Response properties of electrosensory afferent fibers and secondary brain stem neurons in the paddlefish
1 University of Bonn, Institute of Zoology, Poppelsdorfer Schloss, 53115
Bonn, Germany
2 Center for Neurodynamics, Department of Biology, University of Missouri-St
Louis, MO 63121, USA
* Author for correspondence (e-mail: mhofmann{at}uni-bonn.de)
Accepted 22 September 2005
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Summary |
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Key words: passive electroreception, paddlefish, Polyodon spathula, dorsal octavolateral nucleus, topography
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Introduction |
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In `proximity mode', electroreceptors probably work like the more familiar
somatosensory system in that they `feel' the presence of an object by its
electric field, and the location of the object is determined by which
receptors are stimulated. However, electroreception is different from the
somatosensory system in that it can also detect objects a considerable
distance away (e.g. Wilkens et al.,
2001). In this `distance mode', a single source stimulates a large
number of receptors simultaneously and the exact source location has to be
computed centrally. This is comparable to an array of photoreceptors, but
without an image-forming lens.
At present, it is unknown how the brain can compute electrosensory information in this `distance mode'. To begin to address this question, we investigated signal processing in the first relay center of the paddlefish, the dorsal octavolateral nucleus (DON), by comparing the response properties of the second order neurons in the DON with those of the primary afferent fibers, which carry the information from peripheral receptors to the brain. Differences in the following parameters were tested: receptive field shape (lateral inhibition, contrast enhancement), movement detection, topography within the DON, sensitivity and frequency tuning.
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Materials and methods |
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Stimulation
Electrical stimuli were either large quasi-uniform fields or local dipole
sources, which were moved along the rostro-caudal axis parallel to the rostrum
of the fish. Quasi-uniform electric fields were produced by two silver wires,
one 10 cm in front of the animal and one 5 cm behind it. The wires were
connected to a constant current source (A 395 linear stimulus isolator, WPI,
Sarasota, FL, USA) driven by a sound card of a PC. The sound card was modified
to allow stimulus waveforms down to true DC. Custom-made software drove the
sound card with 16-bit resolution and 10 kHz sampling rate. Uniform fields
were used as search stimuli (25 µV cm-1, modulated at 5 Hz).
The moving dipole fields were produced by three silver wires placed 4 mm apart. Two of them were arranged parallel to the rostro-caudal axis of the rostrum, and used for dipolar stimulation. Here the term dipolar refers to the fact that, in this configuration, a given receptor was first under the influence of the leading electrode and, when the center of the dipole has passed over the receptor, the response was dominated by the trailing electrode. In a monopolar configuration, a third electrode positioned 4 mm laterally to the first electrode was used as the second pole. In this configuration, where the electrodes were oriented perpendicular to the movement direction, the first electrode was always proximal to the fish.
The electrodes were translated by a linear stepping motor (LinMot, Sulzer Electronics, Zürich, Switzerland) parallel to the rostro-caudal axis at a distance 2 cm from the edge of the rostrum. In order to scan the receptive field, we delivered a continuous 2 Hz sinusoidal stimulus while moving the electrodes slowly (0.5 cm s-1) from rostral to caudal and, after a brief pause, back to rostral. To test for movement detection, we applied a DC field and moved it at a speed of 5 cm s-1, approximating the normal swimming speed of the paddlefish. Further processing of the data is described below.
Calibration
Calibration of the stimuli was done by measuring the electric field in the
experimental setup with two silver wires placed 2 cm apart parallel to the
electric field. The signal was amplified with a differential DC amplifier and
viewed on an oscilloscope. Due to possible polarization effects, noise and DC
offsets, only large amplitude fields in the range of 1000-5000 µV
cm-1 could be picked up. Even with these large amplitudes, DC
fields were stable over long periods and sine waves and other wave forms
showed no distortion due to polarization of the stimulation electrodes.
To test the linearity of the electric fields down to the low amplitudes used during the recordings (i.e. <50 µV cm-1), one of the calibration electrodes was vibrated parallel to the electric field with an amplitude of 4 cm at 5 or 10 Hz. The modulated signal was amplified, digitized, and band-pass filtered at the modulation frequency and the peak-to-peak amplitude determined. Since only the modulation due to the vibration was measured, any DC offset was eliminated. With this method, we could assure the linearity of the stimulus down to electric fields of <10 µV cm-1.
The stimulus intensities used in our experiments were tested for their behavioral relevance in freely moving paddlefish. Local DC dipole fields with an intensity up to ten times stronger than the one used in the electrophysiological recordings (<50 µV cm-1) elicited prey catching behavior. Only much stronger or larger dimension electric fields resulted in avoidance behavior (>100 µV and dipole size >5 cm). Thus, the stimuli used in our electrophysiological studies were within an intensity range that elicited natural behaviors in the paddlefish.
Recording
Single unit activity was recorded in the hind brain dorsal octavolateral
nucleus (DON) with tungsten electrodes (5-20 M) and from primary
afferent fibers in the lateral line ganglia with glass electrodes (>30
M
, filled with 3% lithium chloride). With tungsten electrodes we were
able to record single units in the DON, but not in the lateral line nerve or
ganglion. The signals were amplified by 1000 (AM Systems, model 1700,
Carlsborg, WA, USA), filtered (notch, 300 Hz low pass, 5 kHz high pass),
displayed on an oscilloscope (Tektronix, 2216, Richardson, TX, USA) and
monitored on a loudspeaker. The amplified signals were fed through a window
discriminator (121 Window Discriminator, WPI, Sarasote, FL, USA) and the TTL
pulses were recorded on a computer with a commercial sound card at a sampling
rate of 10 kHz. To ensure synchrony of the recordings with the stimulation, a
trigger pulse was generated by the computer at the start of the stimulation.
This pulse was recorded simultaneously with the spike data.
Data analysis
The data were further analyzed using IGOR 4 software (Wavemetrics, Lake
Oswego, OR, USA). For every stimulus paradigm, the instantaneous firing rate
was calculated. For stimulus durations of 1 s or less, the stimuli were
repeated ten times and the firing rate averaged. Longer recordings were
repeated five times, although very slow sine waves lasting more than 10 s were
recorded only once. The spontaneous firing rates were recorded for 1 min.
Beside calculating the mean firing rate, the temporal structure of
interspike intervals was analyzed by autocorrelation. Primary afferent fibers
show a characteristic spike pattern that can be detected by autocorrelation
analysis (Bahar et al., 2001).
The mean interspike interval was subtracted from each interval
(
ti=ti-t) and the
autocorrelation C(n) of the
ti calculated
as:
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Data obtained during slow scanning of the receptive fields with the linear stepping motor and a 2 Hz AC field were converted into an instantaneous frequency plot (Fig. 1A),normalized, filtered around the stimulation frequency, and multiplied by the 2 Hz stimulus (Fig. 1B). This multiplication results in a signal whose amplitude reflects the response magnitude and whose polarity represents the phase. This kind of computation is referred to as the phase plot. Fig. 1 shows an example of a 2 Hz stimulation with local electrodes oriented perpendicular to the rostrum, and moved at a speed of 0.5 cm s-1 along the rostro-caudal axis of the rostrum. Fig. 1A shows the firing rate of a DON unit and Fig. 1B the corresponding phase plot. Whereas the original data (Fig. 1A) reveal only the response magnitude, Fig. 1B shows the phase relationship between the stimulus and the response in addition. The multiplication with the stimulus results in a signal with twice the frequency. The maximum response is reached when the electrodes are over the center of the receptive field. The phase as reflected by the sign indicates whether the unit responds with an increase of firing when the stimulus is positive or negative. The phase plot is therefore able to reveal, for example, lateral inhibition or center-surround organization, where the center of a receptive field is surrounded by an area where the polarity of the response is reversed.
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Along with the receptive field, the location of the cell within the DON was determined by the following procedure. On a photograph of the exposed brain taken immediately after the surgery, the position of the electrode was marked according to measurements from the preparation. After the experiment, the head was fixed with 4% paraformaldehyde and the meninges removed to expose the brain surface. The DON was clearly visible as a crest on the hind brain and its outline was superimposed onto the photograph. The position of each DON unit was then calculated as percent of DON length and width, respectively.
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Results |
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Whilst it is safe to assume that we were recording from cells in the DON
and not from PA fibers, we do not know from which cell types we were
recording. At least one class of neurons have ascending projections to the
midbrain (Hofmann et al.,
2002), but it is not known how many other cell types are present
in the DON of the paddlefish.
The physiological properties of the recorded units in the DON are so similar, however, that we conclude that the recordings were only from one cell type with large cell bodies. Below we describe the response properties of these cells and compare them with those of primary afferent fibers to characterize the processing of electrosensory signals in the DON.
Spontaneous activity
The spontaneous rates of 30 PA fibers were measured, and range from 10 to
75 Hz with a mean rate of 44.22±14.73 Hz. From the spike data, the
instantaneous frequency plot was computed and the average root mean square
calculated (8.05±3.60 Hz), reflecting the variability of PA interspike
intervals. Spontaneous rates of DON units were lower (30.94±10.56 Hz,
N=55, P<0.0001). The average root mean square
(3.86±1.69 Hz) was significantly lower than in PA fibers
(P<0.0001), indicating that the variability in interspike
intervals was lower in DON units than in PA fibers.
Differences in the sequence of interspike intervals between DON units and PA fibers were detected by autocorrelation of the spike trains. Fig. 2 shows the autocorrelation of a PA fiber (A) and a DON unit (B). In the PA fibers, long range autocorrelation is clearly visible, but DON units show only some short-range autocorrelation. Fig. 2C shows autocorrelation values for 60 DON units and 31 PA fibers plotted against their mean firing rate. Most PA fibers show high autocorrelation values (6.10±5.05), but some are in the range of DON units (2.03±0.59). In contrast, DON units never show the high autocorrelation values observed in most PA fibers.
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Sensitivity and frequency tuning
Frequency tuning and sensitivity was tested with uniform field, constant
amplitude sine wave stimuli with frequencies between 0.05 and 20 Hz. A
stimulus intensity (25 µV cm-1) was chosen that avoided
saturation of the firing rate (rates were <100 Hz, but cells can be driven
up to 200-300 Hz). Fig. 3 shows
the peak firing rates of PA fibers and DON cells during stimulation at
different frequencies. There is no difference in frequency tuning between PA
and DON units. Although we did not test for threshold at each frequency, our
constant amplitude stimuli provided an estimate about the sensitivity of the
units. However, no differences in sensitivity between PA and DON units could
be observed with our fixed 25 µV cm-1 amplitude.
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The positive peak width (representing the size of the receptive field) is larger in DON units compared to PAs. Peak width was measured as the width at half maximum amplitude (2.11±0.4 s for PA fibers and 2.93±0.89 s for DON units, P<0.05).
Movement detection
In this set of experiments, we tested the responses of PA and DON neurons
to an unmodulated DC stimulus with a moving dipole. The source dipole field
was oriented perpendicular to the movement direction (monopolar configuration)
and moved at a speed of 5 cm s-1, which is equivalent to the normal
swimming speed of the fish. Fig.
5 shows the change in firing rate of PA and DON units to the
moving DC field, with traces aligned to the receptive field of the unit. The
bottom trace in each panel is the average of the traces above. Both PA and DON
units show very similar responses to the stimulus. DON responses showed a
somewhat broader peak than those of PA fibers, as has been noted for the
receptive field size (see above), although there is no evidence for
differences in sensitivity to the moving stimulus.
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Discussion |
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Another reason why we are convinced that our DON recordings do not include
PA fiber activity is that we did find differences between PA and DON units in
their spontaneous activity. The firing rate is lower and more regular in DON
neurons. Furthermore, autocorrelation analysis of DON and PA units shows that
61% of PA units have long-range autocorrelation values of more than 3 (see
Fig. 2). From the 60 DON units
analyzed, only one has an autocorrelation value higher than 3. Thus, based on
the autocorrelation analysis, it is not likely that we were recording from
afferent fibers in the DON. Long-range autocorrelations were described by
Bahar et al. (2001) in the
paddlefish electrosensory afferents, but our data show here, for the first
time, that they are absent in secondary brain stem neurons. Although it is not
clear what causes these autocorrelations, preliminary experiments show that
they could be the result of the presence of multiple spike-generating zones in
the afferent fiber. Two coupled oscillators with slightly different
frequencies will cause alternating interspike intervals. In the
autocorrelation analysis, this shows up as alternating positive and negative
correlations since a long interval is followed by a short one and vice
versa. The node or dip in the autocorrelation
(Fig. 2A) may be caused by the
interference of two different oscillation frequencies. This is similar to the
beat frequency that exists if two oscillators with different frequencies are
mixed. The frequency of this `beating' represents the difference in
frequencies of the two oscillators. In the periphery, branching of fibers into
several myelinated roots has been described in the paddlefish
(Wilkens and Hofmann, 2002
) as
well as in catfish (Peters and van
Ieperen, 1989
; Peters et al.,
1997
) and could indicate the presence of multiple oscillators.
Whereas the mechanisms causing correlations in spike trains are not known, the
functional significance may be in the suppression of low frequency noise
(Chacron et al., 2004
,
2005
).
Comparison of paddlefish PA and DON activity with elasmobranchs shows that
paddlefish neurons have a higher rate of spontaneous activity. In
Platyrhinoidis triseriata and Raja erinacea, mean rates of
PA are 8-18 Hz (New, 1990;
Bodznick et al., 2003
). Only
Raja eglanteria shows PA rates as high as 45 Hz
(Sisneros et al., 1998
) that
are similar to the 44 Hz found in the paddlefish. Spontaneous rates in
sturgeons are 20-60 Hz (Teeter et al.,
1980
) and in catfish 50-100 Hz
(Finger, 1986
), values
comparable to or exceeding the ones in the paddlefish. Finally, amphibian
rates are 15 Hz (Münz et al.,
1984
; Schlegel and Roth,
1997
) in the range of some elasmobranchs. Spontaneous rates for
DON units are always lower than for PA fibers [e.g. 1.2 Hz in elasmobranchs
(New, 1990
) and 6 Hz in
catfish (McCreery, 1977
)]. The
31 Hzrecorded here in the paddlefish DON are the highest found in any passive
electrosensory animal. Also unique for the paddlefish is that DON units are
more regular in their interspike intervals than PA fibers. In other animals,
DON units are always described as being more irregular than PA fibers
(McCreery, 1977
;
New, 1990
;
Bodznick et al., 2003
).
There are also some differences in the paddlefish between PA and DON units that are revealed by slowly scanning their receptive fields. The size of the receptive field of DON units is slightly larger and the inhibitory surround is more pronounced than in PA fibers. In particular, the inhibitory surround resembles the receptive field organization in the visual system, where lateral inhibition mediated by retinal intermediates is responsible for the surround inhibition. However, we find inhibition in the paddlefish electrosensory system is already in PA fibers. Since there are no efferents from the brain innervating the receptors, PA fibers act completely independent of each other and it is not easy to understand how lateral inhibition could arise in the periphery. However, there is one possible explanation that does not require lateral inhibition at the neuronal level. While approaching the receptive field with a stimulus electrode, the signal could take one of two paths to the receptor: one directly through the water to the pore of the receptor and the other through the skin next to the stimulus electrode and through the animal to the base of the receptor cells. This latter path would reverse the effect on the receptor, since it is basically stimulating the internal reference of the receptor. If the internal tissue resistance is much lower than the water resistance, this path could have an overall resistance lower than the path through the water and therefore dominate the response if the stimulus electrodes are far from the receptive field, but close to another skin area. To test this hypothesis, a detailed study of skin and tissue impedances is required.
Apart from this slight difference, we were struck by the overall
similarities of PA and DON responses and the question arises what function can
be attributed to the DON in signal processing. In a previous paper
(Hofmann et al., 2004) on
temporal information processing in the paddlefish electrosensory system, we
showed that the DON cells compute the first derivative in time of the electric
fields at the receptor. One property defining the first derivative is that the
gain is proportional to the frequency. This results in a linear slope of the
frequency tuning curve that has been found in virtually all passive
electrosensory animals (Bretschneider et
al., 1985
; Peters and Evers,
1985
; Kalmijn,
1988
; Andrianov et al.,
1996
; Schlegel and Roth,
1997
; Tricas and New,
1998
; Bodznick et al.,
2003
). A large part of the processing toward the first derivative,
however, could be attributed to the PA or receptor cells, since the frequency
tuning curve of the PA already shows a relatively good linear relationship
between gain and frequency (Fig.
3). Perhaps the most important function of the DON that has yet to
be investigated in the paddlefish is the cancellation of noise. An animal's
own movements cause modulations in the discharge rate of electroreceptors that
are canceled out by common mode rejection and adaptive filters, as found
mainly in elasmobranchs (Montgomery,
1984
; Montgomery and Bodznick,
1999
; Bodznick et al.,
2003
). Adaptive filters involve massive descending input from the
cerebellum into a part of the DON termed the crista cerebellaris. Since this
structure is well developed in the paddlefish, adaptive filter mechanisms are
probably employed in the paddlefish DON as well.
The most surprising result of our investigation is the lack of a
topographic relationship between receptive fields in the skin and the position
of the corresponding neurons in the DON. Although we have not looked for
topography in the dorso-ventral axis (depth), we think topography in this axis
is unlikely since the DON is organized in layers, with the PA fibers entering
dorsally. Principle efferent neurons are located below in a thinner horizontal
layer (Hofmann et al., 2002).
In other layered structures like the cortex or midbrain tectum, topography is
always organized perpendicular to the layers. In the DON of the little skate,
Bodznick and Schmidt (1984
)
reported some topographical order when the electrode was advanced
dorso-ventrally, but in this animal the DON is oriented obliquely and their
figures clearly show that the apparent dorso-ventral topography is in fact a
medio-lateral one. There is no evidence for a topography across the different
layers of the DON.
In many sensory systems, information from arrays of receptors is processed
in topographic maps. This is particularly important in modalities that reveal
information about the location of objects in space. The paddlefish
electrosensory system apparently breaks that rule. Behavioral studies have
clearly shown that the electrosensory system alone is sufficient to localize
objects in space (Wilkens et al.,
2001), yet there is no topographic map in the brain stem. This is
even more puzzling since topographic maps were found in other passive
electrosensory animals (Bodznick and
Schmidt, 1984
; New and Singh,
1994
). However, these studies only examined PA projections, either
by tracers applied to different branches of the lateral line nerves to study
their termination zones or by multiunit activity recorded within the
superficial fiber layer of the DON. There is no comparable study on the
location of second order cells within the DON in relation to their receptive
fields. More detailed studies are needed to solve this problem, but two lines
of thought may be considered.
Topographic organization in the central nervous system could be for two
reasons. First, it could have a true function. An orderly arrangement of
receptive fields in a topographic map may be required for lateral computations
such as contrast enhancement or movement detection. For these spatial
computations, a neuron that sends collaterals to neighboring neurons has to
rely on the fact that the neighboring neurons also have adjacent receptive
fields in the periphery. Second, topography could be the consequence of
developmental constraints, without serving any physiological function.
Preliminary tracer studies in the paddlefish showed that the primary
projections from the lateral line nerve innervating the rostrum and a branch
innervating the electroreceptors on the gill cover form separate terminal
fields in the DON, with the rostrum nerve terminating more laterally than the
gill cover branch. This confirms earlier studies in catfish and elasmobranchs
(Bodznick and Schmidt, 1984;
New and Singh, 1994
). However,
our data on second order DON cells showed that this projection pattern does
not lead to a topographic distribution of cells in the DON. This suggests that
the projection pattern of the different branches of the lateral line nerves
within the DON may be due to the fact that fibers from different branches tend
to stay together and not intermingle with each other. The developmental
sequence of invading pioneer fibers of the lateral line nerves may determine a
coarse `topography' that does not necessarily serve any physiological
function.
Another misconception is that a topographic map is required to preserve
topographic information. As mentioned above, a topographic map may be required
for spatial information processing between neighboring receptor channels. In
the paddlefish, we found little sign of spatial information processing within
the DON, such as contrast enhancement or movement detection, and no
topographic organization. Yet, DON neurons show well-defined receptive fields,
and behavioral studies clearly showed that paddlefish use spatial information
about prey location for feeding (Wilkens
et al., 2001). However, if we look closely at the coordinate
systems involved, we find that the distance of the source from the detecting
fish is an important factor in determining the behavioral response. This
dimension is not present in a simple somatotopic body map and has to be
extracted computationally. An important sensori-motor interface mediating prey
capture is the mesencephalic tectum (TM), and it has been shown that major
ascending electrosensory pathways reach this structure
(Hofmann et al., 2002
).
Although not yet investigated in the paddlefish, the TM is topographically
organized in all vertebrates investigated so far. This has been shown mainly
for the visual system that projects to the TM in all vertebrates. An object in
front of the animal would be represented in a frontal field of the TM and an
object more lateral would be represented in a different location, i.e. more
laterally. If we assume that this is also the case in the paddlefish and if we
further assume that electrosensory information reaching the TM carries
topographic information that is in register with the visual world, an object
at the tip of the rostrum of the paddlefish would be represented in the
frontal field and an object centered at the same `somatotopic' location, but
further from the skin surface, would be represented more laterally. In other
words, an object centered at the same `somatotopic' location, but at different
distances, would be represented in different locations in the TM. A
somatotopic map in the DON simply to preserve spatial information is thus not
a satisfactory explanation since the somatotopic map would have to be
transformed anyway into a spherical map that contains distance information, at
least for skin locations remote from the eye.
But how can an array of receptors compute the distance to the source?
Initial calculations by Kalmijn
(1988) and a more detailed
analysis (Hofmann and Wilkens,
2005
), showed that there is sufficient information in the time
domain in each receptor channel. A single receptor traversing an electric
field receives an electrical signal over time that contains sufficient
information necessary to extract the location, including the distance, of the
source, independent of source amplitude, size and orientation. The computation
algorithm involves an analysis in the time domain, but does not require a
spatial analysis. It is intriguing that an important step in this algorithm is
the computation of the first derivative, which matches very well the behavior
of DON units (Hofmann et al.,
2004
).
We still have to show that the paddlefish, and perhaps other electrosensory
animals, actually compute the temporal structure of electrical events rather
than, or in addition to, the spatial structure still useful at short
distances. What we have shown so far, however, is that the initial step in
signal processing in the DON is perfectly suited to preserve temporal
information (Hofmann et al.,
2004) and, in this study, that there is no topographic map of the
body surface within the DON and little sign of spatial information processing
such as lateral inhibition, contrast enhancement or movement detection, at
least at the level of the brain stem.
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Acknowledgments |
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