Mauthner and Reticulospinal Responses to the Onset of Acoustic Pressure and Acceleration Stimuli

Janet L. Casagrand, Audrey L. Guzik, and Robert C. Eaton

Integrative Physiology and Neurobiology Section, Department of Biology, E.P.O., University of Colorado at Boulder, Boulder, Colorado 80309-0334


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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).


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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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Fig. 1. Experimental system. A: goldfish were secured in a sliding holder and axonal recordings of excitatory postsynaptic responses (PSP) were made from the Mauthner cell (MC) and other reticulospinal axons in response to acoustic pressure and acceleration stimuli. To localize and identify the axons, they initially were stimulated antidromically from the spinal cord. Acoustic pressure stimuli were produced by a loud speaker in air, in front of the fish. These stimuli are transduced by the fish using the swimbladder (SB) and Weberian ossicles (WO) that convey the acoustic pressure component to the fluid of the inner ear. For P+ stimuli, the initial half cycle was compressive; for P-stimuli, the initial cycle was rarefactive. Acceleration stimuli were produced by two force transducers that moved the entire fish holder; ALR stimuli were with the initial half-cycle from left-to-right; ARL were with the initial half-cycle from right-to-left. The direction of displacement is the same as for the acceleration, initially. Although in this paper we are assuming these components are transduced by the inner ear, involvement of the lateral line cannot be ruled out (Coombs et al. 1989), but for the purposes of our study this distinction is not crucial because the underlying theory of sound localization would be the same (Eaton et al. 1995a). B: dorsal view diagram of the goldfish reticulospinal system (rostral is to the top). C: cross-section through the dorsal bundle of the medial longitudinal fasciculus in the mid-vagal lobe region where our recordings were done from the Mauthner axons (MA), which is surrounded by a thick myelin sheath, and other reticulospinal cells. Profiles of some of the larger reticulospinal neurons also are shown. A, derived from Canfield and Eaton (1990); B: derived from Lee et al. (1993); C: drawn from a microscope slide provided by Dr. Thomas E. Finger.

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 MOmega . 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 Omega , 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 MOmega ) 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).


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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, lambda , (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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Fig. 2. Responses of the Mauthner cell to acoustic pressure pulses of 10 ms in duration and 500 Hz. A: examples of the ON responses to acoustic pressure stimuli. Upper traces: waveforms of the tone pulse beginning with either positive or negative pressure (i.e., P+ or P- stimuli) recorded with a hydrophone. Lower traces: corresponding PSPs in the Mauthner cell. The cell's response was very similar whether the tone began with either P+ or P-. B: examples of relationship between stimulus strength and PSP amplitude and latency. * indicates intensity was extrapolated. Upper traces: sample stimulus waveform. Lower traces: PSPs in the Mauthner cell to tone pulses of the 3 different intensities (dB re:1 µPa) indicated in the figure.

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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Fig. 3. Graph showing change in latency and amplitude of PSP with acoustic pressure stimuli of different intensities. Data are taken from 1 cell. As stimulus intensity increased, PSP latency decreased from ~4.5 ms to a minimum of 1.4 ms where the curve became flat (~125 dB). As intensity increased, amplitude increased to a maximum of close to 9 mV. Amplitude curve also reached a plateau at ~125 dB. Thus the Mauthner cell PSP amplitude and latency seem to code stimulus intensity.

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 lambda  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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Fig. 4. Histograms showing distribution of onset latencies for all reticulospinal cells responding to tone pulse onsets of the type shown in Fig. 2. A and B: latencies to either P+ or P-, respectively. Latencies in this entire population varied considerably from ~0.7 ms to >7.6 ms (*, data point off scale). Small difference between the population means to P+ and P- (1.89 and 1.96 ms) was not statistically different. C: distribution of differences in latencies to P+ and P- for individual cells. When subtracting the latency to P+ from the latency to P- for each cell, most showed no difference (0 ms), though for a few P+ was slightly faster than P-. Numerical values in figure are means for the total population ± SEM.

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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Fig. 5. Tuning curves for Mauthner PSPs to acoustic pressure pulses from 100 up to 3,000 Hz. Values were interpolated from a series of test stimuli at predetermined intensities. A: tuning curves of PSPs to positive-onset acoustic pulses. This diagram represents the sound pressure level at a given frequency necessary to generate a PSP of a particular amplitude. Threshold for Mauthner firing is ~11 mV above rest. The cell appears to be broadly tuned. B: tuning curves of PSPs to negative-onset acoustic pressure pulses. Conventions are the same as in Fig. 5A, and the data are taken from the same cell. Note that the best frequency is shifted to higher values for negative-onset pressure compared with positive-onset. These data are taken from the same cell as in Fig. 2.

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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Fig. 6. Responses of the Mauthner cell to acceleration stimuli with an initial frequency of about 125 Hz. A: examples of the responses to acceleration. Upper traces: waveforms of the acceleration beginning either right-to-left (ARL) or left-to-right (ALR) recorded with an accelerometer. Lower traces: corresponding PSPs in the Mauthner cell. As with acoustic pressure, the cell's response was very similar in amplitude and waveform whether the initial acceleration was ARL or ALR. B: examples of relationship between stimulus strength and PSP amplitude and latency. Upper traces: sample stimulus waveform. Lower traces: PSPs in the Mauthner cell to ARL acceleration of the 3 different intensities indicated in the figure (which correspond to -20, -12.4, and 0 dB re 1 m/s2).

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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Fig. 7. Graph showing change in latency and amplitude of PSP with acceleration stimuli of different intensities. Data are taken from 1 cell. As stimulus intensity increased, PSP latency decreased from ~7.5 ms to a minimum of 3.5 ms. As intensity increased, amplitude increased to a maximum of close to 6 mV. Notice that the amplitude curve was still increasing at the upper end of the stimulus range (1 m/s2; 0 dB re 1 m/s2). As with acoustic pressure, the Mauthner cell PSP amplitude and latency seem to code stimulus intensity.

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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Fig. 8. Histograms showing distribution of onset latencies for all reticulospinal cells responding to acceleration stimuli of the type shown in Fig. 6. A and B: latencies to either ALR or ARL. Latencies in this entire population varied considerably from ~0.7 to >9.8 ms (*). Difference between the population means to ALR and ARL (2.85 and 2.0 ms) was not statistically different. Difference in the means of the distributions in A and B is due to the non-Mauthner cells, the average population latencies of which were 3.52 ± 0.83 ms (n = 10) for ALR and 2.08 ± 0.2 ms (n = 9) for ARL. This difference in the mean latency for the 2 polarities was not statistically significant (P = 0.19, t-test). C: distribution of differences in latencies to ALR and ARL for individual cells. When subtracting the latency to ARL from the latency to ALR for each cell, ~30% showed no difference (0 ms), though for the remainder 1 direction was faster or slower than the other. D: distribution of differences in latencies for individual non-Mauthner cells to the 2 directions of acceleration, considered relative to the side the axon occurred on. Here the difference in latency is for acceleration of the fish away from the side of the axon was on minus acceleration of the fish toward the side the axon was on. Numerical values in figure are means for the total population ± SEM.

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 (chi 2, P < 0.05), but not the Mauthner cells (chi 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.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

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, lambda , and probable PSP decrement, by using the same specific axonal membrane resistance as reported for the Mauthner cell (Rm = 22 kOmega cm2) (Funch and Faber 1982), and a calculated value for the internal resistance determined from lambda  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 lambda  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 lambda  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, delta P/delta 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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Fig. 9. Diagram symbolizing phase model detection of 2 types of predatory attacks, those that capture by striking, symbolized by the ball moving toward the fish (A1 and B1) and by sucking, symbolized by the ball moving away from the fish (A2 and B2). Fish profiles contain the representations of the 2 Mauthner cells, with crossed axons that descend the spinal cord. Waveforms represent the change in pressure (delta P/delta t) and the acceleration with the symbol representing the direction of the initial change in pressure (P+ or P-), or direction of acceleration (ALR, ARL). For a source on the left (A, 1 and 2), the left cell will detect the phases for change in pressure and acceleration as being the same (either both initially positive, or both initially negative). For a source on the right (B, 1 and 2), the left cell will detect the phases for a change in pressure and acceleration as being opposite in polarity. Portions of this figure are reprinted from Guzik et al. (1999) with approval.

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.


    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.


    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.


    REFERENCES
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ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
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0022-3077/99 $5.00 Copyright © 1999 The American Physiological Society