j.1749-4877.2008.00131.x

Integrative Zoology 2009; 4: 13-25 doi: 10.1111/j.1749-4877.2008.00131.x

REVIEW

Lateral line system of fish Horst BLECKMANN1 and Randy ZELICK2 1Institute of Zoology, University of Bonn, Bonn, Germany and 2Department of Biology, Portland State University, Portland, USA

Abstract The lateral line is a sensory system that allows fishes to detect weak water motions and pressure gradients. The smallest functional unit of the lateral line is the neuromast, a sensory structure that consists of a hair cell epithelium and a cupula that connects the ciliary bundles of the hair cells with the water surrounding the fish. The lateral line of most fishes consists of hundreds of superficial neuromasts spread over the head, trunk and tail fin. In addition, many fish have neuromasts embedded in lateral line canals that open to the environment through a series of pores. The present paper reviews some more recent aspects of the morphology, behavioral relevance and physiology of the fish lateral line. In addition, it reports some new findings with regard to the coding of bulk water flow.

Key words: central integration, hydrodynamic reception, lateral line, object localization, teleost fish.

INTRODUCTION Aquatic animals that move inevitably cause water

displacements and pressure fluctuations; that is, hydro-dynamic stimuli (Kalmijn 1988a). Consequently, hy-drodynamic stimuli provide important survival informa-tion and this is, no doubt, the reason most aquatic animals have developed a highly refined sensory system for the detection of water movements, pressure fluctuations or both (Bleckmann 1994). In fishes and aquatic amphibians this system is the mechanosensory lateral line.

This article will briefly review the morphology and behavioral relevance of the fish lateral line and then focus in greater detail on some aspects of the peripheral and central processing of lateral line information. We address in particular the question of how the lateral line system encodes space, and whether and how this system deter-mines gross flow direction and gross flow velocity. For

further general information on the fish lateral line, the reader is referred to Coombs and Janssen (1989), Bleckmann (1993, 1994, 2004, 2007), Montgomery et al. (2000), Janssen (2004) and Mogdans et al. (2004).

Morphology of the lateral line periphery The smallest functional unit of the fish lateral line is

the neuromast, a sensory structure that occurs free-standing on the skin (superficial neuromast [SN]) or in fluid-filled canals (canal neuromast [CN]) that usually open to the environment through a series of pores (Fig. 1). In different fish species the organization of the peripheral lateral line can be quite different. For instance, some fish have fewer than 50 SNs on each body side, whereas other fish species have up to several thousand SNs distributed over the head, trunk and tail fin (e.g. Puzdrowski 1989; Schmitz et al. 2008). SNs not only occur in the skin but also in pits or on pedestals raised above the skin (Coombs et al. 1988; Webb 1989a). The lateral line canal system can also vary significantly in different fish species, even if the species are closely related. The variability includes the number, placement and branching pattern of the ca-nals, canal compartmentalization and width, as well as

Correspondence: Horst Bleckmann, Poppelsdorfer Schloss,

Institute of Zoology, University of Bonn, 53115 Bonn, Germany.

Email: [email protected]

2009 ISZS, Blackwell Publishing and IOZ/CAS 13

H. Bleckmann and R. Zelick

Figure 1 The lateral line periphery. The drawing shows the pores of the lateral line canals (circles) and the spatial distribution of super-ficial neuromast (dots) in the bitterling, Rhodeus sericeus amarus (Cyprinidae). In most fish species, one canal runs above the eye (su-praorbital), one below the eye (infraorbital) and one on the lower jaw (mandibular). Note that in Rhodeus the trunk canal does not run the full length of the body. We are gratefully to A. Schmitz for providing the drawing.

the number, size and placement of canal pores (Coombs et al. 1988; Webb 1989b). There is a strong link between the morphology of the peripheral lateral line and its mechanical filter properties (Denton & Gray 1988, 1989; van Netten & Khanna 1993; van Netten & Wiersin-ga-Post 2002; van Netten 2006). We have, unfortunately, only begun to understand the full functional implications of these morphological variations. One reason for this probably is that, with one exception (Pillapakkam et al. 2007), only vibrating sphere stimuli have been used to uncover possible form-function relationships. And, even worse, these stimuli were only applied in a still-water environment. We believe that attempts to uncover form- function relationships will be more successful if we si-mulate natural stimulus and noise conditions in the la-boratory environment.

The principle sensory cell of the lateral line system is the hair cell. Like all hair cells, those of the lateral line have a hair bundle at their apical surface comprised of up to 150 stereovilli that grow longer from one edge of the bundle to the other. A single true kinocilium always oc-curs eccentrically at the tall edge of the hair bundle. Within the sensory epithelium of a neuromast the hair cells are oriented into two opposing directions that define the most sensitive axis of a neuromast (Flock & Wersll 1962). The hair bundles protrude into a cupula that con-

nects the bundles with the water surrounding the fish, or with canal fluid. Lateral line neuromasts are innervated by afferent and efferent nerve fibers. Morphological and physiological studies indicate that a single afferent fiber most likely innervates only hair cells with the same ori-entation (Mnz 1985; Bleckmann 1994). Besides the various morphological structures that influence the fluid flow over the cupula (see above), the hair cell coupling to the cupula, the mechano-electrical properties of the hair cells, the innervation pattern of groups of neuromasts (what is the size of the functional units?) and the spike generating mechanisms in the nerve fibers that innervate the hair cells also determine the response properties of the peripheral lateral line (Montgomery et al. 1994). All this must be taken into account if we want to uncover lateral line adaptations. In any case, the organization of the peripheral lateral line already suggests four parallel pathways that carry lateral line information to the brain: one from SNs, one from CNs, one from hair cells polar-ized in a particular orientation within the cupula, and one from the hair cells of the opposing-orientation.

Hydrodynamic stimuli

A common source of natural water disturbances and pressure fluctuations (i.e. of hydrodynamic stimuli) is the


Fish lateral line

locomotory movements of aquatic animals. For instance, subundulatory swimming fish generate a wake with a complex vortex structure (Fig. 2). Even if the fish are small, the vortices in fish-generated wakes last for more than 30 s, and water velocities significantly higher than background water motions can still be detected after 3 min (Hanke et al. 2000). Fish wakes provide some in-formation about the size, swimming style and swimming speed of the wake generator (Hanke & Bleckmann 2004) and, therefore, probably also about the type of fish that produced the wake. Aquatic animals may produce water motions during agonistic encounters (Satou et al. 1991; Herberholz & Schmitz 2001) and for intraspecific communication (Kaus & Schwartz 1986; Satou et al. 1991). In terms of displacement, the main spectral am-plitudes of fish-generated hydrodynamic stimuli are

usually below 10 Hz (Enger et al. 1989; Bleckmann et al. 1991a), but higher-frequencies might also occur (Bleckmann et al. 1991a). Blind cavefish use self-generated hydrodynamic stimuli for lateral line perception. They can do so because self-produced water motions (or pressure fluctuations) are altered in a pre-dictable way as they approach or pass a nearby object (Campenhausen et al. 1981; Weissert & Campenhausen 1981; Hassan 1992). The struggling movements of in-sects that have fallen into the water produce capillary surface waves (Lang 1980; Bleckmann 1985). Some fish use these waves for prey detection and prey localization (see also below). The spectra of insect generated capil-lary surface waves are narrow-band, with an upper fre-quency limit of approximately 60 to 100 Hz (Lang 1980; Bleckmann 1985).

Hydrodynamic noise A nonmoving limnophilic fish probably faces little

hydrodynamic noise. If it moves, however, its lateral line is exposed to self-generated noise that may contain im-portant information. In addition to self-generated noise, rheophilic fish, and also fish that live along the shoreline of the ocean, are constantly exposed to large background water motions and pressure fluctuations. Whether and how all this hydrodynamic noise interferes with lateral line perception is not known (Bleckmann 1994; Kanter & Coombs 2003).

Figure 2 (AC) The color coded spatial extent of fish wakes. To resolve the low velocities in the aged trails, the velocity scale does not cover the complete range of measured values. Wakes were generated by (A) Lepomis gibbosus, (B) Colomesus psit-tacus and (C) Thysochromis ansorgii (after Hanke and Bleck-mann 2004).

Lateral line and fish behavior Hydrodynamic information provides the basis for

many behavioral decisions (Bleckmann 1993). There is evidence that fish use hydrodynamic information for prey detection, predator avoidance, intraspecific communica-tion, schooling, object discrimination, entrainment and rheotaxis (Bleckmann 1993, 1994; Montgomery et al. 1997). Surface-feeding fish detect the surface waves caused by struggling insects that have fallen into the water with their cephalic lateral line (Schwartz 1970; Hoin-Radkovski et al. 1984). In addition, these fish can determine the direction and the distance to a surface wave source. For the latter they use the damping and dispersion properties of water surface waves, thus exploiting the physical properties of the water surface for prey local-ization (for a review see Bleckmann et al. 1989). Sur-face-feeding fish can identify wave sources. They dis-criminate not only the frequency but also the amplitude of a constant frequency wave stimulus (Bleckmann et al. 1981; Waldner 1981; Vogel & Bleckmann 1997). In ad-



dition, they discriminate pure sine waves from those water motions that show abrupt frequency changes (Vo-gel & Bleckmann 1997).

Midwater fish use lateral line input for the detection of moving objects. They not only discriminate the direction of object motion but also object speed, size and shape (Vogel & Bleckmann 2000). The discrimination of a pattern of objects is accomplished by blind cavefish (Astyanax mexicanus, formerly Anoptichthys jordani; Romero & Paulson, 2001), which can pass through a barrier of rods without touching them using only hy-drodynamic information (Teyke 1989). In addition, blind cavefish use lateral line information to build spatial maps (Burt de Perera 2004).

In all fish in which parts of the lateral line are not di-rectly coupled to air-filled structures (see e.g. Bleckmann et al. 1991b) the lateral line in principal is only a close range detection system. This implies that the lateral line has limited utility for the detection of distant fish, but this in many cases would be a misjudgment. Hydrodynamic stimuli produced by moving animals often have high persistence (see above). Therefore, in principal, the lat-eral line can be effective in detecting stimuli produced by another animal that is now quite far away due to recovery of the still-present residual stimuli from that animal. Some catfish can sense and track the hydrodynamic trails of prey fish (Pohlmann et al. 2001, 2004). Most likely, they can do so even if the wake generator has already covered a distance of several body lengths. More be-havioral studies are clearly needed to uncover the wake tracking capabilities of predatory fish. We do not know, for instance, whether wake tracking is rare or quite common in piscivorous nocturnal and/or schooling fish and whether and with what precision these fish can de-termine the age, wavelength, frequency composition and extension of fish-borne or artificial wakes. Another im-portant question is that of whether and how fast fish can determine the direction in which the wake generator has moved.

Most behavioral experiments designed to uncover lateral line function have been done with fish placed in still water; that is, under conditions that are quite un-natural. In nature, either the water moves, the fish moves, or both move. Although hydrodynamic noise might in-terfere with lateral line perception (Kanter & Coombs 2003; Nauroth & Mogdans, unpublished manuscript), rheophilic fish actually use lateral line information to maintain their position in fast running water and to cap-ture energy from vortices while moving through turbu-lent flow (Sutterlin & Waddy 1975; Liao et al. 2003; Beal

et al. 2006; Liao 2006). Therefore, some fish use hy-drodynamic noise as a source of information.

Peripheral physiology

Hair cells are displacement detectors. A sinusoidal displacement of the ciliary bundle either causes a depo-larization (displacement towards the kinocilium) or a hyperpolarization (displacement away from the kinocil-ium) of the hair cell (Kroese & van Netten 1989). In general the response amplitude of a lateral line hair cell depends on both the angle and the amplitude of ciliary bundle deflection. Therefore, the membrane potential of a single hair cell provides neither unequivocal informa-tion about stimulus direction nor about stimulus ampli-tude (Flock 1965).

Under natural conditions, external water motions (SNs) and canal fluid motions (CNs) displace the cupulae of lateral line neuromasts and, therefore, cause a deflection of the ciliary bundles of the underlying hair cells. The relationship between cupula displacement and the am-plitude of the receptor potential of a neuromast is linear but eventually reaches saturation. Even with no stimulus applied, lateral line afferents show ongoing activity (e.g. Bleckmann & Topp 1981; Mnz 1985; Grner & Mohr 1989; Mohr & Bleckmann 1998).

Since the work of Harris and Bergeijk (1962), most researchers have stimulated the lateral line with small vibrating spheres as this provides a stimulus that can be most readily described from a hydrodynamic perspective. At threshold (approximately 0.02 m), lateral line af-ferents start to phase lock to sinusoidal water motions (e.g. Bleckmann & Topp 1981; Kroese & Schellart 1992). Approximately 20 dB above the threshold phase, locking may reach a plateau. Due to the innervation pattern (see above) and the directional sensitivity of lateral line hair cells, primary lateral line afferents phase lock only to half of a full cycle of a sinusoidal wave stimulus. On average, discharge rates of primary lateral line afferents start to increase at stimulus levels approximately 10 dB higher than those that cause phase locking. The dynamic am-plitude range of lateral line afferents varies between 11 and 90 dB (Elepfandt & Wiedemer 1987; Mogdans & Bleckmann 1999). If stimulated with sinusoidal water motions, afferents that innervate SNs respond up to a frequency of approximately 70 Hz in proportion to water velocity. The flow in lateral line canals depends on the pressure differences between neighboring canal pores. As a consequence, afferents that innervate CNs respond (sinusoidal water motions parallel to the long axis of the lateral line canal) approximately in proportion to outside


Fish lateral line

water acceleration (Kalmijn 1988b). However, if a ho-mogeneous water jet is oriented perpendicular to the surface of a fish and hits a single canal pore, the CNs situated closest to that pore do respond to that water jet (i.e. to a DC stimulus) (Fest & Hofmann, unpublished manuscript). In general, the responses of lateral line afferents are wideband and similar across species (Bleckmann & Mnz 1990; Montgomery et al. 1994). In some fish, however, lateral line afferents may be low-pass, band-pass, broadly tuned or even complex (Weeg & Bass 2002).

Encoding of source location

If a dipole source (a vibrating sphere) is moved slowly along the side of a fish, the surface of the fish will ex-perience a pressure gradient pattern that reflects the po-sition of the sphere (Sand 1981) and the direction of sphere vibration. Calculations show that the information about source azimuth is contained in the location of the maximum pressure-difference amplitude, whereas in-formation about source distance is contained in the points of phase reversal (Coombs et al. 2000; Curcic-Blake & van Netten 2006; Goulet et al. 2008). This distance cue is robust and unambiguous, even under noise conditions (Goulet at al. 2008). As already mentioned, CNs are pressure gradient detectors. Consequently, the excitation pattern of arrays of CNs already contains the information about sphere position (Coombs et al. 2000) and sphere vibration direction (Scholze, unpublished manuscript).

Responses to non-vibrating moving objects Animate sources of natural hydrodynamic stimuli are

usually not stationary and rarely vibrate with constant amplitude and frequency. A fish, for example, moves around at various speeds while searching for mates, prey or shelter. To create more complex or irregular lateral line stimuli, researchers have stimulated fishes with small moving objects (Bleckmann & Zelick 1993; Mller et al. 1996; Mogdans & Bleckmann 1998; Engelmann & Bleckmann 2004). Moving objects cause low frequency transient water motions that are followed by ill-defined long-lasting water oscillations (Mogdans & Bleckmann 1998). Water motions are always associated with changes in hydrodynamic pressure and vice versa. Although the wake caused by a moving object may contain several high velocity peaks, the pressure changes caused by that object are prominent only during the initial transient and are small in the objects wake (Mogdans & Bleckmann 1998).

Lateral line afferents respond to a moving object with a single peak of excitation followed by a decrease in neural activity or vice versa. The response pattern inverses when object motion direction is reversed. Type I afferents (i.e. afferents that most likely innervate SNs), in addition, discharge numerous unpredictable bursts of spikes after the initial, well defined response; that is, after the object has passed the fish (Mogdans & Bleckmann 1998). In contrast, the neural activity of afferents that most likely innervate CNs (type II afferents) is barely affected by the wake of a moving object (Mogdans & Bleckmann 1998).

Running water To fully comprehend lateral line perception, one must

investigate how hydrodynamic noise affects the lateral line system of fishes. Bulk water flow probably always consists of both a DC component and flow fluctuations that are superimposed on the DC flow. If exposed to unidirectional bulk water flow, nearly all flow sensitive lateral line afferents (most likely afferents that innervate SNs) respond with a burst-like increase in ongoing ac-tivity (Voigt et al. 2000; Carton & Montgomery 2002; Engelmann et al. 2002b; Chagnaud et al. 2007a). Fur-thermore, in nearly all flow-sensitive lateral line afferents of goldfish, this increase occurs irrespective of gross flow direction (Chagnaud et al. 2007a). This suggests that flow sensitive lateral line afferents of goldfish do not respond to the DC component of bulk water flow but only to the flow fluctuations (Chagnaud et al. 2007a). Any flow fluctuation will be convected with the mean flow. Therefore, fish could determine gross flow direction and gross flow velocity by monitoring the direction and ve-locity of individual flow disturbances while they move across the body surface. Spike trains recorded simulta-neously from pairs of flow sensitive lateral line afferents are often correlated and the correlation peak depends on both flow velocity and flow direction (Fig. 3) (Chagnaud et al. 2008). This suggests that fish might use a cross-correlation mechanism to determine both meas-ures.

Response masking In running water (10 cm/s) the responses of flow sensi-tive primary lateral line afferents to the water motions caused by a stationary vibrating sphere are masked, both in terms of discharge rate and in terms of phase locking. In contrast, vibrating sphere-evoked responses of flow insensitive lateral line afferents are barely affected by running water (Engelmann et al. 2000, 2002a), provided



Figure 3 (AD) Cross-correlation functions of the firing frequencies of pairs of lateral line afferents recorded simultaneously. (A) From bottom to top gross flow velocities were 0, 4, 6.5, 8, 10, 12 and 13.5 cm s1. Gross flow direction was from rostral to caudal. (BD) Cross-correlation functions of three simultaneously recorded spike train pairs. Gross flow was from rostral to caudal (left) and fromcaudal to rostral (right). Note that the data show both correlation (B and C) and anti-correlation (D). Flow velocities were 0, 6.5, 10 and 13.5 cm s1. Note that there is no correlation in still water and that higher flow velocities systematically shift the time of maximalcorrelation (indicated by the vertical lines in (BD)) (after Chagnaud et al. 2008).

the flow velocity is not too high (Chagnaud et al. 2007b). It remains to be tested whether natural hydrodynamic stimuli of interest to a fish are similarly resistant to masking by bulk water flow.

Central physiology The central lateral line pathway of teleost fishes has

been described several times (e.g. McCormick 1981; McCormick & Hernandez 1996; Wullimann 1998) and, therefore, will not be repeated here. For this review it is sufficient to know that lateral line information is proc-essed in the medial octavolateralis nucleus of the medulla and in the torus semicircularis and optic tectum of the midbrain.

Encoding of object position In contrast to primary lateral line afferents, central

lateral line units can be highly selective. For instance, approximately 40% of all medullary and midbrain lateral line units do not respond to a stationary sphere that vi-brates with constant frequency and amplitude (Plachta et al. 1999; Bleckmann 2007). These units might, however, readily respond to amplitude modulated sinusoidal water motions (Plachta et al. 1999; Ali, unpublished manu-script) or to the water motions caused by a small object that passes the fish laterally (e.g. Mller et al. 1996; Wojtenek et al. 1998). Other central lateral line units respond only to a stationary vibrating sphere but not to a moving object (Engelmann & Bleckmann 2004). One of the stimulus parameters that is clearly encoded in the central lateral line pathway is the direction of object


Fish lateral line

motion; that is, some central lateral line units require a certain temporal and spatial pattern of water motions (Mller et al. 1996; Wojtenek et al. 1998; Plachta et al. 2003). As already mentioned, in still water the excitation patterns of primary lateral line afferents unequivocally reflect both the position (Sand 1981; Coombs et al. 2000) and the vibration direction of a dipole source (Scholze et al., unpublished manuscript). Therefore, the central lat-eral line neurons obtain the information necessary to determine both the position and the vibration direction of an object. However, although the responses of central lateral line neurons might dramatically change with ob-ject position and/or object vibration direction (Fig. 4), up to now no highly space and/or vibration direction selec-tive medullary or midbrain lateral line neurons have been found (Fest, unpublished manuscript; Meyer, unpub-lished manuscript).

Figure 4 Responses of a toral lateral line unit of goldfish, Ca-rassius auratus, to sinusoidal water motions. Left: period histo-grams (bin width 0.5 ms) of the neural activity induced by a stationary vibrating sphere (diameter 10 mm, vibration frequency 50 Hz, peak-to-peak displacement amplitude 220 m, distance from fish 10 mm). Sphere vibration direction was 0 (parallel to the long axis of the fish), 45, 90 (perpendicular to the long axis and the surface of the fish) and 135. Note that the unit responded only to half of a full wave cycle and that the phase angle of the response depended on the direction of sphere vibration. Right: scheme of a fish and schematic representation of iso-pressure contours (dashed lines) and flow lines (solid lines with arrows) about a dipole source. Iso-pressure contours are depicted for a single plane that bisects the source along its axis of oscillation, indicated by the large arrowheads. Note that for a given phase of sphere movement the flow direction on the surface of the fish depends on the direction of sphere vibration. A change in the direction of sphere vibration from 90 to 135 led to a reversal of flow direction and, consequently, to a 180 phase shift of the response (Meyer & Bleckmann, unpublished manuscript).

Figure 5 Response of a lateral line unit from the goldfish torus semicircularis to bulk water flow. Superimposed on the spike trace is the signal from a hot-wire anemometer showing flow character-istics. Note that the flow is initially smooth (S), but then becomes more turbulent, with many high-frequency fluctuations (F). The neural response follows the fluctuation component of the bulk water flow (Zelick et al., unpublished manuscript).

Central encoding of flow velocity and flow di-

rection Consider the computational challenge presented to the

fishs central nervous system in a natural scenario. The



fish would like to know if there is an object moving in the water relative to it, and would like to know the objects distance, direction, and direction of movement. This information should be resolved against a background of bulk water flow relative to the fish, a flow that may be asymmetrical around the fish, and for which the direction and speed should be determinable. Finally, the fish might be creating, through its own locomotion, a complex tur-bulent flow pattern. Not surprisingly, then, midbrain neurons activated by water movement show a great di-versity of responses. Our understanding of the computa-tional rules by which these neural responses provide a full hydrodynamic picture of the fishs environment is in its infancy. Ongoing experiments in the authors laboratory have begun to yield a catalog of response types. For example, a subset of lateral line units in the torus semicircularis of goldfish respond best to bulk water flow, and much less well to other types of hydro-dynamic stimuli, such as the water motions caused by a

stationary vibrating sphere (Zelick et al., unpublished manuscript). Many lateral line units that respond to bulk water flow are multimodal; that is, they are also driven by vibrational stimuli or changes in light level, or they give a purely phasic response at flow onset. The latter suggests that these units receive input from pressure sensitive inner ear receptors. Interestingly, neurons that respond to bulk water flow might be more sensitive to the high frequency fluctuations in the flow than to the low fre-quency or the DC component of the water movement (Fig. 5). This is sensible if the fish is measuring turbulence created, for example, by objects in the water stream. Some midbrain lateral line units are selective with re-spect to bulk flow direction (head-to-tail or tail-to-head; Fig. 6), although to date no neurons have been found that are perfectly selective, insofar as they show no response at all to flow in the non-preferred direction. Such per-fect neurons would be constructed, hypothetically, from delay lines that permit time correlation of turbulent sig-nals traveling along the fish. Some weakly tuned me-dullary (Krther et al. 2002) and midbrain lateral line units (Zelick et al., unpublished manuscript) have also been found, but, to date, sharply velocity-tuned lateral line units have not been recorded from the medulla or midbrain of fish.

CONCLUSIONS

Over the past 20 years researchers have learnt a great deal about the functional significance of the fish lateral line. Nevertheless, we still have only a vague idea about the temporal and spatial features of natural noise and natural lateral line stimuli. As long as we lack this knowledge it will be hard to uncover any formfunction relationship between the various types of lateral lines found in the various types of fish species. To uncover possible form-function relationships we also need to know much more about the alignment and spacing of superficial and canal neuromasts (Schmitz et al. 2008) and also about their innervation patterns. Equally im-portant is the study of the mechanical filter properties of the various types of lateral line canals using natural lat-eral line stimuli and natural noise (Pillapakkam et al. 2007). We also need to know more about the behaviors that depend on lateral line information. Several studies indicate that fish do not need lateral line input for the control of locomotion (Kesel et al. 1989; Liao et al. 2003); however, in highly turbulent waters fish use lateral line information to reduce their costs of locomotion (Sutterlin & Waddy 1975; Liao 2006; Przybilla & Bleckmann, unpublished manuscript). In any case, it

Figure 6 A flow directional-sensitive lateral line unit from thegoldfish torus semicircularis. (A) represents the control voltage to the motor driving a propeller causing bulk water flow. (B) and (C) show the neural responses and superimposed hot-wire anemometer traces for head-to-tail and tail-to-head water flow, respectively. Theneural response is plotted as average spike frequency per 3 s. Thescale bars indicate that the anemometer signals and, hence, waterflow profiles, are similar for both flow directions. Although selec-tive for flow direction, the neuron is not perfectly so (Zelick et al., unpublished manuscript).


Fish lateral line

might be worthwhile considering what kind of water motions the lateral line of a fish, swimming in still or highly turbulent water, experiences and how the system responds to these water motions. It might well be that in certain circumstances SNs warn a fish that its laminar boundary layer is close to turning into a turbulent boun-dary layer; that is, that if the fish does not act rapidly it might experience a large increase in swimming resistance. Although several theoretical models suggest certain computational rules being implemented in the central lateral line pathway (Curcic-Blake & van Netten 2006; Goulet et al. 2008), we need to learn more about the central mechanisms fish actually use to encode object space and to identify and discriminate stimulus sources. Furthermore, we have barely touched upon the question of whether and how the lateral line can perform a real scene analysis. The multimodal integration of lateral line information also requires more research, and we scarcely have any information about the centrifugal control of lateral line input.

ACKNOWLEDGMENTS

We thank J. Mogdans for his helpful comments on an earlier draft of this paper. G. Meyer and V. Hofmann deserve thanks for collecting the midbrain data shown in Figs 46. The original research of H.B. was generously supported by the DFG, the BMBF, DARPA, BfG, DAAD and by the European Commission, Future and Emerging Technologies.

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