Adaptations to an organism's environment often involve sensory system modifications. In this study, we address how evolutionary divergence in sensory perception relates to the physiological coding of stimuli. Mormyrid fishes that can detect subtle variations in electric communication signals encode signal waveform into spike-timing differences between sensory receptors. In contrast, the receptors of species insensitive to waveform variation produce spontaneously oscillating potentials. We found that oscillating receptors respond to electric pulses by resetting their phase, resulting in transient synchrony among receptors that encodes signal timing and location, but not waveform. These receptors were most sensitive to frequencies found only in the collective signals of groups of conspecifics, and this was correlated with increased behavioral responses to these frequencies. Thus, different perceptual capabilities correspond to different receptor physiologies. We hypothesize that these divergent mechanisms represent adaptations for different social environments. Our findings provide the first evidence for sensory coding through oscillatory synchrony.https://doi.org/10.7554/eLife.08163.001
The mormyrids are a family of fish that can generate and detect weak electric fields that they use to navigate and communicate. Each species in this family produces its own distinct shape of electrical signals. Other fish detect these signals using structures called receptors, which then send information to the brain in the form of an electrical nerve impulse.
Previous research showed that different species of fish respond to different aspects of the electric fields they detect, depending on the receptor types that they have evolved. For example, some species have ‘spiking receptors’ that send a spike of activity to the brain at the start or end of a detected electrical pulse. This allows the fish to detect subtle changes to the shape of the detected electric field. Other species have sensors known as oscillatory receptors; these send a continuous wave of nerve activity to the brain even when no electric field is detected. It is not clear exactly how oscillatory receptors work or what the roles of these different receptor types are.
Baker et al. have now recorded the activity of the receptors of mormyrid fish as they were exposed to electrical fields that mimic the signals they are normally exposed to in their daily environment. The recordings confirmed previous results that suggest that species with spiking receptors are able to detect variations in the shapes of electrical signals produced by other fish because the receptors produce spikes of activity at the start or end of a detected signal. This response pattern allows their brains to analyze the signal shape.
In contrast, Baker et al. found that when oscillatory receptors detect a new electric field, they reset their pattern of nerve activity, causing multiple receptors to briefly synchronize their activity. This enables the fish to detect where a signal has been sent from. However, the oscillatory receptors are unable to detect any variations in the shape of the detected electric signal. The oscillatory receptors are most sensitive to certain electric field patterns that are produced by large groups of a single species of fish. Further experiments showed that the fish also change their behavior when these particular electric field patterns are detected.
Why some species of mormyrids have spiking receptors while others have oscillatory receptors is unknown. It is also unknown how the brain processes oscillatory receptor activity. Future studies will investigate whether these two receptor types may be linked to differences in fish's social behavior, and how the neural networks for processing sensory information differ between the two types of fishes.https://doi.org/10.7554/eLife.08163.002
Evolutionary change in sensory systems can have profound effects on perception and behavior (Carlson and Arnegard, 2011; Carlson, 2012; Baldwin et al., 2014). Recently, we documented a striking example of evolutionary change in the electrosensory systems of weakly electric fish (Carlson et al., 2011). Anatomical modifications of sensory receptors and the associated brain pathway that processes electric communication signals resulted in a newfound ability to detect subtle variations in these signals. This perceptual expansion led to a dramatic increase in the rates of signal evolution and species diversification. However, how these anatomical and perceptual differences relate to physiological differences in the underlying information processing mechanisms was unknown. Here, we investigate how evolutionary divergence in sensory perception relates to differences in peripheral sensory coding.
Mormyrid fish communicate by producing a species-specific electric organ discharge (EOD) at variable interpulse intervals (IPIs) (see Carlson, 2002 for review). The ability of some species to detect subtle variation in EOD waveforms is related to differences in the anatomy and physiology of peripheral sensory receptors called knollenorgans (Harder, 1968a, 1968b; Carlson et al., 2011). In species sensitive to waveform variation, these receptors are broadly distributed across the head, back, and underbelly (Carlson et al., 2011). These receptors fire a single, time-locked spike in response to an electric stimulus in all species previously studied (Bennett, 1965; Harder, 1968b; Hopkins and Bass, 1981; Lyons-Warren et al., 2012). Spiking receptors encode electric pulse duration into spike-timing differences, with some receptors responding to pulse onset and others to pulse offset (Hopkins and Bass, 1981). These timing differences are compared in the midbrain (Friedman and Hopkins, 1998; Lyons-Warren et al., 2013).
In contrast, species insensitive to waveform variation have receptors that produce spontaneously oscillating potentials at frequencies up to 3 kHz (Harder, 1968b). These receptors are clustered into three groups, called rosettes, on each side of the head (Harder, 1968b; Lavoue et al., 2004, 2010; Carlson et al., 2011). How oscillatory receptors encode electric communication signals is unknown.
Oscillations in neural activity are widespread throughout the brains of many animals, including humans, and are known to play important roles in generating rhythmic motor behaviors (Ramirez et al., 2004; McCrea and Rybak, 2008). However, how oscillations contribute to processes such as sleep, attention, memory, motor output, and sensory coding remains unclear (Buzsaki and Draguhn, 2004; Thut et al., 2012; Canavier, 2015). Oscillations could facilitate multiplexed signal coding in which spikes carry information depending on the phase of the oscillation during which they occur (Thut et al., 2012). Another effect of oscillations could be to enhance communication between neural populations during periods of synchronized excitability (Thut et al., 2012). Additional hypothesized functions include signal gating, feature binding, and cross-modal integration (Thut et al., 2012), although a clear functional role for electrical oscillations in sensory processing has remained elusive (Canavier, 2015).
To gain insight into the neural basis of differences in perceptual abilities among species, here we investigate for the first time how oscillating receptors encode electric stimuli. Using extracellular recordings, we reveal a novel phase-reset mechanism that mediates signal detection by transiently synchronizing the oscillations among receptors. This is the first demonstration of information coding by oscillatory synchrony at the periphery. These phase resets did not encode pulse waveform, explaining why species with oscillating receptors cannot behaviorally discriminate EOD waveform variation. Furthermore, we provide physiological and behavioral evidence that oscillating receptors respond most strongly to communication signals with fast temporal patterns that are only produced by large groups of conspecifics, suggesting that oscillating receptors may be specialized for group signal detection.
To measure baseline activity rates, we recorded extracellular spontaneous activity from the sensory receptors of five species (Figure 1). This sample included two species with broadly distributed spiking receptors, as well as Petrocephalus microphthalmus, whose receptor physiology was previously unknown. P. microphthalmus is behaviorally sensitive to EOD waveform variation, but it evolved this ability independently of other species with broadly distributed receptors (Carlson et al., 2011). We found that the receptors of P. microphthalmus indeed fire spikes (Figure 1A), just like other mormyrids with broadly distributed receptors and EOD waveform sensitivity. We also recorded spontaneous oscillations from rosette receptors of two species (Figure 1C,D). Spontaneous interspike intervals and spontaneous oscillation periods varied across species (Figure 1B,D).
Oscillating receptors are organized into three rosettes on each side of the head (Figure 2A) (Harder, 1968b; Lavoue et al., 2004, 2010; Carlson et al., 2011). To study how spontaneous activity varied within a rosette, we obtained spontaneous recordings from every receptor in a single rosette in one Petrocephalus tenuicauda (Figure 2B). Spontaneous oscillation amplitude was largest in the center of the rosette and progressively decreased towards the periphery. Oscillation frequency and amplitude were not significantly correlated (Spearman R = 0.27, t(22) = 1.3, p = 0.21), although frequencies tended to be highest near the center of the rosette (Figure 2B).
To determine whether the oscillations of receptors in different rosettes were synchronized with one another, we collected simultaneous recordings from pairs of receptors, each located in a different rosette (see Figure 2C, top). We then determined the phase of the oscillations throughout each pair-wise recording using a Hilbert transform (see Figure 2C, bottom). Next, we constructed probability distributions of the phases for each receptor in pair-wise recordings. If the phases are truly independent, then the joint probability histogram should be similar to the product of the individual probability distributions. Indeed, the joint probability histograms (e.g., Figure 2E) closely resembled the product of the individual probability distributions (e.g., Figure 2D). We also computed the joint probability histogram for surrogate data consisting of non-simultaneously recorded oscillations (e.g., Figure 2F). These histograms should match those of the simultaneous recordings if the oscillations are truly independent. Indeed, the joint probability distributions of non-simultaneous recordings closely resembled those of the simultaneous recordings (compare Figure 2F with Figure 2E).
To evaluate statistically the possibility of phase coupling between receptors in different rosettes, we determined the circular correlation coefficient (r) of the instantaneous oscillatory phases between each receptor pair (Berens, 2009). We averaged the resulting r over five 1-s recordings from each pair. Across 14 receptor pairs in one P. tenuicauda, the mean r was 0.05 + 0.05. For comparison, we generated non-simultaneously recorded surrogate data sets consisting of the first of five 1-s recordings from one receptor in the pair and the fifth recording from the other pair, and vice versa. There was no significant difference between the r of simultaneous recordings and the r (mean = 4 × 10−4 + 7 × 10−4) of non-simultaneous recordings (Wilcoxon matched-pairs test, Z(14) = 1.7, p = 0.084). In another P. tenuicauda, we recorded from six pairs of receptors and found similar results, with no differences in the circular correlation coefficient for phases of simultaneous (mean r = −0.06 + 0.04) and non-simultaneous (mean r = 0.001 + 0.001) recordings (Wilcoxon matched-pairs test, Z(6) = 1.8, p = 0.075). Therefore, oscillatory phase was independent across receptors in different rosettes.
To determine whether the individual receptors within rosettes oscillated at the same frequency, we recorded spontaneous activity with a differential electrode pair centered over a single rosette. The differential electrode allowed us to record local field potentials resulting from the combined oscillations of multiple receptors. If the receptors near our electrodes were oscillating independently at different frequencies, we should see beats, or amplitude modulations, in the field potentials due to constructive and destructive interference. Indeed, beats were present in these recordings (see example in Figure 2G), indicating that our electrode was in fact recording activity from multiple sources with different frequencies. Accordingly, at least three distinct peaks were present in the power spectrum of this recording (black trace in Figure 2H). Recording activity at a position 2 mm posterior to this location led to the loss of two of these peaks and the addition of two new peaks, suggesting that the electrode was recording activity from a distinct, but overlapping population of receptors compared to the previous location (red traces in Figure 2G,H). Qualitatively similar results were seen in differential recordings from additional rosettes. Therefore, in agreement with a previous report (Harder, 1968b), oscillatory activity of individual receptors is not correlated within or across rosettes.
To understand sensory encoding by these receptors, we recorded responses to focal stimulation with monopolar square pulses (Figure 3). We delivered both positive- and negative-polarity pulses to mimic the stimulation that receptors at different locations on the fish's body would experience during a natural global stimulus (Hopkins and Bass, 1981). As shown previously, spiking receptors responded with a single time-locked spike in response to inward current transients (Bennett, 1965), which occur at the onset of a positive-polarity pulse and at the offset of a negative-polarity pulse (Figure 3A,B). Spike probability depended on pulse duration (Figure 3C) but was high for stimulus durations within the range of total durations of conspecific EODs (horizontal bars in Figure 3C).
Oscillating receptors responded to square pulses with phase resets and amplitude increases (Figure 3E,F), which occurred both in receptors with high-amplitude spontaneous oscillations as well as in receptors with little to no spontaneous oscillatory activity. The degree of amplitude enhancement relative to prestimulus amplitudes depended on pulse duration (Figure 3G). At the stimulus duration that elicited the maximum oscillation amplitude in each receptor, there was a positive correlation between spontaneous and stimulus-evoked oscillation amplitudes (Spearman R = 0.90, t(3) = 3.6, p = 0.037).
Spiking receptors encode square pulse duration into differences in spike times between receptors (Hopkins and Bass, 1981; Lyons-Warren et al., 2012). For instance, the differences in spike times elicited by each polarity of a square pulse of durations 0.10 and 0.20 ms were exactly 0.10 and 0.20 ms, respectively (Figure 3A,B). Across all spiking receptors, this spike-timing difference perfectly matched pulse duration for pulses longer than ∼0.1 ms (Figure 3D). Importantly, spike-timing differences accurately represented pulse duration over the behaviorally relevant ranges for total EOD duration in each species (horizontal bars in Figure 3D). Such precise coding of stimulus-timing cues mediates the demonstrated ability of species with spiking receptors to detect EOD waveform variation (Hopkins and Bass, 1981; Arnegard et al., 2006; Machnik and Kramer, 2008; Feulner et al., 2009; Carlson et al., 2011).
In oscillatory receptors, positive- and negative-polarity square pulses elicited phase resets that differed by ∼180° (Figure 3E,F). This phase difference was constant regardless of pulse duration (Figure 3L). To determine whether oscillating receptors could encode pulse duration in a manner similar to the spike-timing differences of spiking receptors, we measured the difference in times of oscillatory peaks evoked by opposite-polarity pulses (Figure 3E,F). For pulse durations exceeding receptors' intrinsic oscillation periods (∼0.5 ms), the phase could be reset in response to each stimulus edge. In these instances, we measured the oscillation-timing difference elicited by the onset of a positive-polarity pulse and the offset of a negative-polarity pulse. However, pulses of shorter durations did not elicit phase resets to each stimulus edge, since these durations are shorter than receptors' intrinsic oscillation periods. For these stimulus durations, we measured the oscillation-timing differences after stimulus offset (e.g., Figure 3E,F). Oscillation-timing differences did not accurately encode pulse durations shorter than ∼1 ms (Figure 3H), reflecting phase resets to the leading edge of the pulse that differ by ∼180° regardless of pulse duration (Figure 3E,F,L). This means that oscillatory responses could not encode pulse durations found in conspecific EODs (black bar in Figure 3H). Therefore, species with oscillating receptors cannot behaviorally detect waveform variation at least in part because the precise timing cues are not encoded at the periphery. However, oscillating receptors would be able to encode the location of the stimulus based on which receptors reset to a peak first, owing to differences in stimulus polarity.
To confirm that natural EOD waveforms also elicit oscillatory phase differences of ∼180°, we presented conspecific EODs at varying intensities to oscillating receptors (Figure 3I,J). We measured the vector strength and phase differences for responses to opposite-polarity stimuli. The vector strength is a measure of phase-locking that ranges from 0 (phase is random across repetitions) to 1 (identical phase across repetitions). At each intensity, we compared the phases elicited by each polarity using the parametric second-order Hotelling test for paired circular data (Zar, 1999). At intensities for which the phase resets evoked by opposite polarity EODs were significantly different, phase differences were always ∼180° (filled circles in Figure 3K).
Spiking receptors have been reported to be most sensitive to the frequencies occurring in conspecific EOD waveforms (Hopkins, 1981; Bass and Hopkins, 1984; Lyons-Warren et al., 2012). To confirm this finding in our test species, we compared the frequency sensitivity of individual spiking receptors to the power spectra of conspecific EODs (Figure 4A,B,D). We collected threshold frequency tuning curves as described previously (Lyons-Warren et al., 2012). We defined a receptor's best frequency as the frequency with the lowest threshold. In general, spiking receptors had best frequencies that were close to the frequencies with the highest power in conspecific EODs (compare Figure 4D with Figure 4B). On average, the best frequencies of spiking receptors were within one octave of the peak power frequencies of conspecific EODs (filled symbols in Figure 4F).
To determine the frequency sensitivity of oscillating receptors, we also collected frequency tuning curves from oscillating receptors. Due to the oscillatory nature of both stimulus artifact and response, we could not use the same constant-frequency stimuli that we used to characterize frequency tuning in spiking receptors. Instead, we presented single-cycle bipolar sine waves at three intensities and measured the vector strength of phase resets at stimulus offset. We defined an oscillating receptor's best frequency as the frequency that elicited the largest vector strength.
Oscillating receptors were most sensitive to frequencies well below the peak power frequency in conspecific EODs (compare Figure 4E with black trace in Figure 4C). On average, the best frequencies of oscillating receptors were more than two octaves below the peak power frequencies of conspecific EODs (open symbols in Figure 4F).
At the stimulus intensity where tuning was sharpest (10 nA), the best frequencies (0.8–2.5 kHz) of oscillating receptors roughly corresponded to their intrinsic oscillation frequencies (1.5–2.0 kHz), suggesting that their frequency tuning resulted from resonance with their spontaneous oscillations. To test this hypothesis, we also delivered single-cycle bipolar sine waves of durations equal to multiples of each receptor's intrinsic oscillation period. Oscillatory phase-locking was strongest for stimulus durations matching each receptor's spontaneous oscillation period (Figure 4G).
Electric communication signals consist of the EOD produced at variable IPIs. Whereas the stereotyped EOD waveform can contain identifying information, such as species and sex, the IPIs convey behavioral state (see Carlson, 2002 for review). To investigate IPI coding in the three species with spiking receptors included in this study, we presented receptors with a pair of 0.2-ms duration monopolar square pulses at a range of IPIs (Figure 5A,B).
The probability that a receptor fired a spike in response to both pulses in the pair decreased at the shortest intervals tested (Figure 5B,C). However, receptors of all species reliably fired spikes at the reported minimum IPIs produced by individual fish (∼8 ms) (Hopkins, 1986; Carlson, 2002).
In agreement with previous studies (Bell and Grant, 1989; Baker et al., 2013), spiking receptors encoded IPIs into interspike intervals within receptors, with the interspike intervals exactly matching the IPI (Figure 5A,D). IPIs shorter than 1 ms in Pollimyrus adspersus and P. microphthalmus failed to evoke spikes in response to the second pulse due to receptors' refractory periods (Figure 5B,C), which is why data points below these values are missing from Figure 5D. Spiking receptors can therefore faithfully encode the IPIs generated by individual signaling fish into interspike intervals within receptors.
To understand IPI coding by oscillatory receptors, we analyzed the amplitude and phase of oscillations as a function of the interval between pulse pairs (e.g., Figure 5E,F). At the shortest intervals tested, the stimulus artifact of the second pulse obscured the oscillatory response to the first pulse (Figure 5F). Thus, to investigate how oscillating receptors encode IPIs, we measured interoscillation intervals as the time between the first oscillatory peak following a single-pulse stimulus and the first oscillatory peak following the second pulse in the paired stimulus (Figure 5E,F). This method was equivalent to measuring the time interval between oscillatory peaks evoked by each pulse in the pair, but it allowed us to measure the interoscillation intervals at the shortest IPIs where the oscillatory response was obscured by stimulus artifact. Interoscillation intervals accurately represented the IPI for intervals of ∼1 ms and longer (Figure 5E,G). Importantly, interoscillation intervals accurately encoded IPIs over the range produced by individual fish (>8 ms) (Hopkins, 1986; Carlson, 2002).
Although interoscillation intervals accurately represent IPIs longer than ∼1 ms, how would the system disambiguate these stimulus-evoked oscillatory peaks from ongoing oscillations? Phase resets in oscillatory receptors serve to transiently synchronize the population in response to a stimulus (Figure 6). Because oscillation frequency varies across receptors, responses rapidly desynchronize (Figure 6). For instance, in response to a single square pulse, across-receptor synchrony is greatest for the first poststimulus oscillatory peak (Figure 6A). In response to a pair of pulses, across-receptor synchrony is high immediately following each pulse and rapidly decreases (Figure 6B). To further illustrate this, we recorded responses to a single conspecific EOD of normal and reversed polarities from all 36 receptors in a single rosette of one P. tenuicauda (same receptors illustrated in Figure 2B). Responses from four of these receptors are shown in Figure 6C. Notice that again, across-receptor synchrony is greatest for the first poststimulus oscillation, but then rapidly declines. Summing responses across all 36 receptors resulted in a single, distinct peak immediately following the stimulus (Figure 6D), due to a transient synchronization across receptors followed by increasing asynchrony of receptors oscillating at different frequencies. In this way, the electrosensory system could use synchrony across the population of receptors to distinguish between stimulus-evoked and spontaneous oscillation cycles.
Oscillating receptors also encoded IPI into amplitude changes (Figure 7A,B). At the highest intensity tested, oscillation amplitudes were attenuated relative to single-pulse responses for IPIs below 1 ms (black curve in Figure 7C). As stimulus intensity decreased, however, oscillation amplitudes became selectively enhanced in response to 0.5-ms IPIs (Figure 7C). Phase-locking, as measured by the vector strength of oscillatory responses to the second pulse in the pair, also depended on IPI and stimulus intensity (Figure 7E). As the intensity decreased, responses became more sharply selective for IPIs around 0.5 ms, which is near the intrinsic oscillation periods (0.47–0.99 ms) of receptors in this species. This is suggestive of resonance in which stimulating an oscillating receptor with a pair of pulses separated by the receptor's intrinsic oscillation period results in stronger responses.
To test this hypothesis, we presented receptors with IPIs equal to multiples of their intrinsic oscillation periods. As stimulus intensity decreased, oscillatory responses became more selective for IPIs equal to the intrinsic oscillation period (Figure 7D). At the lowest intensity tested, the phase-locking was also greatest for IPIs matching receptors' intrinsic oscillation periods (light gray curve in Figure 7F).
These results reveal that oscillating receptors produce the largest responses for IPIs matching their intrinsic oscillation periods. These periods, however, are at least one order of magnitude shorter than the IPIs that single fish produce. Therefore, signals arriving at submillisecond IPIs will be easier to detect, even though these IPIs are too short for each individual pulse within the train to be encoded with a phase reset. Why would some species of mormyrids have receptors that are maximally sensitive to signals too short to be produced by individual fish?
Even though the minimum IPI a single fish produces is approximately 8 ms, a large group of signaling fish may collectively produce much shorter IPIs. Could the oscillations in rosette receptors be tuned to these shorter IPIs? To test this hypothesis, we first recorded 20 min of electric signaling from individual fish of a species with spiking receptors (P. microphthalmus) (e.g., Figure 8A) and from individual fish of a congeneric species with oscillating receptors (P. tenuicauda) (e.g., Figure 8D). Next, we recorded electrical activity from group tanks of each of the same two species (Figure 8B,E). Indeed, group signals contained much shorter IPIs than single-fish signals (compare Figure 8B with Figure 8A, and Figure 8E with Figure 8D; note different time scales).
To determine whether spiking and oscillating receptors could encode the very short IPIs in group signals, we compared the magnitude of receptor responses with the IPIs recorded from a group of the corresponding species (Figure 8C,F). The spiking receptors of P. microphthalmus could not respond to the submillisecond intervals present in collective conspecific group signals (Figure 8C). In contrast, oscillating receptors responded most strongly to 0.5-ms IPIs, which are found only in group signals (Figure 8F). Thus, one advantage for species with high-frequency oscillating receptors may be the ability to detect very short IPIs in group communication signals that spiking receptors cannot encode due to refractoriness.
If oscillating receptors facilitate detection of communication signals produced by a group of individuals, then species with oscillating receptors should exhibit selective behavioral responses to submillisecond IPIs, and species with spiking receptors should not. To test this hypothesis, we performed behavioral playback experiments in which we presented a single fish with a train of 10 conspecific EODs at constant IPIs (Figure 9).
In general, mormyrids respond to electric signals by increasing or decreasing their rate of EOD production (Moller et al., 1989; Post and von der Emde, 1999; Carlson et al., 2011; Lyons-Warren et al., 2012). Increases in EOD rate have been interpreted to be an orienting response to a novel stimulus (Post and von der Emde, 1999). Decreases in EOD rate, or social silence, have been hypothesized to allow the silent fish to attend to another fish's signals or to make the silent fish electrically ‘invisible’ to other fish (Moller et al., 1989). As previously described in another species (Post and von der Emde, 1999), the same fish often produced both an increase and a decrease in response to a stimulus (Figure 9). In general, P. tenuicauda tended to respond with a decrease followed by an increase (Figure 9A), whereas P. microphthalmus tended to respond with an increase followed by a decrease (Figure 9B). Regardless of an individual's response pattern, we measured the maximum increase and decrease in EOD rate in response to each stimulus and compared the fish's electric signaling activity evoked by IPI stimuli to that evoked by a single conspecific EOD. A species with oscillating receptors produced the strongest EOD rate increases at the shortest and longest IPIs tested (P. tenuicauda in Figure 9C). The shortest IPIs (≤5 ms) correspond to those found only in signals recorded from groups of fish.
A congeneric species with spiking receptors (P. microphthalmus in Figure 9D) responded differently to IPIs than the species with oscillating receptors (repeated-measures ANOVA, interaction between species and IPI, F(6,12) = 3.4, p = 0.0048). The species with spiking receptors produced the greatest EOD rate increases for IPIs in the intermediate range of those tested, which roughly correspond to the minimum IPI produced by individual fish.
In general, the species with oscillating receptors tended to produce weaker EOD rate decreases in response to the IPI stimuli than to single pulses (negative y-values in Figure 8B). In contrast, the species with spiking receptors tended to produce stronger EOD rate decreases for IPI stimuli than for single EODs (positive y-values in Figure 9B). Accordingly, there was a significant effect of species in a repeated-measures ANOVA (F(1) = 18, p = 0.0012), but no interaction effect between species and IPI (F(6,12) = 1.0, p = 0.43). The potential social implications of variation in the strength of EOD rate increases and decreases across this range of IPIs are unknown.
These results lend support to our hypothesis that the frequency tuning of oscillating receptors may mediate detection of submillisecond IPIs that are only present in the communication signals produced by a large group of individuals. These very short IPIs elicit stronger responses in oscillating receptors and greater EOD rate increases in species with oscillatory receptors. Since spiking receptors cannot encode these submillisecond IPIs, we propose that oscillations are an adapation for detecting large groups of conspecifics.
Here, we describe a novel mechanism for peripheral sensory coding in which sensory receptors with spontaneous oscillatory activity respond to stimuli with phase resets that result in transient synchrony across the population. The degree of synchrony provides information about pulse timing and polarity, but it does not reflect the precise timing cues within the pulse waveform, consistent with the inability of these fish to behaviorally detect EOD waveform variation (Carlson et al., 2011). Further, oscillating receptors are most sensitive to submillisecond IPIs that match their oscillation period (0.47–0.99 ms). This sensitivity is remarkable for two reasons. First, these submillisecond IPIs are too short for spiking receptors to reliably encode. Second, these IPIs are much shorter than those produced by individual fish, suggesting increased sensitivity to communication signals produced by groups of individuals. Indeed, playback experiments demonstrated that a species with oscillating receptors responded strongly to submillisecond IPIs within the range of enhanced receptor sensitivity. Thus, this novel peripheral coding mechanism may represent an adaptation for detecting communication signals from large groups of fish.
Oscillatory electrical activity is widespread in neural networks (Buzsaki and Draguhn, 2004). Many studies have linked phase resets in ongoing cortical oscillations to perceptual performance, suggesting an important role for phase resets in sensory processing (Thorne and Debener, 2014). However, whether these phase resets encode specific information about a sensory stimulus or instead serve a modulatory function remains to be determined (Lakatos et al., 2009). Futhermore, all phase resets previously described occur in central circuits and involve oscillations of much lower frequencies (<100 Hz) than those (1–3 kHz) in the sensory receptors we describe (Harder, 1968b; Thut et al., 2012; Canavier, 2015). Using in vivo extracellular recordings from single receptors, we demonstrate that phase resets in the periphery lead to transient synchrony among receptors, enabling detection of and entrainment to very fast temporal patterns in communication signals. Sensitivity to temporal patterns has been shown to involve network interactions in a wide range of central circuits (Klug et al., 2012; Buonomano, 2014). Here, we show that electrical oscillations in sensory receptors can act as peripheral filters for temporal patterns.
Spiking receptors are most sensitive to stimulus frequencies near those in conspecific EODs (Hopkins, 1981; Bass and Hopkins, 1984; Lyons-Warren et al., 2012). Peripheral sensory receptors whose maximal sensitivity corresponds to a principal feature present in the signal they detect are considered ‘matched filters’ (Wehner, 1987). Spiking receptors thus appear to be matched filters for conspecific EODs. Moreover, the precisely timed spikes in these receptors enable discrimination of waveform variation (Hopkins and Bass, 1981; Carlson et al., 2011), which would facilitate signaler identification. In fact, studies on species with spiking receptors have revealed behavioral sensitivity to individual differences in conspecific EODs (Graff and Kramer, 1992; Hanika and Kramer, 2005). Collectively, these results support the hypothesis that spiking receptors are specialized for detecting communication signals produced by individual fish and encoding the information necessary to identify the sender.
In contrast, oscillating receptors exhibit matched filters not for EOD waveforms, but for IPI patterns in group signals. A receptor's oscillation frequency is predictive of its frequency tuning. However, oscillating receptors are tuned to much lower frequencies than the peak power frequencies of conspecific EODs. Furthermore, these oscillation frequencies confer enhanced sensitivity to IPIs that can only be produced by groups of fish. Therefore, whereas spiking receptors are specialized for detecting signals from individuals, oscillating receptors appear specialized for detecting signals from a large group. Importantly, the submillisecond IPIs to which oscillating receptors are most sensitive do not necessarily reflect concerted signaling amongst the group. It will be interesting to determine whether fish modulate their IPI patterns in groups and how this might affect the IPI distribution of collective signals.
Differences in receptor physiology and tuning may relate to differences in social behavior. Species in the lineage with oscillatory receptors have been reported to school as adults and prefer open water (Nichols and Griscom, 1917; Hopkins, 1980, 1981; Chapman et al., 1996; Lavoue et al., 2004; Lavoue, 2012), unlike other species, which have generally been described as solitary and territorial, and prefer areas with dense vegetation and detritus (Nichols and Griscom, 1917; Hopkins, 1980, 1981; Hopkins and Bass, 1981; Chapman et al., 1996; Friedman and Hopkins, 1996; Lavoue, 2012). In single-species lab tanks, Petrocephalus tenuicauda and Petrocephalus soudanensis form open-water shoals and schools, respectively, while Brevimyrus niger, Pollimyrus adspersus, and Brienomyrus brachyistius are solitary and seek shelter (unpub. obs.). Thus, the increased sensitivity of peripheral receptors for communication signals produced by groups of conspecifics may mediate the detection and localization of shoals or schools in fish's native habitats. However, P. microphthalmus presents an important exception. These fish, with spiking receptors and associated sensitivity to EOD waveform variation, form schools both in laboratory and natural settings (Lavoue et al., 2004). Field studies will be necessary to more fully understand how behavioral and ecological differences between species may have selected for different peripheral coding strategies and the perceptual abilities they confer.
Previous work has demonstrated that electrical oscillations serve as a frequency-tuning mechanism in electroreceptors and non-mammalian hair cells (Bennett, 1965, 1967; Hopkins, 1976; Viancour, 1979; Watson and Bastian, 1979; Fettiplace and Fuchs, 1999; Kawasaki, 2001, 2005). Indeed, damped oscillations in spike probability following an electrical stimulus in mormyrid spiking receptors may reflect subthreshold oscillations related to frequency tuning (Bennett, 1965, 1967; Roth and Szabo, 1972). Spontaneous, noisy oscillations in paddlefish ampullary electroreceptors are hypothesized to contribute to prey detection (Neiman and Russell, 2001, 2011). In frog saccular hair cells, differences in spontaneous spiking vs oscillatory electrical activity are linked to differences in the density of three types of potassium channels (Rutherford and Roberts, 2009). Whether similar mechanisms contribute to high-frequency oscillations in some mormyrid species and spikes in other species remains to be determined. Furthermore, whether the oscillations that can be recorded extracellularly from rosette receptors represent collective spiking activity or non-spiking oscillatory potentials in sensory cells at the base of the receptor pore is also unknown. Importantly, the oscillations in mormyrid rosette receptors are distinct from oscillations in hair cells and other electroreceptors due to their larger amplitudes and higher frequencies, as well as the fact that they respond to stimuli with phase resets. To our knowledge, the phase resets reported here are the first observed in any oscillating sensory receptor.
These stimulus-evoked phase resets result in transient synchrony across oscillating receptors. Since receptors have different intrinsic oscillation frequencies, this synchrony will rapidly decrease. Therefore, pooling the responses of multiple receptors could mediate synchrony detection and allow for the disambiguation of stimulus-evoked and spontaneous oscillations. Whether and where such integration may occur remains to be determined. A single afferent fiber could contact multiple receptors, or multiple afferents may converge onto single postsynaptic neurons in the hindbrain nucleus of the electrosensory lateral line lobe, as reported previously in a species with spiking receptors (Bell and Grant, 1989).
The entirety of our knowledge of electric communication signal coding in mormyrids had been limited to species with broadly distributed receptors (see Baker et al., 2013 for review), which fire spikes in all species that have been studied. These species' behavioral sensitivity to EOD waveform variation is due in part to the precise encoding of waveform timing cues by spiking receptors. Spike-timing differences between receptors on opposite sides of the body are compared in the midbrain anterior exterolateral nucleus (ELa), where excitation and inhibition from different receptive fields establish single-neuron selectivity for stimulus pulse durations (Friedman and Hopkins, 1998; Lyons-Warren et al., 2013). These duration-selective neurons project to the posterior exterolateral nucleus (ELp), where single-neuron selectivity for IPIs arises (Carlson, 2009). Notably, species with rosette receptors have a smaller exterolateral nucleus (EL) that is not subdivided into anterior and posterior regions (Carlson et al., 2011). In all three species that have been studied, rosette receptors produce high-frequency oscillations (this study, Harder, 1968b). How electric communication signals are processed centrally in the EL of these species remains unknown.
Central processing of oscillatory receptor inputs could contribute to distinguishing self-generated stimuli from external stimuli while maximizing sensitivity to external stimuli. In the absence of an electric stimulus, the spontaneous oscillations across receptors are asynchronized. When the fish emits its own EOD, receptors in all rosettes will experience stimuli of identical polarity based on the direction of current flow through the receptors (Hopkins, 1986). This means that oscillations in all receptors will reset to the same phase in synchrony. In this case, subtracting the oscillatory responses of receptors on opposite sides of the body would result in cancellation. In contrast, in the presence of an external electric stimulus, receptors at different locations on the fish's body will experience opposite-polarity stimuli and will reset to opposite phases. Subtraction of these opposite-phase responses would increase oscillation amplitudes relative to single-receptor oscillations, thus potentially increasing sensitivity to external signals.
The electrosensory system could implement a subtraction mechanism if oscillatory receptors on one side of the body mediate excitation, and receptors on the opposite side mediate inhibition onto central neurons. In response to self-generated signals, excitation would arrive coincidentally with inhibition, thus canceling responses. This proposed mechanism would also provide the information necessary to locate external signal sources. Stimuli coming from one direction would elicit maximal excitation from one side of the body and minimal inhibition from the other side, whereas stimuli from the opposite direction would elicit minimal excitation and maximal inhibition. The directionality would presumably be reversed in the contralateral hemisphere, leading to a reversed directional preference. Given the excitatory–inhibitory interactions found in the ELa of species with broadly distributed receptors (Friedman and Hopkins, 1998; Lyons-Warren et al., 2013), similar interactions could occur in a homologous circuit within the midbrain EL.
The mormyrid family contains two subfamilies called Mormyrinae and Petrocephalinae (Sullivan et al., 2000). Most mormyrine species have broadly distributed receptors, ELa/ELp, and behavioral sensitivity to EOD waveform variation (Carlson et al., 2011). In contrast, most petrocephaline species have receptors organized into rosettes as well as an EL midbrain region, and they are not sensitive to EOD waveform variation (Carlson et al., 2011). One interesting exception is P. microphthalmus, which has broadly distributed receptors and ELa/ELp, and it can also detect EOD waveform variation (Carlson et al., 2011). Therefore, P. microphthalmus represents a striking case of parallel evolution in which a perceptual ability arose twice independently, once in Mormyrinae and once in P. microphthalmus (Carlson et al., 2011). Here, we show that the broadly distributed receptors of P. microphthalmus fire spikes and display similar physiological properties to the spiking receptors of all mormyrine species studied (this study, Bennett, 1965; Harder, 1968b; Hopkins, 1981; Arnegard et al., 2006). Thus, we demonstrate correlated evolutionary changes in peripheral sensory physiology, neuroanatomy, and perception.
In addition to describing the sensory basis for a major perceptual difference among species, our results provide the first illustration of information coding by modulations of ongoing oscillations at the periphery. These results lay the groundwork for future investigations into (1) the cellular mechanisms responsible for generating continuous high-frequency oscillations and phase resets in sensory receptors, (2) cellular and network mechanisms for central processing of oscillatory modulations, and (3) differences in ecology and/or social behavior among species with different peripheral coding strategies.
15 B. niger (standard length [SL] = 5.9–8.6 cm), 17 P. adspersus (SL = 6.2–8.6 cm), 20 P. microphthalmus (SL = 6.3–8.0 cm), 20 P. tenuicauda (SL = 5.5–9.0 cm), and 5 P. soudanensis (SL = 8.0–10.0 cm) contributed data to this study. We used fish of both sexes. All fish were acquired through the aquarium trade except for P. microphthalmus, which were obtained from Lac Zilé, Gabon. EODs and receptor frequency tuning data from B. brachyistius came from a previously published study (Lyons-Warren et al., 2012). In the laboratory, we housed fish in conspecific-only group tanks with 12 hr:12 hr light:dark cycle, water conductivity of 200–400 μS/cm, and temperature of 25–29°C. We fed the fish live black worms four times per week. Species of the genus Petrocephalus are extremely difficult to obtain. Therefore, to minimize the number of fish used in our physiological recordings, we collected just enough data to detect a robust pattern, which in some cases happened at a low sample size (e.g., n = 3 in Figures 5, 7). All procedures were in accordance with the guidelines established by the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee at Washington University in St. Louis.
Extracellular recordings from single receptors were obtained using previously reported methods (Bennett, 1965; Hopkins and Bass, 1981; Lyons-Warren et al., 2012). Briefly, we anesthetized fish in 300 mg/l tricaine methanesulfonate (MS-222, Sigma–Aldrich, St. Louis, MO) and then paralyzed and electrically silenced fish with 20–80 μl of 0.1 mg/ml gallamine triethiodide (Flaxedil, Sigma–Aldrich). We then placed the fish in a 20 × 12.5 × 45 cm chamber filled with freshwater and positioned the fish on a plastic platform with lateral supports. We respirated the fish with freshwater through a pipette tip in the fish's mouth while monitoring the fish's electromotor output using a pair of electrodes placed next to the fish's tail (Carlson, 2002). Flaxedil silences the EOD, but external electrodes can record fictive EOD motor commands from spinal electromotor neurons. After the fish had completely recovered from anesthesia, as indicated by the return of fictive EODs, we began the recording session. After receptor recordings, fish were allowed to recover completely from paralysis before being returned to their home tank.
We used electrodes made from borosilicate capillary glass (o.d. = 1 mm, i.d. = 0.5 mm; A-M Systems, Everett, WA, USA). We bent the last ∼1 cm of the electrode to a 30° angle and polished the tip using the flame from a Bunsen burner. We filled the electrode with tank water and placed it in an electrode holder with a Ag-AgCl wire connected to the headstage of the amplifier. We placed the electrode next to, but not quite touching, individual receptors. The fish remained completely under water for the entire recording session. Extracellular activity was referenced to ground, amplified 10 times (Neuroprobe Model 1600, A-M Systems), digitized at a rate of 97.7 kHz (RP2.1, Tucker–Davis Technologies, Alachua, FL, USA), and saved using custom software in Matlab 7.0 (MathWorks, Natick, MA, USA). Spiking responses and some oscillating responses were also low-pass filtered (cut-off frequency = 100 kHz) during recording.
We obtained simultaneous recordings from pairs of oscillating receptors using the same glass electrodes as single-receptor recordings. Extracellular activity was referenced to ground, amplified 100 times, band-pass filtered (0.3–20 kHz) (Model 1800, A-M Systems), digitized at a rate of 97.7 kHz (RP2.1, Tucker–Davis Technologies), and saved in Matlab. We selected one spontaneously oscillating receptor in each rosette, for a total of 14 possible pairs across six rosettes. In one P. tenuicauda, we collected five 1-s simultaneous recordings of spontaneous activity in 13 pairs of rosettes. We were unable to see the left and right nackenrosettes at the same time under our microscope in this fish, so we could not obtain simultaneous recordings from this pair of rosettes. In another P. tenuicauda, we obtained simultaneous recordings from six pairs of rosettes.
To assess whether receptors within a rosette oscillate at the same frequency, we recorded local field potentials with a differential electrode consisting of a pair of Ag-AgCl wires separated by 5 mm. This method allowed us to record the collective activity of several receptors located closer to one another than separate glass electrodes would allow. Each wire had a diameter of 0.635 mm and uninsulated tip length of 2 mm. We oriented the electrode pair perpendicular to the skin, with the recording electrode next to the skin and reference electrode farther away. Electrical activity was amplified 100 times and band-pass filtered (0.3–5 kHz) (Model 1800, A-M Systems), digitized at a rate of 97.7 kHz (RP2.1, Tucker–Davis Technologies), and saved using custom software in Matlab 7.0.
All stimuli were generated in Matlab, digital-to-analog converted at a rate of 195.31 kHz (RP2.1, Tucker–Davis Technologies), and attenuated (PA5, Tucker–Davis Technologies) before delivery to the current input of the amplifier (Neuroprobe Model 1600, A-M Systems). The amplifier then delivered constant-current stimuli through the monopolar recording electrode referenced to ground (Lyons-Warren et al., 2012). We used a bridge balance to minimize the stimulus artifact.
To study how receptors encode electric pulse waveform, we recorded responses to 10 or 25 repetitions of monopolar square pulses of durations ranging from 0.01 to 5 ms. We used both positive and negative polarities at an intensity within the behaviorally relevant range that reliably elicited responses from each receptor (10–18 nA in P. adspersus, 18 nA in B. niger, 32 nA in P. microphthalmus, and 45–100 nA in P. tenuicauda).
We also delivered previously recorded conspecific EOD waveforms. For each receptor, we randomly selected an EOD stimulus from a library of 10 conspecific EOD waveforms. We presented 10 repetitions of both normal- (head-positive) and reversed-polarity waveforms at intensities ranging from 6 to 312 nA.
To measure frequency tuning in spiking receptors, we presented 15 repetitions of 90-ms duration sinusoidal stimuli of frequencies ranging from 0.1 to 50 kHz with 5 ms cosine-squared on- and off-ramps. Because we could not separate a continuously oscillating response from a continuously oscillating stimulus artifact, we had to use different stimuli to measure frequency tuning in oscillating receptors. We presented 10 repetitions of single-cycle bipolar sine wave stimuli of frequencies ranging from 0.2 to 84 kHz to measure frequency tuning of oscillating receptors. We also delivered single-cycle bipolar sine wave stimuli at durations equal to multiples (¼, ½, 1, 2, 4) of each receptor's spontaneous oscillation period. We used both positive (peak leading trough) and negative (trough leading peak) polarities at three stimulus intensities (10, 32, and 100 nA) in P. tenuicauda. In P. soudanensis, we used positive- and negative-polarity stimuli at 32 nA.
To study how spiking and oscillating receptors encode IPIs in communication signals, we presented 10 repetitions of a pair of monopolar square pulses, with each pulse 0.2 ms in duration. We used IPIs ranging from 0.3 to 13 ms. For oscillatory receptors, we also used IPIs equal to multiples (¼, ½, 1, 2, 4, 8) of each oscillating receptor's intrinsic oscillation period. We used both positive and negative polarities at an intensity that reliably elicited responses in each receptor (6–10 nA in P. adspersus, 7–10 nA in B. niger, 18 nA in P. microphthalmus, and 10–100 nA in P. tenuicauda).
To quantify the responses of spiking receptors, we detected spikes by finding the peak voltage that crossed a manually set threshold specific to each receptor. We measured interspike intervals as the difference in time between consecutive spikes. To measure the responses of oscillating receptors, we first median filtered the recordings using a filter width of 0.1 ms. Next, we detected the first seven oscillatory peaks after stimulus offset on each stimulus sweep, which allowed us to measure the first six poststimulus oscillation periods. Imperfections in artifact balancing had no impact on the detection and measurement of spiking responses. However, sometimes it was unclear whether the first poststimulus oscillatory peak was contaminated by artifact. To assess whether the stimulus artifact affected the first poststimulus oscillation peak, we compared the first poststimulus period to the average of the following five poststimulus periods. If the first period was less than 85% or more than 115% of the average of the next five periods, we concluded that the artifact interfered with the first poststimulus peak and instead used the second poststimulus peak for all measurements.
The time between the first and second poststimulus peaks represented the poststimulus oscillation period. We measured the oscillation amplitude by first averaging the voltage values of the first two poststimulus oscillatory peaks, and then subtracting the voltage value at the intervening oscillatory trough. Next, we calculated the angle (φ, in radians) of the oscillatory phase reset as
where lat is the latency of the first poststimulus peak (time of peak minus time of stimulus offset) and p is the first poststimulus period. We then used these values across repetitions to calculate the vector strength (r), a measure of phase-locking, according to
where n is the number of stimulus repetitions. The vector strength is a normalized measure of phase-locking that equals 1 when the constituent angles are perfectly in phase with one another across stimulus repetitions, and 0 when the constituent angles are completely random across stimulus repetitions.
We recorded spontaneous activity of each receptor five times for 1 s each. For a given spiking receptor, we measured the interspike intervals on each repetition and then averaged across recordings. For oscillating receptors, we averaged the fast Fourier transform (FFT) of the oscillatory activity across five repetitions, and then used the peak of the averaged FFT as the receptor's spontaneous oscillation frequency. We used the inverse of the spontaneous oscillation frequency as the spontaneous oscillation period. We measured the spontaneous oscillation amplitude of each receptor by subtracting the voltage at each oscillatory trough from the voltage of the preceding oscillatory peak, and then averaging these amplitude values across the spontaneous recordings. If spontaneous activity was recorded more than once from the same receptor, we averaged measurements across all recordings.
To determine whether the oscillatory activity of different receptors was correlated, we obtained the instantaneous phase of oscillations of two receptors recorded simultaneously using a Hilbert transform in Matlab. We then computed the circular correlation coefficient between the phases of two simultaneous recordings (Berens, 2009). For each pair of receptors, we generated two surrogate data sets of non-simultaneous recordings. The first data set consisted of the first 1-s recording from one receptor and the fifth 1-s recording from the other receptor. The second data set consisted of the fifth 1-s recording from one receptor and the first 1-s recording from the other receptor.
We measured frequency tuning in spiking receptors following previously described methods (Lyons-Warren et al., 2012). We defined a response as at least one more spike/sweep during the 90-ms stimulus period than during the 90-ms prestimulus period. We determined a receptor's threshold by finding the lowest intensity at which the receptor responded to a given frequency. The frequency with the lowest threshold was defined as the receptor's best frequency. We generated normalized tuning curves by setting threshold intensities as dB relative to the threshold intensity at the best frequency. For oscillating receptors, we generated tuning curves by plotting vector strength vs stimulus frequency. We defined the best frequency as the frequency with the highest vector strength. In three of eight oscillating receptors, the best frequencies in response to positive- and negative-polarity stimuli differed slightly. In these cases, we determined the receptor's best frequency by averaging the best frequencies to each stimulus polarity.
To study how spiking receptors encode electric pulse waveform, we averaged the time of the first spike that occurred after stimulus onset (positive-polarity stimuli) or stimulus offset (negative-polarity stimuli). The difference in average spike times elicited by stimuli of opposite polarities was defined as the spike-timing difference. Since receptors on opposite sides of the body experience stimuli of opposite polarities, this procedure is equivalent to measuring the difference in spike times between receptors at different locations on the body evoked by the same stimulus (Hopkins, 1981). For a comparable measurement in oscillatory responses, we subtracted the average time of the first peak that occurred after the offset of the negative-polarity pulse from the average time of the first peak that occurred after the onset of the positive-polarity pulse for pulse durations longer than 0.5 ms. For shorter stimulus durations (≤0.5 ms), we measured the difference in the times of the first oscillatory peaks that occurred after stimulus offset for each stimulus polarity. This procedure is equivalent to measuring the difference in oscillatory peak times between receptors on opposite sides of the body evoked by the same stimulus. To quantify the degree of oscillation amplitude enhancement, we first measured all prestimulus oscillation amplitudes on each recording trace, and then averaged across all repetitions of the same stimulus. Next, we measured the poststimulus oscillation amplitude on each recording trace and averaged across all repetitions of the same stimulus. Lastly, we divided the mean poststimulus oscillation amplitude by the mean prestimulus oscillation amplitude for each pulse duration.
Since the peak power frequency of a single-cycle bipolar sine wave is slightly lower than the inverse of the wave's duration, we used Welch's power spectral density in Matlab to compute the power spectrum of each single-cycle sine wave. We took the peak of the power spectrum as the frequency of each stimulus.
We measured the difference in the phases of oscillatory resets evoked by opposite-polarity stimuli by first averaging the recorded potential across stimulus repetitions, and then measuring the first poststimulus period and the latency of the first poststimulus peak. We used these values to calculate the difference in the phase (Δφ) of oscillatory reset between responses to opposite-polarity pulses according to
where latN is the latency of the first peak, and pN is the first period, respectively, evoked by the negative-polarity stimulus, and latP is the latency of the first peak, and pP is the first period, respectively, evoked by the positive-polarity stimulus.
To measure spiking responses to paired pulses, we measured the interspike intervals between the spikes elicited by the onset (positive-polarity stimuli) or offset (negative-polarity stimuli) of each pulse. If no spikes occurred in response to the second pulse, no interspike intervals were recorded. For clarity, stimulus artifacts were removed from the recording traces shown in Figure 5A,B by drawing a straight line from the times of stimulus onset or offset plus 0.03 ms. In oscillating receptors, the stimulus artfact at the shortest IPIs tested obscured responses to the first pulse in the pair. To circumvent this issue, we first averaged oscillatory responses across stimulus presentations. Next, we measured the time interval between the first oscillatory peak that followed a single pulse and the first oscillatory peak that followed the second pulse in the pair. This method is equivalent to measuring the time interval between the first peaks following each pulse in the pair. We also measured the vector strength and poststimulus oscillation amplitude following the second pulse in the pair. We normalized mean poststimulus oscillation amplitudes by dividing by the mean poststimulus oscillation amplitude following a single pulse of 0.2 ms with the same polarity.
EODs were amplified 20–100 times, band-pass filtered (1 Hz–50 kHz) (BMA-200, Odmore, PA, USA), digitized at a rate of 195 kHz (RP2.1, Tucker Davis Technologies), and saved using custom software in Matlab 7.0. We measured total EOD durations using previously defined criteria (Carlson et al., 2000). EOD power spectra were computed using Welch's power spectral density estimate in Matlab.
To measure the IPIs generated by single fish and groups of fish, we recorded 20 min of electrical activity from a species with spiking receptors (P. microphthalmus) and a congeneric species with oscillating receptors (P. tenuicauda). For single-fish recordings, fish were removed from their home tank and placed into a 15 × 17 × 31 cm plastic chamber with a single recording electrode in which the positive and negative terminals were separated by 20 cm. Fish were allowed to acclimate for at least 10 min before the recordings. For group recordings, we placed four vertically oriented electrodes suspended with the reference terminal up (16.5 cm from the top of the tank) in the home tank (61 × 76 × 61 cm) of each species. Each electrode was located one-third of the distance between tank walls (i.e., at the vertices of nine equally sized rectangles). We placed the electrodes in the tank and allowed fish to acclimate to them overnight before starting the recordings. We amplified electrical activity 100 times before band-pass filtering (0.1 Hz–20 kHz) (Model 1700, A-M Systems) and digitizing at 97.7 kHz (RX8, Tucker–Davis Technologies). We detected EODs as points where the rectified recorded potential exceeded a threshold of 0.6 mV. Data were saved using custom Matlab software. For the histograms shown in Figure 8B,C,E,F, we used the recording from just one of the four electrodes. Single-fish and group tank recordings took place between 1700 and 1900.
To test whether species with different receptor physiologies exhibited different behavioral responses to IPIs, we recorded fish's EOD output in response to a train of 10 conspecific EODs at a constant IPI (0.3–100 ms) using previously described methods (Carlson et al., 2011). Briefly, we removed one fish from its group tank and placed it into a 10 gallon tank with a cylindrical plastic chamber (5 cm diameter × 13.2 cm) in the middle of the tank. If the fish was not inside the chamber at the end of a 30-min acclimation period, we guided the fish into the chamber with nets. Netted caps were then placed over each end of the chamber to keep the fish inside during the experiment. Fish were then allowed an additional 5-min acclimation period before starting the experiment. A pair of electrodes oriented horizontally along the inside walls of the chamber delivered the stimulus, and a pair of electrodes oriented vertically at either end of the chamber recorded the fish's EOD output. For each fish, we randomly selected a conspecific EOD waveform stimulus from a library. To generate the IPI train stimuli, we first defined the start and end of the EOD as the first and last points, respectively, where the absolute value of the waveform exceeded 0.5% of the maximum peak-to-peak amplitude. We then concatenated 10 identical EODs together, adding zero padding as necessary in between EODs to achieve the desired IPIs. If the duration of the EOD was longer than the IPI, we truncated the EOD at the IPI. We presented only normal-polarity (head-positive) stimuli. All stimuli were generated in Matlab, digital-to-analog converted at a rate of 195.31 kHz (RX8, Tucker–Davis Technologies), and attenuated (PA5, Tucker–Davis Technologies) before delivery to the analog stimulus isolator (Model 2200, A-M Systems).
To record fish's EOD output, we amplified electrical activity 100 times before band-pass filtering (0.1 Hz–20 kHz) (Model 1700, A-M Systems) and digitizing at 97.7 kHz (RX8, Tucker–Davis Technologies). We collected a fish's responses to 20 repetitions of each IPI train and to a single EOD stimulus, with 1 min between trials to reduce habituation. We randomized stimulus order. To quantify responses, we recorded the time of each EOD produced by the fish, and then computed the spike-density function (SDF) by convolving each EOD time with a Gaussian of 200-ms width (Carlson and Hopkins, 2004). We then averaged the SDF across stimulus repetitions. Next, we measured the fish's baseline response by averaging the resulting EOD rate over a window starting 0.2 s after the start of the 5-s prestimulus period and ending 0.2 s before stimulus onset. Because mormyrids can respond to electric stimuli with increases or decreases in their EOD rate (Moller et al., 1989; Post and von der Emde, 1999; Carlson et al., 2011), we measured the maximum and minimum of the SDF that occurred between 0.2 s before stimulus onset and 1.2 s following stimulus offset. We subtracted the baseline discharge rate from these two values to get the maximum increase in EOD rate and the maximum decrease in EOD rate in response to the stimulus. Because response patterns varied across individuals (Moller et al., 1989), we used a normalized measure of response strength. To compare responses across individuals, we subtracted the single-pulse response from the response to the IPI stimulus, and then we divided by the sum of the single-pulse and IPI response. We performed the same analysis on EOD rate increases and decreases.
We used circular statistics to measure mean angles ± S.E.M. of phase modulations of oscillatory receptor responses (Batschelet, 1981). We used the parametric second-order Hotelling test for paired circular data to compare oscillatory phase modulations elicited by normal- and reversed-polarity EODs (Zar, 1999). We used repeated-measures ANOVA in Statistica 6.1 (StatSoft, Tulsa, OK, USA) to compare behavioral responses to IPI stimuli across two species. We used Wilcoxon's matched-pair test in Statistica to compare circular correlation coefficients of simultaneous and non-simultaneous recordings. We used Spearman's rank test in Statistica 6.1 to compare spontaneous oscillation amplitudes and frequencies, as well as spontaneous and stimulus-evoked oscillation amplitudes in response to square pulses in oscillatory receptors. For all tests, significance level was defined as α = 0.05.
All of the data and all of the custom-written Matlab software used for data analysis are available at the Dryad Digital Repository (Baker et al., 2015).
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The authors present a very interesting electrophysiological and behavioral analysis of oscillatory electroreceptors in a group of mormyrid fishes and compare these receptors to spiking receptors found in other mormyrid species. The oscillating receptors encode electric pulses into phase resets that preserve the information necessary to detect and locate signals. Interestingly they are tuned to interpulse interval (IPI) rates that are well above the frequency range of pulses used by individual fish. These short IPIs are found only in recordings from groups of conspecifics and lead the authors to hypothesize that these receptor properties evolved as a means of detecting groups of conspecifics. The authors then test fish with oscillatory receptors and show that unlike those with spiking receptors they respond behaviorally (changes in electric organ discharge rate) to stimuli with the very short IPIs to which their receptors are optimally tuned.
The paper should be of wide interest in the neuroethology community and also to other sensory physiologists and neuroscientists interested in the role of oscillations in brain networks (especially sensory networks) because it serves as an example of how phase resets can carry critical information.
1) The expert reviewers were concerned about how the reset coding proposed might work. One expert reviewer wrote “Figure 5 outlines the problem of coding pairs of input pulses. The spiking receptors respond to each input with a spike until the interval (IPI) is too small (they are refractory). In this case, it is clear (because of the nonlinearity of the spiking mechanism) that the timing of each stimulus pulse is represented in the receptor response (a spike). On the other hand, the oscillating receptors respond to the stimulus pulse with strong phase resets – by this I mean that the “new phase” after the pulse is consistent across trials and does not depend on the timing of the stimulus (Figure 3). The authors argue that this reliable, stimulus-evoked phase reset can signal the timing of multiple pulses (IPI). This makes sense, as the cycle reset is somewhat analogous to a spike in the other receptors. However, it is not clear how downstream neurons would distinguish the reset cycles from the others. For example, when the IPI is longer than the cycle period (Figure 5E, lower panel), what distinguishes the second cycle (i.e. second peak) following the first stimulus, from the cycle immediately following the second stimulus? Similarly, when the IPI is shorter than the cycle period (Figure 5F lower panel), the time between the cycles following each pulse is longer than the intrinsic cycle period. (Note that the receptor would not have access to the single pulse response for comparison, as in the analysis). In such cases, how will ambiguities between stimulus-perturbed cycles and free-running cycles be dealt with? Perhaps I have missed a key assumption but in the very least, a more detailed explanation of how this coding/decoding works would be appreciated.”
In discussions among the reviewers and the Reviewing editor this was further amplified: “The problem with IPI coding with resets is not trivial. The strong reset does function to synchronize receptors, and this could code for detection of a short IPI (in the same way that electroreceptors detect brief communication signals in wave-type fish, Benda et al., Neuron 2006). However, according to data shown in the present study, synchrony is not so brief, but lasts many cycles of the receptor oscillation. When many fish are around, additional short IPI will likely arrive during this time (while receptors are still synchronized) so how would change in synchrony allow detection of these? In this case, the phase reset itself could carry information but would require a reference phase for comparison.”
The authors should clarify these points in their Discussion.
2) The analyses performed to claim that oscillatory receptors are not correlated (Figures 2D, F) are not substantive enough. The expert reviewer wrote: “For the cross-correlogram (Figure 2D), the authors should use surrogate datasets to test whether the data are significant or not. There is also concern about the reports in the text saying that cross-correlation coefficients could reach values as high as 0.8. How can such high values not be significantly different from zero? However, the CCG is probably not the best measure to use here. The authors should also test for phase coupling between oscillators. This can be done by extracting the time-dependent phase of each oscillator (e.g. via Hilbert transform), plotting their joint probability distribution, and comparing it to the product of the individual distributions (again use surrogate datasets to test for significance). I am also unsure as to why the authors use different recording/analysis techniques to test whether oscillators within a rosette are correlated or not. Please be consistent and thorough in justifying your choice of measures for determining whether oscillatory receptors are correlated or not as this will very much determine how results obtained from single receptors will generalize to the population level.”
3) The same referee stressed: “In Figure 3, the authors analyze the phase reset of oscillatory receptors caused by pulses of opposite polarity of different widths. While their results show that the phase resets are always out of phase with each other independent of width, this analysis alone is not enough to prove that no information about pulse width is transmitted by these receptors. For example, have the authors looked at the timecourse of the decay in the damped oscillation following the pulse? Strong statements saying there is no information need to be properly substantiated. The authors could also use information theory. The sentence stating that the amplitude enhancement depends on pulse duration would suggest a coding mechanism. This is really critical for the authors' claim that the fact that species with oscillatory receptors cannot distinguish between pulses with different characteristics is caused by the fact that the peripheral receptors do not transmit this information in the first place.”
4) The data of Figure 8 cause some concern. Behavioral/EOD responses – both EOD rate increases and decreases were observed and described in the panels A and B. Did a single individual exhibit both types of responses? Did some stimuli result in no changes? What was the relative frequency of each type of response over individuals and across species? What conditions or stimuli lead to EOD rate increases and what lead to EOD rate decreases? Is this increase vs. decrease random or is there a pattern in individual fish? What is the behavioral significance of a rate decrease vs. a rate increase? What are the implications of the observation that tuning the effect is not obvious for the rate decreases? The description of this analysis should be clarified/justified e.g. why are the authors choosing a measure of “contrast”?
5) The writing is in general clear but still the paper could be improved for a general audience. The Abstract needs more of a general introduction to the problem and needs more context in terms of adaptation to the natural environment. The authors need to set up background/context for each of their experiments.
[Editors' note: further revisions were requested prior to acceptance, as described below.]
Thank you for resubmitting your work entitled “Peripheral sensory coding through oscillatory phase reset in weakly electric fish” for further consideration at eLife. Your revised article has been favorably evaluated by Eve Marder (Senior editor), a Reviewing editor, and two reviewers. The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:
Please address the concerns of reviewer #2.
The authors have done a very nice job addressing my concerns. In particular, the new figure explaining transient synchronization among oscillating receptors and the explanation provided for the behavioral analyses are very much appreciated. However, I feel that additional clarification of the “coding by resets” idea is still necessary.
The title of the paper and much of the discussion suggests that sensory information is coded in phase resets. To me, this means that the phase reset varies with some feature of a sensory stimulus, and that phase (or change in phase) information is passed on to downstream networks as a representation of this stimulus feature. It follows that the phase reset (new phase) should depend on the particular value of the sensory feature. Taken in this context, the data presented suggest that only stimulus polarity can be coded by the phase reset; the phase reset is invariant with the other tested stimulus features, so cannot provide any information about these features. That said, the data show very nicely that transient synchrony among receptors (now shown to be sufficiently brief) provides a means to code for sub-millisecond inter-stimulus intervals, and very importantly, that this is reflected in behavioral responses. So my concern is not with the quality of the data or its significance (that is very clear). I just don't see it as “sensory coding by phase reset” and presenting it as such leads to questions and confusion. Rather, information appears to be coded in the level of synchrony, which is changed by strong resets of the oscillating receptor to a single phase (an implicit requirement of a synchrony code). The work is then more appropriately set in the context of “synchrony coding”, and compared, for example, to the coding strategy described by Benda and colleagues in a related electrosensory system (as noted in the first review).https://doi.org/10.7554/eLife.08163.012
- Bruce A Carlson
- Christa A Baker
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank Tim Holy and Barani Raman for helpful discussions. This research was supported by the National Science Foundation (IOS-1255396 to BAC) and the National Institutes of Health (DC012452 to CAB).
Animal experimentation: All procedures were in strict accordance with the guidelines established by the National Institutes of Health and were approved by the Institutional Animal Care and Use Committee (Animal Welfare Assurance Number: #A-3381-01) at Washington University in St. Louis. The protocol was approved by the Animal Studies Committee at Washington University in St. Louis (Approval Number: 20130265). Every effort was made to minimize pain and stress. No surgical procedures were performed.
- Ronald L Calabrese, Reviewing Editor, Emory University, United States
- Received: April 17, 2015
- Accepted: July 8, 2015
- Version of Record published: August 4, 2015 (version 1)
© 2015, Baker et al
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.