Processing of Single-Frequency Dipole Stimuli

Numerous studies have investigated the responses of lateral line neurons to single-frequency stimuli generated by a stationary dipole source (i.e., a small sinusoidally vibrating sphere). Dipoles sources generate well-defined and highly reproducible stimuli (Harris & van Bergeijk, 1962; Kalmijn, 1988, 1989). Lateral line afferents respond to a dipole stimulus with a change in their ongoing firing rate (e.g., Mogdans & Bleckmann, 1999). Fibers are sensitive to peak-to-peak water displacements as low as 0.01 μ‎m (at 100 Hz) at the surface of the skin and to frequencies between less than 1 Hz up to at least 200 Hz, and they exhibit phase-coupled action potentials (Sand et al., 1975). Phase coupling is weak at low stimulus amplitudes and increases with increasing stimulus amplitude until it reaches saturation (Figure 3). Firing rate also increases with increasing stimulus amplitude but only at stimulus levels that are about 10 dB greater than those causing significant phase coupling (Mogdans & Bleckmann, 1999). Thus, the amplitude of a dipole stimulus is represented both by the degree of phase coupling and by firing rate (e.g., Coombs et al., 1998; Mogdans & Bleckmann, 1999).

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Figure 3. Response of a goldfish afferent nerve fiber to the stimulation of the lateral line system with sinusoidal water motions. A: Original recording. Spike train (red), raster plot (each marker represents one action potential) across five stimulus presentations (black) and stimulus trace (blue) (50 Hz, peak-to-peak vibration amplitude 4 μ‎m) are shown. B: Level-response function. Discharge rate (red line connecting circles re: left-hand axis) and synchronization coefficient R as a measure of the strength of phase coupling (blue line connecting triangles re: right-hand axis) are plotted as function of stimulus level (in dB). An attenuation of −20 dB corresponds to a peak-to-peak sphere displacement of 425 μ‎m.

Modified from Mogdans and Bleckmann (1999).

When stimulated with different frequencies but equal displacement amplitude, two types of afferent fibers can be distinguished based on the slopes of the level-response functions, the frequency of best sensitivity, and the angle of phase coupling (e.g., Engelmann et al., 2000; Kroese & Schellart, 1992; Montgomery & Coombs, 1992; Montgomery et al., 1994; Münz, 1985). In the study by Kroese and Schellart (1992), response magnitude in one type of fiber increased by about 20 dB with a tenfold increase in frequency, best sensitivity was between about 25 Hz and 60 Hz, and action potentials exhibited a phase lead of about 120° with respect to sphere displacement for low frequencies (Figure 4). These values are typical for a velocity detector (Kalmijn, 1989); that is, fibers of this type presumably innervate SNs. In the other type of fibers, response magnitude increased by about 35 dB with a tenfold increase in frequency, best sensitivity was between 70 Hz and 120 Hz, and action potentials exhibited a phase lead with respect to sphere displacement of 180° (Kroese & Schellart, 1992). These values are characteristic of an acceleration detector (Kalmijn, 1989); that is, fibers of this type most likely innervate CNs.

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Figure 4. Frequency response functions of trout afferent nerve fibers obtained by stimulating the lateral line system with sinusoidal water motions. A: Data from four fibers innervating superficial neuromasts. B: Data from four fibers innervating canal neuromasts. Different colors refer to different fibers. Identical colors in A and B do not refer to the same fiber. Gain is given in arbitrary decibels. For clarity, some of the curves are shifted vertically. Phase is given in degrees with respect to displacement. Phase values measured below −180° were shifted up by 360°.

Redrawn and modified from Kroese and Schellart (1992).

In most studies, the temporal response patterns of afferent nerve fibers have been described as sustained with little or no adaptation to sustained sinusoidal stimulation, thus representing stimulus duration (Coombs et al., 1998; Mogdans & Bleckmann, 1999). One study, however, showed that afferent nerve fibers of the lateral line, just like afferent nerve fibers in the auditory system of vertebrates (fishes: e.g., Coombs & Fay, 1987; Fay, 1985; Kuno, 1983; frogs: e.g., Megela & Capranica, 1981, 1982; mammals: e.g., Kiang et al., 1965; Westermann & Smith, 1984), can exhibit adaptive responses to sine-wave stimuli (Mogdans et al., 2017). Adaptation increases with increasing stimulus level and frequency and with increasing rise time of the stimulus envelope (Mogdans et al., 2017). This suggests that adaptation is a fundamental property implemented in the periphery of all hair cell–based sensory systems.

In contrast to afferent nerve fibers, the responses to sine-wave stimuli of brainstem neurons in the MON were characterized by greater degrees of adaptation and greater heterogeneity in terms of response patterns and phase coupling (e.g., Coombs et al., 1998; Künzel et al., 2011; Mogdans & Kröther, 2001). For instance, the temporal patterns of the responses of brainstem neurons range from primary-like sustained or adapting discharges to patterns that are considerably altered relative to the afferent input, like long-latency responses that take considerable time to reach a maximum, responses with periods of intermittent excitation and inhibition (Coombs et al., 1998), and responses that are characterized by a suppression of ongoing activity (Kröther et al., 2002). The meaning of the various types of central responses patters still needs to be disclosed.

Most notably, however, brainstem units are less sensitive to sine-wave stimuli than primary afferents (e.g., Caird, 1978; Coombs et al., 1998; Montgomery et al., 1996; Paul & Roberts, 1977; Wubbels et al., 1993). For instance, in goldfish, about 30% of the units in the MON do not respond to a stationary vibrating sphere, even when displacement amplitudes of up to 800 μ‎m are used (Mogdans & Goenechea, 2000). Such displacement amplitudes are substantially greater than those causing rate saturation in lateral line afferents. However, many of these seemingly insensitive units readily respond to the water motions generated by a non-vibrating sphere moving alongside the fish (Mogdans & Goenechea, 2000) (Figure 5). Similar data were obtained from units in the midbrain TS, where the vast majority (83%) of those units that responded reliably to a moving sphere did not respond to a stationary sphere vibrating with 160 μ‎m displacement amplitude (Plachta et al., 2003). These findings suggest that central lateral line neurons are not particularly well adapted for the analysis of sinusoidal water motions generated by a stationary wave source.

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Figure 5. Responses of lateral line units in the medial octavolateral nucleus of goldfish to the water motions generated by a sphere moving alongside the fish (left) and a stationary, sinusoidally vibrating sphere (right). Each graph shows a dot raster plot (black) of the responses to 10 stimulus repetitions and the corresponding peri-stimulus time histogram (red). A: Example of a unit that responded to the moving but not the vibrating sphere. B: Example of a unit that responded to both the moving and the vibrating sphere. Speed of moving sphere was 8 cm s⁻¹, displacement amplitude of sphere vibration was 270 μ‎m, and vibration frequency was 50 Hz.

Modified from Mogdans and Goenechea (2000).

Processing of Spatial Information

One of the fundamental tasks of a sensory system is to determine the spatial location of a stimulus source. Modelling data and neurophysiological recordings of peripheral lateral line responses to dipole stimuli originating from discrete spatial locations alongside the fish have shown that an array of neuromasts can encode amplitude, location, and vibration direction of a dipole source (Coombs & Conley, 1997; Coombs et al., 1996, 1998; Ćurčić-Blake & van Netten, 2006; Goulet et al., 2008). A vibrating sphere generates a distinct pressure gradient pattern or velocity field, the pattern of which changes in a predictable way with location (azimuth and elevation), distance, and orientation (vibration axis) of the sphere with respect to a given location on the fish surface (Figure 6). Size and shape of the receptive field of a single afferent nerve fiber (i.e., discharge rate, strength of phase coupling and phase angle) change exactly as predicted from the location, distance, and vibration axis of the sphere. In other words, the pressure gradient pattern or velocity field generated by a stationary dipole source is encoded by the pattern of excitation across an array of linearly arranged neuromasts (Coombs et al., 1996, 1998; Ćurčić-Blake & van Netten, 2006; Goulet et al., 2008).

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Figure 6. Predicted and measured spatial excitation patterns of lateral line nerve fibers in response to a sphere vibrating sinusoidally either parallel (0°) or orthogonal (90°) to the fish. Upper: Modelled pressure gradient patterns across a linear array of lateral line receptors and resulting predictions for excitation patterns. Lower: Measured excitation pattern of a goldfish lateral nerve fiber. Red: Predicted and measured discharge rates. Blue: Predicted and measured phase angles. Insets indicate direction of sphere vibration and corresponding iso-pressure contours.

Modified from Künzel et al. (2011).

In terms of receptive field shape and size, only a proportion of the units on the MON and the TS are primary-like, exhibiting receptive fields that are in agreement with predictions based on the spatial excitation pattern generated by a stationary dipole source (Figure 7). Most central units have receptive fields that are quite different from those of afferent nerve fibers and are thus more difficult to relate to the stimulus field (Coombs et al., 1998; Künzel et al., 2011; Meyer et al., 2012; Mogdans & Kröther, 2001). For example, many MON and TS units have fairly broad receptive fields (Figure 7), extending across much of the fish surface, which is indicative of integration across large portions of the lateral line periphery. Moreover, receptive fields of central units may consist of excitatory and/or inhibitory regions, a finding that is consistent with lateral inhibition underlying the shaping of these fields. Yet other units may have receptive fields that consist of two or more areas from which stimulation with the vibrating sphere causes an increase or a decrease in discharge rate.

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Figure 7. Measured spatial excitation patterns of lateral line neurons in the brainstem medial octavolateral nucleus of goldfish in response to a sphere vibrating sinusoidally either parallel (0°) or orthogonal (90°) to the fish. Upper: Example of a primary-like excitation pattern (see lower part of Figure 6). Lower: Example of a non-primary-like excitation pattern. Red: Discharge rates. Blue: Phase angles. Insets indicate direction of sphere vibration and corresponding iso-pressure contours.

Modified from Künzel et al. (2011).

In summary, most central lateral line units do not encode location and vibration direction of a stationary vibrating sphere. If this was the case, receptive fields should be small and tuned to a particular sphere location, distance, and/or motion direction. Exactly how and where in the brain spatial information is represented by the lateral line system is presently unknown. Perhaps this information is represented by a population code (i.e., by the joint activities of a number of neurons within a given brain nucleus).

Processing of Complex Hydrodynamic Stimuli

Most hydrodynamic stimuli are more complex than the single-frequency stimuli generated by a dipole source. For instance, a small object that is moved alongside the fish creates changes in water velocity consisting of an initial short transient and predictable component followed by an ill-defined long-lasting wake (Mogdans & Bleckmann, 1998). These changes are reflected in the discharges of afferent nerve fibers, which exhibit a characteristic succession of increased and decreased firing rates (Mogdans & Bleckmann, 1998; Mogdans & Geisen, 2009). As predicted from the intrinsic directional sensitivity of the hair cells within a neuromast, the order in which discharge rates of afferent fibers increase and decrease inverses when the direction of object motion is reversed (Figure 8). In addition to this transient response, many fibers discharge bursts of spikes even after an object has passed the fish, presumably in response to the wake generated by the object (Mogdans & Bleckmann, 1998). Afferents of this type most likely innervate SNs. In contrast, a smaller number of fibers do not respond after the object has passed the fish. These fibers presumably innervate CNs, which do not respond to the low-frequency, low-amplitude water motions generated by the object’s wake since they are filtered out by the lateral line canals (Denton & Gray, 1989; Engelmann et al., 2000).

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Figure 8. Responses of a goldfish posterior lateral line nerve fiber to an object passing the fish laterally with a speed of 15 cm s⁻¹. The graph shows peri-stimulus-time histograms (bin width 20 ms) of the responses to 10 stimulus presentations. Upper: Motion direction from anterior to posterior (AP). Lower: Motion direction from posterior to anterior (PA). The fish symbol indicates size, location, and orientation of the fish relative to the orbit of the moving object. Note that discharge patterns inversed when motion direction was reversed.

Modified from Mogdans and Bleckmann (1999).

Central lateral line neurons also respond readily to the water motions generated by a moving object (Mogdans et al., 1997; Wojtenek et al., 1998). However, the responses are much more variable than those observed among afferent nerve fibers. Most MON units exhibit a single peak of increased discharge rate when the object passes the fish, others exhibit two response peaks separated by reduced spike activity, yet others show multiple response peaks, and in some units ongoing activity is even suppressed when an object passes the fish. Moreover, most units in the MON do not show any obvious responses to the water motions generated in the wake of a moving object. In addition, many responses are independent of the direction of object motion both in terms of discharge pattern and in terms of discharge rate (Mogdans et al., 1997).

In the midbrain TS, the number of units that respond with a different pattern or with a different rate to reversed directions of object motion is greater than in the MON (Wojtenek et al., 1998). While some TS units may respond with a transient discharge to only one motion direction but not the opposite, other units may exhibit complex temporal discharges, the patterns of which depend on the direction of object motion. Thus, these units clearly encode object motion direction but are not tuned to other aspects of a moving stimulus source, like speed or distance. Nonetheless, the finding that distinct response types in the TS are not found in the MON is indicative of neural computation between brainstem and midbrain. The causes and mechanisms underlying these computations are, however, not known.

Plachta et al. (2003) found TS units which responded to the passing of a sphere and to the wake caused by that sphere with increased spike rates, while other TS units responded to the passing sphere but not to the wake behind it. It is possible that units of the first type are processing input from SNs but that units of the second type are processing input from CNs. The same study suggested that the position of the moving sphere is represented systematically in the TS (Figure 9). That is, when an object was moved from anterior to posterior along the side of the fish, units in the anterior torus respond when the object passes anterior locations (e.g., near the head of the fish). As the object continues to move along the fish, units located at increasingly caudal positions in the torus respond to increasingly posterior locations stimulated by the object. Thus, object location is represented in a neural map in the TS. There is no evidence that other aspects of a lateral line stimulus, like speed or distance, are represented in neural maps in the TS or any other area in the fish brain studied so far.

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Figure 9. Responses to a moving sphere of two neurons in the midbrain torus semicircularis of goldfish recorded simultaneously with electrode 2 (E2, red) and electrode 3 (E3, blue). Upper left: Responses to sphere movements in the anterior-to-posterior direction. Upper right: Responses to sphere movements in the posterior-to-anterior direction. The time axis was adjusted to match the distances travelled by the sphere. Lower: The positions of the electrodes relative to the fish midbrain surface. Red, E2; blue, E3. Colored circles below the fish drawing show sphere positions at times labeled 1, 2, 3, and 4 in the upper graphs. Arrows indicate direction of sphere motion.

Redrawn and modified from Plachta et al. (2003).

Vortex rings delivered from a pipette and passing the fish laterally have also been used to study the representation of complex hydrodynamic stimuli by the lateral line (Chagnaud et al., 2006). Similar to moving objects, vortex rings produce defined changes in water velocity and in water flow direction followed by less defined water movements. This is mirrored in the responses of afferent nerve fibers, which respond to vortex rings, just as they do to a moving object, with an initial strong and highly reproducible component consisting of either an increase or a decrease in discharge rate and a second weaker and less reproducible component. Again, these data can be explained by the hair cells’ intrinsic directional sensitivity. The responses of central neurons to vortex rings have thus far not been investigated.

Processing of Bulk Water Flow Information

Fish that are actively swimming or residing stationary in running water are constantly exposed to a bulk water flow across their body. In laboratory experiments in which fish are exposed to bulk water flow in a flow channel, two types of afferent nerve fibers can be distinguished: type I fibers, which respond to the flow with an increase in discharge rate for as long as the water flow was maintained, and type II fibers, which do not change their discharge rate, at least not up to 10 cm s⁻¹ flow velocity (Engelmann, Hanke, & Bleckmann, 2002; Engelmann et al., 2000). The continuous firing of the first type of fibers suggests innervation of SNs, which are continuously stimulated by background flow. In contrast, fibers of the second type most likely innervate CNs, which are unresponsive to background flow due to the filter characteristics of the lateral line canals.

The functional difference between SNs and CNs is revealed if the lateral line is stimulated with sinusoidal water motions in still and running water. In still water, both flow-sensitive and flow-insensitive fibers exhibit sustained and phase-locked responses that can hardly be distinguished from each other. In a 10 cm s⁻¹ background water flow, however, the responses of flow-sensitive fibers to a sinusoidal water motion are masked, whereas the responses of flow-insensitive fibers are hardly affected by the flow (Engelmann, Hanke, & Bleckmann, 2002; Engelmann et al., 2000). This demonstrates that fibers innervating SNs, which are constantly stimulated by the water flow, respond with high accuracy to a vibrating sphere only if the fish is not exposed to a background water flow. In contrast, fibers innervating CNs respond equally well to a vibrating sphere in still and running water.

In running water, many neurons in the lateral line brainstem exhibit firing behaviors that are similar to those of primary afferents. Again, flow-sensitive and flow-insensitive neurons can be found. However, in contrast to primary afferents, some flow-sensitive brainstem neurons may exhibit increased ongoing discharge rates, while others may exhibit suppressed ongoing rates. In some brainstem neurons, discharge rates may increase or decrease transiently when a water flow is turned on but thereafter decrease or increase again while the water flow is continued; that is, these fibers adapt to an ongoing water flow (Kröther et al., 2002). It is likely that flow-sensitive MON neurons receive input predominantly from fibers innervating SNs, and flow-insensitive MON neurons predominantly from fibers innervating CNs.

If stimulated with sinusoidal water motions produced by a vibrating sphere in a water flow, at least three types of MON neurons can be distinguished (Figure 10). Type MI neurons have properties similar to type I afferents; that is, they are flow-sensitive and their responses to a vibrating sphere are masked by running water. In contrast, type MII neurons have properties similar to type II afferents; that is, they are insensitive to water flow and their responses to a vibrating sphere are not masked by running water. Finally, type MII neurons are, like MII neurons, flow-insensitive. Nonetheless, their responses to a vibrating sphere are, just like those of type MI neurons, masked by running water either in terms of spike rate or in terms of phase coupling. These findings support the idea that type input from superficial and canal neuromasts is maintained to a large degree separate in the lateral line brainstem but that interactions between these two subsystems may also occur (Engelmann, Kröther, et al., 2002).

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Figure 10. Responses of type MI, MII, and MIII neurons of in the brainstem medial octavolateral nucleus of goldfish to a 50 Hz vibrating sphere stimulus in still (left) and running 15.5 cm s⁻¹ (right) water. Raster plots of the responses to ten stimulus presentations are shown. For details, see text.

Redrawn and modified from Kröther et al. (2002).

Neurons in the TS may also be either flow-sensitive or flow-insensitive, again pointing to the two input channels arising from the superficial and canal neuromast systems (Bleckmann et al., 2004). Surprisingly, responses to dipole stimuli not only of flow-sensitive but also of nearly all recorded flow-insensitive toral neurons were masked in running water. To account for this finding, neuronal interaction between the two submodalities, such as excitatory input from CNs and additional inhibitory input from SNs, have been proposed (Bleckmann et al., 2004). This argues for a discontinuation of the strict separation between superficial and canal neuromast information at the level of the midbrain.

In fish, the optic tectum is the most prominent midbrain structure, receiving sensory input from the visual, auditory, and lateral line system (e.g., Bartels et al., 1990; Echteler, 1985; Lowe, 1987; McCormick & Hernandez, 1996; Vanegas, 1983). While there is evidence for multimodal integration in the optic tectum from studies in amphibians (Bartels et al., 1990; Bastian, 1982), this has not been systematically investigated in fishes. Using calcium imaging, neuron ensembles responsive to water flow have been found in the optic tectum (Thompson et al., 2016). Exactly how tectal neurons are processing hydrodynamic information and how this information is integrated with other sensory modalities still need to be uncovered.

Determination of Bulk Water Flow Velocity

Flow-sensitive primary afferents respond to a water flow exclusively with an increase in discharge rate (Engelmann et al., 2000; Voigt et al., 2000). This is surprising since lateral line neuromasts contain two populations of hair cells with opposite orientation that are innervated by distinct fibers. Therefore, one would expect about equal numbers of the fibers that respond to water flow with an increase or a decrease in discharge rate, respectively. In contrast to this expectation, Chagnaud et al. (2008) showed that fibers increased discharge rate in response to both anterior–posterior and posterior–anterior water flow; that is, lateral line afferents do not encode the direction of a background flow by spike rate. Characterization of the water motions in the flow using particle image velocimetry (PIV) revealed the presence of flow fluctuations that increased with increasing flow velocity. Maximal spectral amplitudes of the flow fluctuations were similar to those of the firing rates of lateral line afferents, suggesting that the afferents mainly respond to the flow fluctuations but not to the direct current component of the flow.

The fluctuations that are contained in a background flow move downstream across the surface of the fish and will be sensed by consecutively arranged neuromasts. In fact, neurophysiological recordings from pairs of primary lateral line afferents showed that responses of many fiber pairs were highly correlated and that the time shift of the cross-correlation peak decreases with increasing flow velocity for flow velocities between 6.5 and 13 cm s⁻¹ (Chagnaud et al., 2008). This spawned the notion of a cross-correlation mechanism to determine bulk flow velocity by comparing the inputs from an upstream neuromast with those of a downstream neuromast. However, the conduction velocities of primary afferents do not allow time delays that are sufficiently large to compensate for the time delay between the arrivals of a flow fluctuation at successive neuromasts. In any case, if such a mechanism was realized, central lateral line neurons should be found that are tuned to distinct flow velocities. However, such neurons have not yet been found.

Processing of Kármán Vortex Street Information

Any obstacle in a bulk water flow causes the shedding of vortices at the obstacles’ edges. Rheophilic fish (i.e., fish that prefer to live in fast-moving water) use flow regions that are disturbed by such vortices to hold station behind obstacles. If a cylinder or a half-cylinder of defined dimensions is placed in a flow, a Kármán vortex street develops, which consists of a defined staggered array of discrete columnar vortices which detach periodically with alternating sign from either side of the cylinder (Vogel, 1983). It has been shown that fish may use the upstream water motions in a Kármán vortex street to conserve locomotor energy (Bleckmann et al., 2012; Liao, 2007; Liao et al., 2003a; Przybilla et al., 2010). Vortex shedding frequency and wavelength (i.e., the spacing between successive vortices in a Kármán vortex street) can be changed systematically by changing flow velocity and/or cylinder diameter (Liao et al., 2003b). Thus, vortex shedding frequency and wavelength provide information about location, size, and form of the object that caused the vortex street.

Flow-sensitive afferent nerve fibers increase their discharge rates in running water (Chagnaud et al., 2008; Engelmann et al., 2000; Voigt et al., 2000). However, the average discharge rates in running water with and without Kármán vortex street do not necessarily differ. Rather, when exposed to a Kármán vortex street, the temporal pattern of the discharge follows the vortex shedding frequency, in particular if the fish is located at the edge of the vortex street (Chagnaud et al., 2007). The precise pattern of firing changes in a predictable way if cylinder position, cylinder size, or flow velocity is altered, thus providing to the brain the information that is needed to estimate location and 3D structure of the cylinder.

Like afferent nerve fibers, MON units may discharge bursts of spikes in a Kármán vortex street (Klein et al., 2015; Winkelnkemper et al., 2018), and the burst pattern matches the vortex shedding frequency (Figure 11). The representation of the vortex shedding frequency may be even better in the MON than in primary lateral line afferents (Bleckmann et al., 2012); that is, central mechanisms lead to an improved signal-to-noise ratio. It is, however, not known how this is achieved.

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Figure 11. Representation of the vortex shedding frequency by central lateral line neurons. A: Schematic drawing of the vortices generated behind a half-cylinder in a water flow, passing the fish from anterior to posterior. Note the alternating sense of rotation of successive vortices. B: Original recording of a medial octavolateral nucleus neuron in the nase (Chondrostoma nasus) with the fish exposed to a Kármán vortex street generated behind a half-cylinder in a water flow. C: Probability density function of the spikes in the spike train shown in B. D: Cross-correlation function of the function shown in C. Note that periodic peaks occur at temporal separations corresponding to the vortex shedding frequency (arrowheads).

Adapted from Winkelnkemper et al. (2018).

Furthermore, in a Kármán vortex street, the cross-correlation of the information provided by two consecutively arranged neuromasts about sequentially received vortices could be used to determine bulk flow velocity (Chagnaud et al., 2008). Cross-correlation of temporally distant events like these requires the use of neural delay lines, similar to those found in the auditory system of birds (Carr, 1993; Carr & Konishi, 1988). If delay lines were implemented in the fish lateral line system, flow-sensitive central units should be found, which respond exclusively to a head-to-tail flow, are tuned to a particular bulk flow velocity, and are insensitive to flow fluctuations. Some MON and TS units may indeed be highly sensitive to the direction of water flow. However, neurons tuned to a distinct flow velocity have not yet been found. Thus, at present, there is no physiological evidence for a cross-correlation mechanism in the lateral line system, and the question of how the lateral line system computes flow velocity remains unsolved.

Contribution of Efferent Connections to Sensory Processing

The hair cells of the lateral line neuromasts are innervated by efferent nerve fibers (Hama, 1978), which can be distinguished morphologically from afferent nerve fibers by their smaller axon diameters (Münz, 1985; Northcutt, 1992). A single efferent fiber may innervate all hair cells within a neuromast independent of their polarity (Faucherre et al., 2009). In addition, an efferent fiber may innervate more than one neuromast (Köppl, 2011), hair cells in both SNs and CNs (Münz, 1985), on different sides of the body (Roberts & Meredith, 1989), and even in other modalities (e.g., the vestibular and auditory systems) (Münz, 1985).

Efferent fiber activity typically reduces both spontaneous and evoked discharges of afferent nerve fibers (Lunsford et al., 2019; Russell, 1971; Tricas & Highstein, 1991). There are, however, some studies in which efferent fiber activity caused increased discharge rates of vestibular (Highstein & Baker, 1985) and lateral line afferents (Flock & Russell, 1973). The efferent system can be activated by stimulation of the visual (Tricas & Highstein, 1990) or somatosensory (Roberts & Russell, 1972) system or by motor acts like vocalizations or gill and/or swimming movements, which may cause self-stimulation of the lateral line receptors (Ayali et al., 2009; Palmer et al., 2005; Roberts & Russell, 1972; Russell & Roberts, 1974; Tricas & Highstein, 1991; Weeg et al., 2005). In summary, the present evidence suggests that the efferent system modulates the “gain” of the sensory hair cells to prevent them being overstimulated and exhausted by the water disturbances resulting from powerful body movements or to suppress or enhance afferent nerve fiber sensitivity depending on behavioral context.

In addition to the efferent innervation originating in the octavolateral efferent nucleus, the peripheral lateral line system also receives efferent input from the diencephalon in zebrafish (Bricaud et al., 2001; Metcalfe et al., 1985), goldfish (Puzdrowski, 1989; Zottoli & Horne, 1983), and catfish (New & Singh, 1994). The function of these inputs is not known.