World Precision
Instruments, DAM 6) with high-input impedance (500 M
) and band-pass
filtered (10 Hz to 3 kHz).
Acceleration stimuli were produced by two force transducers
(Brüel and Kjær, model 4810) to generate pulsed, sinusoidal
stimuli that accelerated the entire fish holder (and fish) in the
horizontal plane at a right angle to the long axis of the body of the
animal (Fig. 1A). The side-to-side acceleration we used is
probably a relatively poor stimulation axis for the goldfish's
saccular endorgan, though it should be effective for the utricule and
lagena (Fay 1995). Side-to-side acceleration was found
by Lewis and Rogers (1998)
to be particularly effective
in eliciting escape responses. The design of this apparatus was
inspired by a device of Fay (1984)
, which he used to
activate goldfish auditory fibers. However, to create the relatively
large horizontal accelerations needed to activate the reticulospinal
neurons, our two transducers operated in a push-pull fashion. Their
bases were mounted on the recording table, and their movable spindles
were attached to opposite sides of the base of the fish holder, which
was mounted on a precision ball bearing slider (Parker Positioning
Systems, model 4601) attached to the recording table. The slider
permitted accelerations of adequate amplitude by allowing the fish to
be displaced relative to the recording table, though the high mass of
the holder prevented us from exploring a variety of frequencies.
However, the frequency we used, 125 Hz, is close to the best frequency
for lagenar neurons (100-200 Hz for displacement) (Fay 1981
;
Fay and Olsho 1979
). We measured acceleration with an
accelerometer [PCB Piezotronics, sensitivity, 100 mV/(m/s2)] attached to the fish holder.
We used another channel of our Tucker-Davis Technologies system to produce the acceleration stimulus sinusoids. These signals were amplified with a power amplifier (Brüel and Kjær 2706) that drove the force transducers (Fig. 1A). The originating stimulus signal was typically 10 or 30 ms in duration, although the movable fish holder resonated longer. For present purposes, this resonance was not harmful because we mainly were interested in the initial responses (latency and amplitude) of these cells to the stimulus.
Electrophysiology and data analysis
We used a Narashige, Canberra-type ME-7 micromanipulator to
position our microelectrodes for intracellular recordings. The base of
the manipulator was fixed to the recording table. Despite the fact that
the stimulus moved the fish relative to the microelectrode, we found
that with axon recordings from the larger cells we could often
accelerate the animal 3.1 m/s2 (9.8 dB re 1 m/s2) at 100 Hz without noticeable movement artifact,
without losing the intracellular penetration, and without damage to the
cells. Our ability to hold cells during these accelerations also was facilitated by our relatively long and flexible microelectrode shafts.
Intracellular signals were recorded with a World Precision Instruments 767 DC amplifier. Data were collected and analyzed on a Gould 1604 digital oscilloscope, saved as ASCII files on a 486/66 MHz computer, and imported into Sigmaplot for Windows 3.1 (Jandel Scientific) to generate figures. Statistical analyses were performed using Sigmastat for Windows 3.1. The Mann-Whitney rank sum test was used unless indicated otherwise. Results are reported as mean ± SEM. The binwidth for the histograms (0.2 ms) was based on the maximum variability observed within replicate trials (although most cells showed less or no variability between replicate trials).
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RESULTS |
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Our primary objectives were 1) to determine the
responsiveness of the Mauthner cells to the onset of acoustic pressure
and acceleration stimuli, and 2) to compare these responses
to those in other fast-conducting reticulospinal axons believed to be
members of the BEN. Our analysis was based on recordings of
sub-threshold synaptic responses obtained in the
axon of the Mauthner cell and putative members of the BEN.
With the animal restrained for electrophysiology, anesthetized and
given a neuromuscular blocker, the adult goldfish Mauthner cell rarely
fires an action potential to an acoustic stimulus. Our axonal recording
site was at the level of the vagal lobes, ~1.5-3.0 mm caudal to the
spike initiating zone of the field of reticulospinal cells, in general,
and 2.0-2.5 mm for the Mauthner cell, which is within 1 length
constant, , (see following text). The Mauthner axons travel along
with other reticulospinal axons in the MLF at this position, making it
possible to record from putative members of the BEN. In addition, the
reticulospinal axons are highly myelinated at this site (Eaton
et al. 1995b
) (Fig. 1C). Myelin may provide good
mechanical support for maintaining intracellular recordings even during
relatively powerful acceleration stimuli that we used. We begin by
first describing the responses of the Mauthner cell to acoustic
pressure stimuli.
Acoustic pressure causes relatively short ON responses
In the lower traces of Fig.
2A, we show two superimposed
PSPs recorded in the Mauthner axon to two acoustic pressure stimuli (upper traces). As can be seen for these 10-ms, 500-Hz tone
pulses, the cell responded with short-latency, compound PSPs that
reached maximal amplitude within 1-2 ms from onset, and then rapidly
decayed (see METHODS for latency definitions). This was
true of all the reticulospinal neurons we analyzed. Regardless of
frequency, all the cells adapted rapidly with a more-or-less phasic
ON response. The duration of the compound PSP was similar
for tone pulses of 10 and 30 ms. In addition, all the cells responded
to the stimulus regardless of the polarity of the initial onset phase;
in Fig. 2A, the initial onset phase is either compression
(positive-onset, P+, ) or rarefaction (negative-onset, P
, - - -).
Also note in Fig. 2A, the individual PSPs in response to
both positive and negative peaks of the pressure pulse. In general, the
responses to P+ and P
were similar in amplitude and waveform,
although there were differences, as can be seen in this example. We did not explore these differences further.
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The responses were reproducible in both latency and amplitude for
a given intensity and frequency stimulus if there was a sufficient
interval between subsequent trials. However, if the intertrial interval
was less than ~1 min, the PSP amplitude was reduced; the shorter the
intertrial interval, the larger the decrement of the response. This
adaptation correlates with behavioral observations of reduced response
probability to acoustic stimuli at short inter-trial intervals
(Eaton et al. 1988). The mechanism for such long-term effects is not yet studied, although there is sensory adaptation of the
hair-cell-to-S1 afferent synapses (Furukawa and Ishii
1967
; Furukawa and Matsuura 1978
). Therefore the
intertrial interval was generally
2 min.
It is believed that Mauthner cell firing latency decreases with
stimulus intensity (Eaton et al. 1984). The present
findings are consistent with this. For both the Mauthner cell and other reticulospinal neurons, the latency and amplitude of the acoustic pressure-evoked PSPs varied with stimulus intensity over a range (data
from 11 cells). This is shown in the recordings of Fig. 2B,
which were obtained from the same cell as Fig. 2A. Here, we illustrate three superimposed PSPs evoked at three intensities of a
500-Hz tone pulse lasting 10 ms. As the stimulus intensity increased,
the PSP latency decreased and the amplitude increased. This
relationship is shown graphically for one cell in Fig.
3. The differences in latency in Fig. 3
are greater than would be expected due to electronic decay (see
DISCUSSION). On average, the maximum amplitude of the
acoustic pressure-evoked PSPs was 5.44 ± 0.84 mV
(n = 9) for the Mauthner cells, and 3.74 ± 0.59 mV (n = 12) for the non-Mauthner cells. These values
were not statistically different from one another (P > 0.19). The maximal PSP amplitude was generally observed at 128-132 dB
re:1 µPa, and in some cells was close to 10 mV, just below the
Mauthner spike threshold. PSPs did not increase in amplitude above this
intensity, and in some cells a slight decrement in the PSP amplitude
was observed above this level. This is just below the value of ~140 dB for the escape behavior threshold observed in behavioral studies by
Lewis and Rogers (1998)
.
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Different cells vary in their latencies to acoustic pressure
Recall that the Mauthner cell is believed to fire first, followed
by other reticulospinal members of the BEN. This model would predict
that the Mauthner cell has the shortest latency PSPs to acoustic
stimuli. To test this, we measured the minimum PSP latencies for
recorded cells, i.e., latencies at intensities from the flat part of
the intensity-latency curve in Fig. 3. Indeed, we observed that the
Mauthner cell responded fastest of the cells we recorded. The Mauthner
cell generated its PSP ~1 ms sooner than the average of the
non-Mauthner cells. For instance, when comparing responses to P+
stimuli, the average Mauthner PSP latency was 1.42 ± 0.14 ms
(n = 9) versus an average of 2.32 ± 0.51 ms
(n = 12) for the non-Mauthner cells. The difference was
similar when comparing responses to P stimuli (Mauthner mean = 1.41 ± 0.12 ms; n = 8; non-Mauthner mean = 2.39 ± 0.51 ms; n = 12). The difference between Mauthner and non-Mauthner PSP latencies evoked by acoustic pressure was
statistically significant (P < 0.006).
We do not believe that passive cable properties could account for these
differences because the slopes of the PSPs appeared to be fairly sharp
(if we assume the subthreshold events are similar in slope for all
cells at their spike initiating zones). When we measured the average
slope (time to peak divided by amplitude at peak) for acoustic
pressure-evoked PSPs in the Mauthner and non-Mauthner groups, we found
no significant differences (P > 0.39). The average
slope was 5.47 ± 0.77 ms/mV (n = 9) and 4.61 ± 0.88 ms/mV (n = 10) in the Mauthner and non-Mauthner
groups, respectively. In addition, consideration of in these cells
suggests that there would not be sufficient decrement at our recording position to influence our measurements of latency (see also
DISCUSSION). Thus the differences in latency are not due to
substantial decrement of the PSPs in the non-Mauthner cells resulting
in our inability to resolve their actual onsets, particularly given the
range of latencies we observed (7 ms).
When considering the entire population of cells (Mauthner
and non-Mauthner), the range of minimum latencies (that is,
those at the highest intensities) varied considerably, from 0.7 to 7.6 ms. This is shown in the frequency histograms of Fig.
4A for P+ and 4B
for P. (Note: * on this and later figures indicates that the longest
responding cell was off scale). The mean PSP latencies for the entire
population evoked by P+ versus P
were not statistically different
(P > 0.7; n = 21, 20). When considered
on an individual cell basis, we observed that the Mauthner cell had
nearly indistinguishable differences in the latency of its PSPs to P+
and P
stimuli (for example, Fig. 2A), as did many of the
non-Mauthner cells. However, some had differences of
0.27 ms in their
latencies to P+ and P
stimuli. We show in Fig. 4C the
distribution of differences between the latencies to P+ and
P
for all cells. Cells with little or no difference in latency
between P+ and P
fall in the 0.0-ms bin (
0.1- to 0.1-ms
difference). The cells falling on the positive side of the graph had
PSPs to P+ with shorter latency than to P
. For the entire population,
however, the mean difference in latency for PSPs evoked by
P+ and P
stimuli in a given cell was only 0.08 ± 0.02 ms
(n = 20).
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Mauthner cell is broadly tuned to acoustic pressure
It was previously shown by Blaxter et al. (1981)
that the escape response of the herring is broadly tuned, but the
frequency response of the Mauthner cell has not been systematically
examined. Therefore we constructed tuning curves as shown in Fig.
5A. This diagram represents
the sound pressure level at a given frequency necessary to generate a
PSP of a particular, integer amplitude (4-9 mV). To generate the
curves, a series of standard intensity test pulses was given in each
experiment at a variety of frequencies. The resultant PSPs varied in
amplitude, and integer values were determined by interpolation from the
amplitudes of the evoked PSPs. From the tuning curve we observed that
the response of the Mauthner cell was broadly tuned from 100 to 3,000 Hz for positive-onset pressure, with a Q10dB of
0.5-1.1 over the best frequency range. (Similar results were found in
2 other cases.) The best frequency for positive-onset pressure was from
400 to 700 Hz and appeared to shift slightly up in frequency as the
intensity increased. This can be seen by comparing the tuning curve for
the 5-mV PSPs, which had a best frequency of ~400 Hz, versus that for
8 mV, which had a best frequency of ~700 Hz. Tuning curves for
negative-onset pressure were similar in many respects to those for
positive-onset pressure (n = 2), as shown in Fig.
5B. As for negative-onset pressure, the response was broadly
tuned over the range of 100-2,000 Hz, with a Q10dB of
0.6-1.1. The best frequency for negative-onset pressure was shifted
slightly compared with positive-onset, and ranged from 500 to 800 Hz.
With increased intensity, the curve appeared to shift to slightly lower
frequencies. There was no change in the stimulus waveforms that could
be attributed to these shifts in best frequency.
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We observed that the PSPs increased linearly in amplitude with each
decibel increase in acoustic pressure intensity (data not shown). We
calculated the linear regressions for the PSP amplitude versus stimulus
intensity for frequencies ranging from 400 to 1,000 Hz. Although the
cell responded better to some frequencies than others (Fig. 5,
A and B), as the sound pressure level (in dB)
increased, the amplitude of the PSP increased in a similar fashion
regardless of the frequency. The slopes of these relationships were
similar and all were statistically significant (for P+,
r2 ranged from 0.91 to 0.987, with slopes
from 0.18 to 0.28; for P, r2 ranged
from 0.91 to 0.982, with slopes ranging from 0.14 to 0.24). The fact
that the regressions were not zero indicates that there was a
significant correlation between intensity and amplitude. The
alternative is that there was no significant correlation, meaning that
the cells do not code amplitude. The fact that the regressions were
similar in slope indicates that they were coding intensity in a similar
way, despite the fact that the cells probably receive inputs from
afferents of different response types and frequency sensitivities. One
would not necessarily expect the intensity coding to be similar at
different frequencies.
Acceleration causes relatively sustained ON responses
The reticulospinal neurons also responded to acceleration in the horizontal plane. Of the 21 neurons tested with acoustic pressure, we were able to characterize the responses of 18 to acceleration, although all responded to acceleration. As with acoustic pressure, all the cells responded with compound excitatory PSPs to both initial "polarities" of acceleration (ARL and ALR). The acceleration here is characterized according to its initial value; that is, ALR, means that the fish initially was accelerated left-to-right, and ARL, right-to-left (see Fig. 1A). Although one might expect that some cells would respond to acceleration in only one direction, all of them responded to both directions. Figure 6A shows a comparison of the responses of a left Mauthner cell to ALR and ARL stimuli [onset frequency ~125 Hz at 1.0 m/s2 (0 dB re m/s2)]. The top traces are the corresponding acceleration; the bottom traces the subthreshold activity evoked in the Mauthner cell by acceleration. Note, as for pressure, that there are individual PSPs in response to the positive and negative peaks of the initial acceleration waveform.
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The duration of the compound PSP for both polarities of acceleration appeared to be somewhat longer, or more sustained, than for acoustic pressure. We did not attempt to quantify this precisely because our primary interest was responses to stimulus onsets. The effect, though, can be seen if we compare carefully the PSPs in Figs. 2A and 6A. (Notice that the time axes are different scales). The compound PSP to P+ declined to ~50% amplitude in ~3.5 ms even though the stimulus was maintained for another 6.5 ms. In contrast, the response to acceleration remained within 50% of its initial value for ~10 ms even though stimulus amplitude decreased during this time. Notice that the amplitude of the PSPs was not as large for acceleration as for acoustic pressure, even with the relatively high values used in this study. However, the average ratio of the amplitudes for acoustic pressure- and acceleration-evoked PSPs was not statistically different for Mauthner and non-Mauthner cells (P > 0.86; mean = 1.44 ± 0.26, n = 8, for Mauthner cells vs. 1.37 ± 0.17, n = 11, for non-Mauthner cells).
As with acoustic pressure, the latency and amplitude of the acceleration-evoked PSPs varied with stimulus intensity over a range (tested in 6 cells). For example, we show in Fig. 6B the PSPs evoked in a Mauthner cell at three intensities of an ALR stimulus (same cell and stimulus parameters as Fig. 6A). As the stimulus intensity increased, the latency decreased and the amplitude increased. This relationship between stimulus intensity and PSP onset latency and amplitude is graphically shown for this cell in Fig. 7. We could not test a very wide range of acceleration intensities because the penetration was not stable at the larger intensities. Despite this, these intensities are comparable with those that elicit escape responses (see DISCUSSION). The average, maximum amplitude of the acceleration-evoked PSPs was 3.79 ± 0.53 mV (n = 8) for the Mauthner cells, and 2.94 ± 0.45 mV (n = 11) for the non-Mauthner cells. These values were not statistically different from one another (P > 0.23).
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The latency also varied among the different cells for the acceleration-evoked PSPs. We see in Fig. 8, A and B, that the range of minimum latencies varied considerably, just as it did for responses to acoustic pressure. The minimum latencies for acceleration-evoked PSPs in the population ranged from 0.7 to 9.8 ms. This was determined by comparing values for the various cells at stimulus intensities from the flat part of their latency curves. Although the means differ by 0.85 ms, when comparing responses to ALR versus ARL in Fig. 8, A and B, for the entire reticulospinal population, these differences were not statistically significant (P > 0.25; n = 18, 17).
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Just as the Mauthner cells produced the shortest latency PSPs to acoustic pressure, the Mauthner cells also had the quickest responses to acceleration. The average Mauthner cell PSP latencies were 1.9 ± 0.32 ms. This was ~1 ms faster than the average non-Mauthner latency of 2.84 ± 0.47 ms. This difference in acceleration-evoked PSP latency between the Mauthner and non-Mauthner populations was statistically significant (P < 0.023).
We showed above that there was little difference in onset latency for the two polarities of acoustic pressure. This was also true for responses to the two polarities of acceleration for some cells. Figure 8C plots the distribution of individual cell differences between the latencies for ALR and ARL accelerations. The convention here is the same as in Fig. 4C. On average, the mean difference between the latency for ALR- and ARL-evoked PSPs was 0.44 ± 0.25 ms (n = 17) for the population, although some cells had individual differences of up to 3 ms. About 30% of the cells shown in Fig. 8C fell in the 0.0-ms bin, with little or no difference in the latency of PSPs evoked by ALR and ARL. When comparing all our Mauthner responses, we observed little difference between the two directions of acceleration in terms of PSP onset latency (average difference in PSP onset to ALR vs. ARL was 0.1 ± 0.04 ms, n = 8). In fact, all the Mauthner cell data in Fig. 8C were either in the 0.0-ms bin, or the next bin to the right, 0.2 ms, regardless of the side of the axon (3 left, 5 right axons).
For the non-Mauthner cells, the average difference in minimum PSP onset
latency for the ALR versus ARL was 0.74 ± 046 (n = 9). Interestingly, all of the cases (n = 3) that fall
to the left of zero (ARL > ALR) are from non-Mauthner
cells with axons on the right side of the fish, and all the
non-Mauthner cases (n = 6) that fall to the
right of zero (ALR > ARL) have axons on the left side
of the fish. This distribution, relative to the side of the axon, is
significant for the non-Mauthner cells (2,
P < 0.05), but not the Mauthner cells
(
2, P > 0.25). This suggests
that we need to consider the direction of acceleration relative to the
side that the axon was on. This is shown in Fig. 8D for the
non-Mauthner cells, which plots the distribution of the differences in
latency of PSPs evoked by acceleration of the fish away from the side
the axon was on versus acceleration of the fish toward the side the
axon was on. (In other words, for a left axon, this would be ALR
ARL, and for a right axon, ARL
ALR.) The mean difference in
PSP onset, when considered this way, was 1.14 ± 0.34 ms
(n = 9), with a range of 0.12-3.02 ms. [For the
Mauthner cells, the mean difference, when considered with reference to
the axon, was only 0.03 ± 0.06 ms (n = 8), with a
range of
0.23-0.3 ms.] Thus for the non-Mauthner cells, the PSP
onset for acceleration of the fish toward the side the axon was on was
significantly faster than for acceleration of the fish away from the
side the axon was on (P < 0.01); whereas for the Mauthner cells, there was no difference (P > 0.95).
Also interestingly, there was no difference in the amplitude of the
PSPs evoked by acceleration in the two directions, considered with
respect to axon side, in the non-Mauthner cells (P > 0.42). This has potentially important implications for how this systems
controls the behavior (see DISCUSSION).
Latencies of acoustic pressure- versus acceleration-evoked PSPs
According to the phase model, determination of acoustic direction
underwater depends on a comparison of the polarity of both the acoustic
pressure and acceleration components (Schuijf 1981). Although we did not examine combinations of acoustic pressure and
acceleration in this study, our analysis raises the question as to the
relative latencies of these two signals. Presumably, the simplest
mechanism for solving the phase discrimination would be where acoustic
pressure and acceleration information activate the cell at the same
time. Thus we were interested in whether cells that had later onset
latencies for acoustic pressure-evoked PSPs also had later onset
latencies for acceleration-evoked PSPs.
To address this issue, we first looked at the difference in average,
minimum latency of the two acceleration directions (ALR, ARL) minus the
average, minimum latency of the two acoustic pressure (P+, P) stimuli
for each cell. The average difference in timing between acoustic
pressure and acceleration was ~0.74 ± 0.2 ms (n = 18). This means that the acoustic pressure-evoked PSPs were on
average faster, by this amount, than acceleration-evoked PSPs. In
~20% of the cases, there was no measurable difference in average latencies of acoustic pressure versus acceleration PSPs; but in the
other cases, acoustic pressure was faster than acceleration by up to
3.1 ms. This difference in the distribution (i.e., that acoustic
pressure was faster than acceleration) was statistically significant
(2-tail sign test, P < 0.002).
For Mauthner cells, the acoustic pressure stimuli produced PSPs that
were, on average, faster than the acceleration responses by
~0.59 ± 0.37 ms (n = 8), for stimulus
intensities that lie in the flat part of the stimulus response curves.
For the non-Mauthner cells this difference was about twice as large,
with the acoustic pressure-evoked PSPs being 1.11 ± 0.35 ms
(n = 10) faster. However, the average difference in
latencies of acoustic pressure- and acceleration-evoked PSPs was not
significantly different between Mauthner and non-Mauthner cells
(P > 0.15). Analyzing the data in this way, while
giving an overall idea of the relationship between the timing of
responses to acoustic pressure and acceleration stimuli for the
population, may be misleading relative to some of the non-Mauthner
cells because we were averaging the response latencies to the two
polarities of acceleration. Recall from the preceding text that the
response latencies to the two polarities of acceleration were
significantly different for the non-Mauthner cells when the direction
was considered relative to the side of the axons. To take this into
account, we compared the difference in latency to two combinations of
acceleration and pressure stimuli separately. The two combinations
would correspond to the initial phases of strike and suction predatory
attacks (see DISCUSSION). We define "strike"
as the difference in latency to acceleration of the fish in the same
direction as the side on which the axon is located, minus the latency
to P+; "suction" is the difference in latency to
acceleration of the fish away from the side on which the axon is
located, minus the latency to P. For the Mauthner cells, the
difference for the strike combination was 0.59 ± 0.37 (ms)
(n = 8) and for the suction combination was 0.55 ± 0.68 (ms) (n = 8); these values were not different
(P > 0.8). For the non-Mauthner cells, however, the
difference in timing for acceleration and pressure in the strike
combination was 0.4 ± 0.24 ms (n = 10), whereas
for the suction combination, it was about three times longer, 1.23 ± ms 0.34 (n = 9). There was a significant difference (P < 0.016) between the two cases. This suggests that
non-Mauthner cells would fire later for real suction stimuli than for
strike stimuli but that there would be no difference for the Mauthner cells.
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DISCUSSION |
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In this paper, our main interest was to determine how the Mauthner
cell and other large, fast-conducting reticulospinal neurons responded
to acoustic components likely to be important in predatory attacks that
elicit escape. We were also generally interested in what responses to
these components could tell us about the acoustic innervation of these
cells and how sensorimotor integration is mediated by the BEN. Both the
auditory system of fish and the Mauthner cell system are well-studied
models for acoustic processing and central neurophysiological
mechanisms respectively (for reviews, see Eaton 1991;
Popper and Eaton 1995
). Relatively few experimental studies, however, have attempted to use the knowledge of fish hearing
to better understand Mauthner function and other central mechanisms
important in generating body movements. Conversely, understanding how
the Mauthner cell responds to acoustic stimuli may give insights into
central acoustic processing in fish although this was not our main
objective. This is the first study to characterize comprehensively the
response of the Mauthner cell to acoustic pressure. This is also the
first attempt to look at Mauthner cell responses to pure acceleration,
although technical limitations prevented us from fully analyzing the
frequency response to acceleration.
Population coding of escape response
Escape responses in goldfish are oriented directionally away from
aversive acoustic stimuli (review, Eaton et al. 1995a). Thus stimuli approaching from the head require larger initial turns
than stimuli from the tail. The first component of this response is a
bend of the fish's body away from the stimulus (C-bend). The angle
that the fish turns, and thereby the direction of the escape response,
depends on how long the fish maintains the C-bend. Larger turns require
that the C-bend be of longer duration than for smaller turns. This
escape behavior is generated by reticulospinal neurons of the type we
studied. Because the fish's escape is directionally oriented, and the
reticulospinal neurons activate the musculature responsible for the
C-bend, these reticulospinal neurons must code their firing
patterns as a function of stimulus location. But how does this occur?
We know that the Mauthner cell fires at very short latency to stimuli
presented on one side of the body, regardless of angle (Eaton
and Emberley 1991). Therefore it has a receptive field covering
180°. Foreman and Eaton (1993)
suggested from EMG
records that varying the number and timing of activation of
other reticulospinal neurons would permit the fish to produce a
variable trunk muscle contraction that depends on stimulus angle. The
increases in EMG duration and amplitude associated with larger angle
turns could be due to activating greater numbers of cells. In the
present study, we used only one acceleration axis, at 90° to the long axis of the fish. Thus cell number would not be a variable in our
experiment. If some BEN neurons fire later than others to prolong
muscle contractions, then we would expect there to be differences in
the activation latencies of Mauthner and other BEN cells to acoustic
stimuli (acoustic pressure and acceleration). Consistent with this
hypothesis, we observed that there exists a distribution of minimum
onset latencies among the reticulospinal neurons sampled. How does this
distribution of latencies correlate with the behavior?
In the present study, we selected for cells that responded to both
acoustic pressure and acceleration. Because the acoustic endorgans code
the axis of direction (Fay 1984), selective innervation of BEN neurons by subsets of these afferents would result in their having different stimulus angles to which they respond. Our
acceleration was 90° to the long axis of the fish. Thus assuming
these cells have receptive fields (O'Malley et al.
1996
), we selected for those having fields that included 90°
(though perhaps this was not the preferred angle for all cells). Thus
our sample may have been biased toward cells that would code a 90°
turn. If the way that the system produces larger turn angles is to
recruit additional cells later, the distribution of activation
latencies we observed to an acceleration at 90° should correlate with
the duration of the EMG during a 90° turn. In fact, for an average
turn angle of 82°, a corresponding EMG duration of 12 ms was observed
previously (n = 53) (Foreman 1991
, p.
151, 159; Foreman and Eaton 1993
). This EMG duration is
close to the range of minimum latency values that we observed, lending
further support for the hypothesis that the system codes turn angles by
recruiting members of the BEN at different latencies. It is also
interesting that the distribution of minimum activation latencies for
pressure, a nondirectional stimulus (Schuijf 1981
),
was similar to that for acceleration. This may suggest an obligatory
temporal relationship between the time of arrival of the two components
in neurons of the BEN, and indicate how this system is using the
encoded acoustic information. This point is discussed in the following text.
For both acoustic pressure and acceleration stimuli, we also observed
that the minimum response latency for the Mauthner cell was ~1 ms
faster than the average, minimum latency for the non-Mauthner cells.
This is consistent with the model in which other members of the BEN are
slower but contribute to the production of larger more sustained
responses and consequently larger turn angles. The shortest latencies
we observed are probably via direct acoustic afferent input. Longer
latencies may be due to conduction delay lines in these pathways. For
instance, built-in delays (using internode lengths) have been described
in auditory pathways (for a review, Carr 1993). Another
possibility is synaptic delays, perhaps in auditory nuclei (Lee
et al. 1993b
), although there is no physiological information
on these input pathways to the Mauthner or other reticulospinal cells.
Regardless of the origin of the delays, different latencies between the
cells would be magnified further at the level of the spinal motor
neurons because of differential spinal conduction velocities among
these cells (Eaton et al. 1995b
), and the organization
of the spinal circuits (Fetcho 1991
).
Given that we were recording subthreshold events in axons of
reticulospinal neurons that have smaller diameter axons than the
Mauthner cell (and originate at different segmental levels of the
medulla), one might speculate that the subthreshold events in the
non-Mauthner cells were undergoing significant electronic decay before
reaching our recording site, as compared with the Mauthner cells. This
potentially could have resulted in latency measurements that appeared
longer than they actually were because significant decrement of the
signals could make it difficult to discern their actual onset times. We
do not think this is the case, though. Eighth-nerve evoked excitatory
postsynaptic potentials (EPSPs) were, in fact, found by Funch
and Faber (1980) to decrement by only 30-35% from Mauthner's
axon hillock to a recording site 2-2.5 mm distal in the axon, similar
to our recording position. For the non-Mauthner cells we estimated
the length constant,
, and probable PSP decrement, by using the same
specific axonal membrane resistance as reported for the Mauthner cell
(Rm = 22 k
cm2) (Funch and Faber 1982
),
and a calculated value for the internal resistance determined from
and Rm. The next 30 largest axons after
Mauthner, from which we believe we were sampling, have axon diameters
of 12-14 µm (Eaton et al. 1995b
). We therefore can
estimate
in these cells to be 2.7-3.0 mm, compared with 4.9 mm in
the Mauthner cell (Funch and Faber 1980
). Thus our
recording site (1.5-3.0 mm from the somata of these cells) is within
0.5-1.0
of the spike initiating zones of these cells. We therefore
estimate that the evoked potentials will have decremented by no more
than 39-63%, depending on the likely location of the cells. This
would not be enough to affect the accuracy of our latency measurements. Consequently, our measurements of onset latencies reflect actual differences in the responses of these cells to the acoustic components.
Directional sound sensing
We do not yet know that the reticulospinal neurons can solve
directional discrimination, but our data are consistent with this
hypothesis. To illustrate the mechanism of sound source localization under water, it is helpful to contrast a fish's detection of two types
of predatory attacks, those that capture by striking (Katzir and
Intrator 1987) and by sucking (Lauder 1985
).
This is diagramatically shown in Fig. 9.
If a predator strikes at the fish from the left side, it produces an
initial acoustic wave in front of it that propagates at the speed of
sound (1,500 m/s) toward the prey. This is symbolized by the ball
moving toward the fish in Fig. 9A1. This accelerates the
prey fish away from the strike (because the fish is neutrally buoyant
in the water). Using our conventions, the direction is ALR. In
addition, the water would be initially compressed, or positive in
acoustic pressure (P+; note the pressure waveforms in Fig. 9 show the
change in pressure,
P/
t). With this
combination, ALR/P+, the left Mauthner cell should activate a turn to
the right, away from the predator. Alternatively, as shown in Fig.
9A2, if a predator on the left used a sucking mechanism, the
acceleration would be in the opposite direction (ARL) but the initial
acoustic pressure would be rarefactive (P
). With this combination,
ARL/P
, the left Mauthner cell should also activate a turn to the
right, away from the predator. Thus there are two combinations that
should activate the left Mauthner cell (ALR/P+, ARL/P
). Conversely,
for a predator on the right side (Fig. 9B, 1 and
2), there are two other combinations (ARL/P+ and ALR/P
) to
which the left cell would not respond, because the waveforms are out of
phase. This gives a total of four combinations, two of which activate
the left cell and two which do not. This discrimination between the
four components is equivalent to the XNOR logical operator
(Eaton et al. 1995a
).
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This XNOR discrimination could be solved either by a network or a
single cell (Eaton et al. 1995a). In a network solution, the problem is solved by the interactions between multiple cells. Thus
it is not necessarily required that neurons of the BEN receive independent afferent input conveying all four polarities of acoustic pressure and acceleration (P+, P
, ARL, ALR (Eaton et al.
1995a
; Guzik et al. 1999
). However, a single
cell solution does require a cell to receive information
from these four phases. (In this case, the solution would result from
differences in processing of the inputs in the cell.) Thus if the
reticulospinal cells did not receive all four polarities, we could rule
out the single cell solution. We observed, however, that all recorded
cells responded to both onset phases for the two components (P+ and
P
, and ALR and ARL). Furthermore we observed no differences in the
amplitude or waveform of responses of cells to P+ versus P
or to ALR
versus ARL. We know that the cells are responding to the two
phases of each component because the difference in latency for each of
the polarities was less than one half a wavelength of the stimulus frequency (when the wave reverses polarity). The greatest difference in
latency for P+- and P
-evoked PSPs was 0.25 ms, which would correspond
to a frequency of 4,000 Hz, higher than the acoustic sensitivity of the
fish. Most cells had latency differences of
0.05 ms for the two
polarities. For acceleration, the greatest difference in latency for
ALR and ARL evoked PSPs was 3 ms. For a 125-Hz acceleration signal,
half a cycle would be 4 ms. Thus these cells appear to be receiving
inputs from both P+ and P
afferents and from both ALR and ARL
afferents. Although we do not yet know that these cells solve XNOR,
they receive the components they would need to perform the computation.
The timing of these inputs also should be appropriate for cells to solve this XNOR discrimination because the cell needs to compare the phases of the acoustic pressure and acceleration stimuli produced at the same time. In particular, the minimum onset latency for PSPs evoked by acoustic pressure should be similar to those evoked by acceleration, so that the cells can make the right directional "decision" soon after stimulus onset. Because there is a distribution in the minimum time of arrival of acceleration inputs to members of the BEN, which presumably code the duration of the EMG and consequently the escape trajectory, there would thus need to be a corresponding distribution in the time of arrival of acoustic pressure inputs. This would be the simplest way to allow the cell to compare the phases of pressure and acceleration stimuli.
For the Mauthner cell, and about half of the non-Mauthner cells we
recorded, the difference between minimum onset latency for
acceleration- and acoustic pressure-evoked PSPs was 1 ms. We believe
this is fast enough that these cells could solve XNOR (Casagrand
et al. 1998
; Guzik et al. 1997
). Presumably when
acoustic pressure and acceleration cues occur together, rather than
individually as in this study, the cell will show a different response
to combinations coding for sound sources on the left versus the right.
Thus for example, we might expect cells causing a turn to the right to give bigger PSPs to ALR when combined with P+ (left sound source) than
to ALR with P
(right sound source). To mediate such an effect, the
Mauthner cell has voltage-dependent K+ channels
(Faber and Korn 1986
) that could provide it with
nonlinear summation properties to solve this computation (see for
example, Margulis and Tang 1998
; Mel et al.
1998
).
Where the difference was >1 ms, the cells may not solve XNOR. We chose 1 ms because, in the best frequency range of the Mauthner cell, ~400-800 Hz, the change in polarity of pressure occurs in 0.625-1.25 ms (and the peak of the initial wave would occur in half of that time). So any difference of 1 ms or longer in the response to acoustic pressure and acceleration is relatively long with regard to the phase relationships of these stimuli, since the next pressure-evoked PSP will have occurred in that time. Thus the difference may be too long to allow directional discrimination.
For about half of the non-Mauthner cells we recorded, there was this
much difference or more, which would make the timing of the inputs
problematic for localization, particularly for sources beginning with
P, or suction. While there was no difference in the latencies to P+
versus P
, cells responded >1 ms faster, on average, to one direction
of acceleration (Fig. 8D). In particular, the difference in
minimum onset latency to acceleration and acoustic pressure stimuli was
three times longer for the hypothetical suction combination, than for
the strike combination (see RESULTS). For the strike
combination, these cells had a difference in latency that was similar
to the Mauthner cells. These cells thus would respond appropriately in
the case of a striking predator approaching from the opposite side of
the body, if we presume that they are like the Mauthner axon and excite
motoneurons on the same side of the spinal cord, thus causing a turn
toward that side. However, these cells would respond inappropriately in
the case of a sucking predator attacking on the same side of
the body as their axon; that is, they would cause a turn toward the
predator, all else being equal.
It is probably not necessary that all reticulospinal cells have the
ability to solve XNOR. This problem could be resolved by a small
population of XNOR-solving cells regardless of initial polarity or
predatory strategy. These cells could modulate, or bias, the activation
of the rest of the circuit, and thereby control the non-Mauthner cells,
which would otherwise be activated inappropriately. This would govern
the direction of the escape turn. There is, in fact, already evidence
that the Mauthner cell can strongly inhibit non-Mauthner responses
(DiDomenico et al. 1988; Eaton et al.
1984
; Foreman 1991
). Our present data are
consistent with the idea that the Mauthner cell can solve XNOR. It thus
could control the non-XNOR solving cells for cases where they would fire inappropriately. Because there was no difference in the amplitude of the PSPs in response to acceleration from the two directions in the
non-XNOR solving cells, the cell's homologue would receive enough
activation to fire and contribute to the response.
In summary, Mauthner and some reticulospinal cells have the appropriate sensory input that they could solve the XNOR discrimination. These cells also have the output representation, in terms of distributed onset latencies, to use that information to effect a directionally oriented escape trajectory. Therefore they are capable of performing the sensorimotor transformation necessary to produce a directed escape response following an aversive acoustic stimulus.
Intensity of acoustic pressure and acceleration stimuli
We observed that for both acoustic pressure and acceleration, the
latency to onset and the amplitude of PSPs were correlated with the
intensity of the stimulus. The latency decreased and the amplitude
increased, as the stimulus intensity increased (Figs. 3 and 7). This is
consistent with earlier behavioral observations (Eaton et al.
1984). The amplitude of PSPs to sound pressure in the Mauthner
cell saturated when presented in the absence of acceleration. Although
the cell was brought close to spike threshold, our findings suggest
that acoustic pressure-evoked PSPs contribute a large part in
depolarizing the cell near, but not above, threshold, in these
anesthetized fish. Acceleration, on the other hand, appeared to
contribute a smaller part in depolarizing the cell. However, in the
range tested, the cell's response to acceleration did not saturate, as
it did for acoustic pressure. Therefore it would appear from our data
that acceleration should bring the cell above threshold in the presence
of acoustic pressure stimuli. This conclusion is consistent with the
behavioral observations of Lewis and Rogers (1998)
, and
with the phase model, which requires both components to compute sound
source location.
Taken together, our neurophysiological findings and earlier behavioral
studies give new insights into the stimuli that activate the Mauthner
cell and thus help in understanding its underlying neurophysiology. The
acoustic pressure intensities we used were similar to those used in
other studies to activate the escape behavior (Blaxter et al.
1981; Canfield and Rose 1996
; Eaton et al. 1988
; Lewis and Rogers 1998
), although there
were some informative differences. In these earlier behavioral studies,
escape responses to sound pressure occurred in the range of 140-150 dB
(re 1 µPa) (Blaxter et al. 1981
; Canfield and
Rose 1996
; Lewis and Rogers 1998
), 10-20 dB
above the level at which the Mauthner cell response saturated (~130
dB re 1 µPa). Why is there a difference?
Direct observation by Canfield and Rose (1996) showed
that a predatory strike by a largemouth bass (Micropterus
salmoides) produced an acoustic pressure intensity of 170 dB (at
~200 Hz). If pressure-evoked PSPs kept increasing in amplitude,
pressure alone would drive the cell above threshold. This would not be desirable because the cell would be ignoring the acceleration component, which would provide crucial information necessary to direct
the response away from the attack.
The acceleration intensities we used were also similar to those in
other studies (Blaxter et al. 1981; Lewis and
Rogers 1998
), again with some informative differences. In
goldfish,1 the
threshold acceleration to elicit escape was 0.03 m/s2, and in
herring2 it was ~0.06
m/s2, a similar value. This is comparable to the lower
end of our acceleration intensities which began at around 0.01 m/sec2 for just detectable PSPs, and extended up to around
3 m/sec2, where the intensity/amplitude
relationship flattened out (and where we were no longer able to hold
the cells, Fig. 7). Near threshold (0.03 m/s2),
Lewis and Rogers (1998)
observed that behavioral
responses were randomly directed but that higher accelerations were
required to produce directional
responses.3 The acceleration
produced during the bass strike, mentioned above, can be used to place
these figures in context as well. We calculate that the bass would have
produced an acceleration of
4.5 m/s2 during its
strike.4 Thus our
observed values make sense, because the cell should respond at levels
lower than those associated with a predatory strike.
It is important to note that the actual hydrodynamic acceleration due
to the bass strike may be higher than the 4.5 m/s2 value we cited, however. This is because the
strike may be accompanied by additional acceleration due to fluid flow
(a bow wave) (G. I. Cummins, personal communication; see also
Bleckmann et al. 1981; Brown 1995
). This
would be detected by otolithic receptors but is not included in our
calculated acoustic acceleration. This component may be of relatively
low frequency, such as that produced by fish during fast starts (see
Bleckmann et al. 1991
), which are comparable with the
dynamics of some predatory strikes (Eaton and Bombardieri
1978
; Eaton and Hackett 1984
). Thus the
frequency sensitivity of the Mauthner cell to this component may not be the same as to acoustic pressure. This suggests that the pressure and
acceleration components for a predatory stimulus do not necessarily have either the amplitude or frequency relationships predicted from
well described, but artificial sound sources.
We reach similar conclusions if we use our values of pressure or
acceleration and calculate what the corresponding pressure and
acceleration would have been if we had used an underwater source. For the calculations, we will assume that the source would produce a spherically propagating wave (Pierce 1981). On
one hand, at the acoustic pressure where the PSP amplitudes saturated,
the acceleration would be 0.04 m/s2 if this had
been an underwater
source.5 This
value is close to threshold for eliciting escape but, as mentioned
earlier, probably too low to elicit directionally correct responses. On
the other hand, at the intensity where we observed the
acceleration-evoked PSP amplitudes to flatten out (1 m/s2), the corresponding pressure from an
underwater sound source would be 157 dB.6 This value is
higher than that required to saturate Mauthner PSPs.
Combined, these calculations also support the notion that a natural
source that would maximally activate the Mauthner cell should have a
relatively high ratio of acceleration to pressure.
We conclude from these considerations that the relevant stimulus for
the Mauthner system, and for activating directional escape responses,
is a combination of acoustic pressure and intense acceleration. There
may be a contribution to acceleration from fluid flow. These components
may not have the same frequency spectra, and their amplitudes may not
resemble those expected from standard artificial sources. In this
context, it would be particularly interesting to use the method of
Bleckmann et al. (1991) to measure particle acceleration
in response to predatory strikes, and also to determine the frequency
response of the Mauthner cell to relatively high levels of
acceleration, such as used in our study.
Auditory inputs to the Mauthner cell
All the cells adapted rapidly with a more-or-less phasic response
to pressure and more sustained responses to acceleration. We did not
attempt to quantify this difference, but it is likely a result of the
afferents providing the input to these cells rather than a function of
the stimuli we presented. It is believed that the saccule predominantly
codes pressure and that afferents from the saccule, particularly S1
afferents, which are known to be more phasic in their responses to
pressure (Furukawa and Ishii 1967; Furukawa and
Matsuura 1978
; Fay 1978
), provide input to the
Mauthner cells. Acceleration transduction probably occurs predominantly
in another endorgan (utricle or lagena), and different afferents, with
apparently more tonic responses (Fay 1978
) are activated, which then provide input to the Mauthner cell. We believe this is the basis for the different duration responses we saw to
acoustic pressure and acceleration stimuli.
Fishes are similar to other vertebrates in their hearing capabilities
(see Fay and Popper 1980, for a review). Goldfish have a broadly tuned
behavioral audiogram, extending from <50 Hz up to 2-3 kHz. In
addition, the behavioral audiogram for escape is broadly tuned in the
herring (Blaxter et al. 1981
), another hearing specialist. It is possible that the Mauthner cell could have been broadly tuned as well. Because the cell responds within only a few
milliseconds of the stimulus, acoustic frequency might seem to be
unimportant to the cell. Thus the cell might respond only to rise time
of the stimulus, which depends on both frequency and intensity. If this
was the case and because the auditory afferents can phase lock up to
2,000 Hz (Fay 1995
), we would expect, for example,
responses to 1,000 Hz to be better than 500 Hz for the same intensity.
Alternatively, it could be that the Mauthner cell might prefer special
frequencies, perhaps characteristic of some feature of the aversive
stimuli that activate the escape behavior. Thus we were interested in
whether the Mauthner cell displayed any frequency preferences. This
would also inform us about its auditory afferent inputs.
The Mauthner cell response to acoustic pressure appears to be
representative of the tuning of the saccular afferents, which presumably represent the frequencies of most importance to the animal.
Goldfish saccular afferents display a Q10dB of
0.2-2.4 with an average of ~0.7 (Fay 1995). Our
observations were within this range, with a Q10dB of
0.5-1.1, over the best frequency range. The saccular afferents in
goldfish are nonhomogeneous. They can be classed broadly into two basic
types relative to tuning characteristics: low frequency and medium to
high frequency (Fay 1978
; Furukawa and Ishii
1967
). Some of the cells fire only to the compression phase
(P+), others to the rarefaction phase (P
), and some to both. The
responses that we observed best fit the description of S1 afferents,
and closely resemble those recorded intracellularly from S1 afferents
(Furukawa and Ishii 1967
; Sento and Furukawa 1987
). The responses of the reticulospinal cells to acoustic
pressure were fast adapting, broadly tuned, with best frequencies
between 400 and 800 Hz for both polarities of acoustic pressure. The
response of the cells dropped off rapidly >500 Hz with little evidence for responses >3,000 Hz. The S1, or high-frequency, afferents are fast
adapting, nonspontaneous, high-threshold, with best frequencies between
300 and 800 Hz (Furukawa and Ishii 1967
; Fay
1978
: Furukawa and Matsuura 1978
), and have been
reported to project to the Mauthner lateral dendrite (Furshpan
1964
; Nakajima 1974
; Zottoli
1978
; Zottoli et al. 1995
). In addition, the S1
afferents are large diameter (15 µm), which, because of their high
conduction velocity, would be desirable for inputs to an escape system.
Finally, the general shape of the tuning curves also suggest that the
Mauthner cells are receiving inputs from the medium-to-high tuned class of saccular afferents (Fay 1995
).
We observed that the best frequency of the Mauthner cell varied over a
range (400-800 Hz) depending on the initial polarity and the intensity
of the acoustic pressure. A similar shift in best frequency of saccular
afferents was also observed by Lu and Fay (1993) when
intensity was increased. From this we would conclude that shifts in
best frequency in our reticulospinal cells may be a property of the
afferents. Given the differences we observed with the different
polarities, we may have been recruiting different populations of
afferents. This perhaps is not surprising given that the afferents are
not a homogenous population, having different response properties and
thresholds, with some of them responding only to the compression phase
some only to rarefaction.
Concluding remarks
Mauthner cells are more widely distributed and play more general
roles than previously thought. For instance, it is now believed that
Mauthner cells activate rapid hindlimb contractions associated with the
diving escape response in some adult frogs, such as Xenopus (Will 1991). Thus there may be interesting similarities
in activation and control of limb and trunk musculature by the
reticulospinal system. Furthermore Mauthner cells function not only in
escape but also in the rapid acceleration during the terminal phase of prey capture (Canfield and Rose 1993
). Thus implications
of reticulospinal functions from Mauthner studies may be more generally
based than previously thought. Added to this is the remarkable fact
that the spinal cord central pattern generating circuits for trunk movements are similar in amphibians, lamprey, and fish (for a review,
Fetcho 1991
), all animals that have Mauthner neurons
(Currie 1991
; Will 1991
; Zottoli
1978
). Mauthner cells have not been described in mammals, but
giant reticulospinal cells of the caudal pontine reticular nucleus are
now known to be the primary interneurons in the basic acoustic startle
circuit in rats (Lingenhoehl and Friauf 1994
). Mauthner
cells are similar to these mammalian reticulospinal cells in having a
high firing threshold, and in the present paper we add to this
observation by showing that they have broad frequency tuning. These
characteristics seem to be generally important in initiating the first
phase of rapid locomotion, and the presence of such cells in fish and
mammals suggests that this concept may extend across the vertebrate
classes. Thus insights into sensorimotor coordination, such as the main
findings of this study, may provide general clues to the nature of
sensory processing and motor function in the reticulospinal system.
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ACKNOWLEDGMENTS |
---|
We thank Drs. Thomas Geers and Peter Rogers for comments regarding various aspects of this work, Dr. Tom Finger for providing histological sections of the goldfish MLF, and E. M. Kraska for technical assistance. We also thank G. I. Cummins for mathematical solutions to hydroacoustic equations and for suggesting the possible importance of fluid flow acceleration in activating the Mauthner cell.
This work was supported by grants from the Office of Naval Research (N00014-94-19-0380) to R. C. Eaton and the National Science Foundation (IBN-9723527) to J. L. Casagrand and R. C. Eaton.
Present address of A. L. Guzik: Johns Hopkins University, Cognitive Science Dept., 243 Krieger Hall, 3400 N. Charles St., Baltimore, MD 21218.
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FOOTNOTES |
---|
Address for reprint requests: J. Casagrand, Dept. of Biology, EPO, University of Colorado, Boulder, CO 80309-0334.
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.
1
Calculated from the acoustic pressure by Lewis
and Rogers (1998),
30 dB re 1 m/s2, at 43 cm, in
the horizontal plane.
2
We calculated from velocity data in Fig. 2B of
Blaxter et al. (1981),
25 dB re 1 m/s2, at
a mean distance of 1 cm.
3
Directional responses probably required considerably
higher stimulus values than the 0.06 m/s2 we estimated from
the Blaxter et al. (1981) experiment because fish closer
to the source would have experienced higher accelerations and would
have responded first. It is difficult to know by how much higher our
estimate should be because of uncertainties about the characteristics
of the stimulus and which fish in the test tank responded first.
4 For a spherically propagating sine wave at 10 cm distance, 200 Hz (G. I. Cummins, personal communication).
5
Calculated for a spherically propagating wave at 10 cm
and 500 Hz, the best frequency for the Mauthner cell, 29 dB re 1 m/s2, (G. I. Cummins, personal communication).
6 Re 1 µPa at 10 cm, and 125 Hz, the frequency we used, for a spherically propagating wave (G. I. Cummins, personal communication).
Received 21 September 1998; accepted in final form 17 May 1999.
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REFERENCES |
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