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INTRODUCTION |
Most biologically important sounds contain frequency modulation (FM) that repeats over periods of time. This characteristic is seen in most sounds encountered by echolocating bats such as the big brown bat, Eptesicus fuscus. For bats, there are several possible sources of repetitive FM sounds. One source is the echoes from the vocal pulses Eptesicus emits while searching for prey. The echolocation sounds of Eptesicus consist entirely of FM signals (Simmons 1989
). The signals have varying slopes of FM rate, but all sweep from high to low frequencies over time. During the search phase of echolocation, the signals are relatively long (up to 20 ms) and have a shallow sweep; the most intense harmonic sweeps from ~29 to ~23 kHz. With this relatively narrowband signal, echoes reflected from fluttering targets would have amplitude modulation and FM (Schnitzler et al. 1983
). A second source of repetitive FM is found in the signals that the bat uses while it is approaching prey. During this stage of hunting, the bat emits brief FM sweeps at a high repetition rate. As the bat detects and approaches a target, the signals become progressively briefer and broader in bandwidth; the most intense harmonic sweeps from ~50 to ~23 kHz. At the same time, the interval between echolocation sounds becomes shorter, changing from ~20 to ~6 ms. These intervals correspond to modulation rates of 60-166 Hz. A third source of FM may be in prey-generated sounds heard by the bat. The sounds generated by the beating wings of beetles contain frequencies well within the hearing range of Eptesicus (Hamr and Bailey 1985
). These sounds are modulated in amplitude, possibly also in frequency, at rates corresponding to the beating of the insect's wings, ~50 Hz. Finally, the social communication repertoire of Eptesicus includes sounds that are modulated at ~30 Hz (Gould 1971
). If the social communication sounds of Eptesicus resemble those of other bats (Fenton 1985
), it is highly likely that they contain other modulation rates as well. In mustached bats, communication signals have been studied extensively. Some of these signals contain periodic FM; moreover, the modulation frequency is usually
200 Hz (Kanwal et al. 1994
). What all of these sounds have in common is a modulation rate that is low (<200 Hz) relative to the rates that can be followed by neurons at auditory centers below the inferior colliculus (IC).
These findings raise the following hypothesis. If the auditory system contains neurons tuned for repetitive FM that are biologically important, the tuning or specialization should be for low modulation rates.
A second hypothesis arises from the fact that tuning for some temporal parameters such as duration, delay between successive sounds, and sweep direction is known to take place at the IC (Casseday and Covey 1992
; Casseday et al. 1994
; Feng et al. 1978
; Fuzessery 1994
; Suga 1969
; see Casseday and Covey 1995
, 1996
for reviews). Recently, neurons in the IC of the mustached bat have been found to be tuned to the time delay between two tones that differ in frequency (Mittmann and Wenstrup 1995
; Yan and Suga 1996a
,b
). Therefore there may be mechanisms in the IC for tuning to other temporal parameters as well.
In addition to being tuned to a specific time-varying parameter, some IC neurons are also selective in the sense that they do not respond to pure tones or broadband noises. We will use this distinction between tuning and selection in this report.
There are two ways in which neurons could be adapted to respond to repetitive modulations in biologically important ranges. First, they could be tuned to specific modulation rates. Because tuning to relatively low modulation rates would be one form of tuning to biologically important sounds, we examined the responses of units in the IC to different modulation rates. A second and more extreme adaptation would be a selective response to repetitive modulations and little or no response to other stimuli. A convenient way to simulate naturally occurring repetitive modulations is to vary amplitude or frequency sinusoidally over time. Therefore, as a first step in answering the question of whether any IC neurons are specialized to respond to repetitive modulations, we compared responses to single pure tone or noise bursts, single FM sweeps, sinusoidally amplitude-modulated (SAM) tones, and sinusoidally frequency-modulated (SFM) tones. We discovered a class of neurons that responded best or exclusively to components contained within SFM signals. This report documents this selectivity and describes the tuning of these and other IC neurons to SFM rate and SFM depth.
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METHODS |
Surgical procedures
The animals used in this study were 35 big brown bats (E. fuscus) of both sexes, obtained from the attics of local houses. On the day before recording, the bat was anesthetized with a combination of Metofane (methoxyflurane) and a neuroleptanalgesic (fentanyl, 0.4 mg/ml + droperidol, 20 mg/ml; 0.125 ml/kg). The bat's head was held in a specially designed bite bar that was attached to manipulators that allowed the head to be rotated in three dimensions. Fine adjustments were made in the orientation of the skull so that it conformed to a standard stereotaxic position, and a metal post was attached to the skull with cyanoacrylate adhesive. The post was constructed so that the placement of the bat in the stereotaxic apparatus could be replicated precisely from one day's recording session to the next. Each bat was used in one to six recording sessions, on separate days. Each session was
6 h in duration. Between recording sessions, bats were housed in individual cages and given free access to food and water. The cages were located in a temperature- and humidity-controlled room.
Recording
Recording began on the day after implantation of the post.Before placement in the stereotaxic device, the bat was giventhe neuroleptanalgesic and lightly anesthetized with Metofane(methoxyflurane). A small opening, <1 mm diam, was made in the skull overlying the IC. Between recording sessions, the opening was covered with a coat of Vaseline. The animal was allowed to recover from the anesthesia for
30 min before recording began. This period was sufficient for recovery of neural responses. During recording, local anesthetic (Lidocaine) was applied to the scalp incision. During the recording sessions the bat was restrained in a foam-lined holder that was molded to the shape of the body so as to hold it firmly but comfortably. The holder was suspended in an elastic sling to damp movements. If the bat showed any signs of restlessness, the recording session was terminated.
Recordings were made with the use of glass micropipettes filled with 0.9% NaCl or 0.9% NaCl plus 5% horseradish peroxidase (HRP) or wheat germ agglutinin (WGA)-HRP. The electrodes had impedances of 15-40 M
and tip diameters of <1 µm. Electrodes were advanced in 1.0-µm steps with the use of a hydraulic microdrive. Data were only collected from units that had a signal-to-noise ratio of
3:1 and could be identified as cell bodies with reasonable certainty, in that they had a biphasic action potential waveform (Bishop et al. 1962
; Hubel 1960
). Recordings were amplified with a negative capacitance electrometer. Action potentials were discriminated with the use of a Tucker-Davis spike discriminator and digital oscilloscope. Spikes and the output of the spike discriminator were displayed on a multichannel oscilloscope and monitored on an audio amplifier and loudspeaker. Spike times were digitized with a time resolution of 1.0 µs. Data were visualized as dot rasters on-line and collected with the use of custom software developed in our laboratory. Twenty or 50 trials were presented for each stimulus condition. Multiple electrode penetrations were made in each animal. To mark recording sites, very small deposits (<50 µm diam) of HRP were made iontophoretically with the use of pulsed current, positive at the electrode tip, 0.7-0.9 µA, applied for 2-3.5 min.
After the final recording session, animals were administered a lethal dose of Nembutal (pentobarbital sodium) and perfused through the heart with phosphate-buffered saline (PBS) followed by a fixation solution of 4% glutaraldehyde in PBS or 8% Formalin plus 5% sucrose in PBS. After perfusion, the brain was removed and stored overnight in 30% sucrose in PBS. Sections were cut 40 µm thick on a freezing microtome. To visualize HRP or WGA-HRP, sections were reacted with tetramethylbenzidine according to the method of Mesulam (1982)
.
Auditory stimulation
In the early experiments, stimuli were delivered with analog devices (Wavetek function generators) presented in the free field at the center of the neuron's excitatory field (Grothe et al. 1996
). For later experiments, digitally generated stimuli were synthesized on a computer and delivered by 16-bit D/A converters (sampling rate 345 kHz) through filters and attenuators (all from Tucker-Davis Technologies). We tested the response to pure tones, noise of various bandwidths (1-100 kHz), linear FM sweeps of different depths (±0.1 to ±20 kHz), SAM tones (100% modulation depth), and SFM sounds. The SFM stimulus consisted of a 100-ms tone, the frequency of which was continuously modulated about a center frequency. The modulating frequency was a sine function, and for the computer-generated sounds, the modulating frequency always started at +90°. The amount by which the frequency changed about the center frequency during a period of modulation is referred to here as the "depth of modulation" or "SFM depth." The frequency of the modulating waveform is referred to here as the "modulation rate" or "SFM rate." Pure tones, noises, and single FM sweeps were presented one per trial, and the duration of these stimuli was varied. When a unit was found that responded well to SFM, we also tested it extensively with single FM sweeps to explore the possibility that the unit was responsive to some particular combination of FM direction and FM duration. For SAM or SFM sounds, the duration was 100 ms. The testing of SFM began by determining with audiovisual monitoring the unit's best response to SFM rate and best response to SFM depth. Then, with SFM depth set to give optimal response, peristimulus time histograms were collected while SFM rate was varied, usually in steps of 20 Hz. Next, with SFM rate fixed to give the best response, peristimulus time histograms were collected while SFM depth was varied. For some units we obtained rate-intensity functions at best SFM rate and best SFM depth.
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RESULTS |
We recorded from 116 neurons in the IC. Of these neurons, 89 (77%) responded to SFM, and 27 (23%) did not respond to SFM but did respond to other sounds. Of those that responded to SFM, 20 (17% of the total) responded only to SFM, and 10 (9% of the total) responded vigorously to SFM but also responded poorly to one or more other types of sound. For 59 neurons (51%) the response to SFM was about equal to the response to one or more other sounds. In the following we refer to the 30 cells that clearly responded best to SFM or exclusively to SFM as SFM-selective neurons. Figure 1 shows the distribution of these classes of units.

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| FIG. 1.
Proportion of units that responded to sinusoidal frequency modulation (SFM) only, that responded best to SFM, that responded to SFM and some other stimulus about equally well, and that did not respond to SFM. Crosshatched bars: SFM-selective neurons. Diagonally striped bars: other units.
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Location in IC
Figure 2 shows the location of 70 units in identified electrode tracks in the IC, pooled from four bats. The IC has been divided rostrocaudally into three approximately equal sections. All of the units shown responded to SFM, and 15 responded only to SFM. The latter neurons were located throughout the central nucleus of the IC and appeared to be intermingled with cells that responded to SFM but were not selective. Neurons that failed to respond to SFM (not shown) were distributed among the SFM-responding cells. The number of SFM-selective neurons is insufficient to answer the question of whether there is a topographical organization of best SFM rate or best SFM depth.

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| FIG. 2.
Reconstructions of location in the inferior colliculus (IC) of 15 SFM-selective units and 55 units responsive to SFM. The locations have been collapsed onto 3 frontal sections of approximately equal thickness. Top: anterior. Bottom: posterior. CG, central gray; D, dorsal; L, lateral; M, medial; SC, superior colliculus; V, ventral.
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Center frequencies
To examine how the frequencies to which SFM-selective neurons respond are related to echolocation frequencies, Fig. 3 shows a distribution of best center frequencies for SFM-selective neurons and for other neurons. We have used best center frequencies because for neurons that responded only to SFM it is obviously not possible to obtain a pure tone best frequency. The distributions show an expanded representation of frequencies between 20 and 40 kHz, corresponding to the lower end of the echolocation range. A few SFM-selective neurons had best center frequencies below the echolocation range, and even fewer had best center frequencies within the upper range of echolocation frequencies. For neurons that responded best to SFM, all but three center frequencies were <40 kHz; the three neurons with high best center frequencies responded best but not exclusively to SFM. The mean center frequency for all SFM-selective neurons was 31 kHz. Thus the center frequencies of most but not all SFM-selective neurons are within the range of the FM sweep that Eptesicus uses either during the search or during the approach and capture phases of hunting. In the search phase, the most intense harmonic sweeps from ~28 to ~23 kHz, and in the approach and capture phase it sweeps from ~50 to ~23 kHz (Simmons 1989
).

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| FIG. 3.
Distribution of best center frequencies. The largest proportion of center frequencies, including selective and nonselective neurons, was in the frequency range of 20-40 kHz. Most of the neurons were tuned to frequencies in the lower range of frequencies contained in the dominant harmonic of echolocation calls. The dominant harmonic in the pulse that Eptesicus uses during the searching phase of hunting contains frequencies in the range of 23-28 kHz; the dominant harmonic used during other stages of hunting extends up to 50 kHz, and other harmonics contain mainly frequencies of 50-80 kHz.
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Overview of SFM-selectivity
Figure 4 shows typical examples of discharge patterns of a neuron that responded exclusively to SFM. Figure 4A shows the response to varying modulation rate while depth was held at ±0.5 kHz, approximately the best depth. This neuron responded best to an SFM rate of ~80 Hz. It responded poorly to rates <40 Hz or >100 Hz (Fig. 4, A and C). Note that the response to the first cycle (*) was very small.

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| FIG. 4.
Example of an SFM-selective neuron's response to changes in SFM rate and SFM depth. A: peristimulus time histograms (left) and corresponding dot rasters (right) of responses to different rates of SFM. Note that even at SFM rates that yield high spike counts, the number of peaks is 1 less than the number of cycles, suggesting that the response to the 1st cycle (*) is diminished or absent. B: peristimulus time histograms (left) and corresponding dot rasters (right) of responses to different depths of SFM presented at a rate of 80 Hz. As in Fig. 3A, at SFM depths that yield high spike counts, the number of peaks is 1 less than the number of cycles. C: number of spikes per SFM cycle for modulation rates from 20 to 140 Hz. The neuron responded maximally to rates of 40 and 80 Hz. D: number of spikes per SFM cycle for SFM depths from ±0.1 to ±1.2 kHz. The neuron responded best to depths between ±0.4 and ±0.9 kHz. Stimulus duration: 100 ms.
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Figure 4, B and D, shows this neuron's response to changes in SFM depth while rate was held at 80 Hz, approximately its best rate. The neuron did not respond to a pure tone, but at a modulation depth of slightly above 0.1 kHz, spike count was above spontaneous activity; the neuron stopped responding at modulation depths of ~1.2 kHz and greater (Fig. 4, B and D). Thus the range of modulation depth was only a little over 1 kHz, suggesting that it was narrowly tuned to frequency. We were able to confirm this implication by obtaining a tuning curve with the use of a signal of 80 Hz SFM and varying the carrier frequency at a modulation depth of ±0.4 kHz. The result showed a Q10dB of 21 and a Q40dB of 63. (Q-best center frequency divided by the bandwidth of the tuning curve at 10dB or 40 dB above threshold.) This type of narrow tuning is typical of many neurons in the IC of Eptesicus (Casseday and Covey 1992
).
The fact that the tuning curve was narrower at high intensities than at low intensities suggests that the neuron would have a nonmonotonic rate-intensity function. To examine this issue in a general way, we obtained rate-intensity functions for 10 SFM-selective neurons; all were nonmonotonic.
Typically, SFM-selective neurons responded once per SFM cycle at the most, so that the response appeared to occur on only one direction of a modulation cycle. Figure 5 shows the percentage of SFM-selective and nonselective neurons that phase locked. The phase locking pattern, like that shown in Fig. 4, A and B, was a characteristic shared by all but 1 of the 30 SFM-selective neurons (Fig. 5). In contrast, of 59 neurons that were not selective but did respond to SFM, only 32 phase locked, and the remaining 21 had only an onset response to SFM (Fig. 5). Thus, although phase locking is not unique to SFM-selective neurons, it is a reliable feature of their response pattern.

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| FIG. 5.
Proportion of units that phase locked (p.l.) or failed to phase lock (non p.l.) to SFM, within the population of units that responded to SFM. All but 1 of the SFM-selective neurons (crosshatched bars) phase locked. Slightly over half of the nonselective neurons (diagonally striped bars) phase locked to SFM; the remainder had an onset response to SFM.
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Do SFM-selective neurons integrate over several cycles?
In the example shown in Fig. 3, A and B, there was little response to the first cycle (*), even at SFM rates or depths that elicited a vigorous response at later cycles. This response pattern was seen even for the rates or depths to which the neuron responded well. This observation raised the question of whether a "buildup" pattern was typical of SFM-selective neurons. Exactly 50% of the SFM-selective neurons failed to respond to the first cycle. The other 50% responded vigorously to the first cycle. Of 28 nonselective neurons, only 1 failed to respond to the first cycle.
These findings suggest that some SFM-selective neurons require integration over more than one cycle. Because the mechanism for integration would most likely be a neural delay network, these neurons might also be delay tuned to two sequential sounds, a possibility we address in the DISCUSSION.
Response latency
The question of response latency of SFM-selective neurons is not easy to answer because these neurons do not respond to pure tones; therefore latency cannot be measured directly. We have addressed this issue as follows: The first question is whether or not the neurons' responses were simply correlated in time with a particular frequency within the sweep. If so, the neurons should have responded twice per cycle. Most did not, suggesting that a specific sequence of frequency change was the effective stimulus. Second, if the neuron did require a sequence of frequencies, then its response should have occurred at a particular phase of the SFM cycle. Most neurons did in fact phase lock to the SFM cycle. Because the latency of the response to a pure tone cannot be determined, it is not possible to determine exactly what is the excitatory frequency in the cycle. However, if we assume that the latency of the response is a constant, it is possible to determine whether the excitatory frequency remains the same with changes in SFM rate, with the use of a method similar to that used by Anderson et al. (1970). A response that is phase locked to the modulation rate and, in addition, has a constant time delay from some specific frequency in the carrier signal, will have a positive phase shift as modulation rate is increased. In this case, plotting the phase of the response (
) against modulation rate (SFMf) will yield a positive linear function. The slope of the function (
/
SFMf), when converted from degrees to milliseconds, is the time delay (Anderson et al. 1970).
For seven neurons that were clearly phase locked, we plotted phase histograms at best SFM depth and at each SFM rate. We then measured the cumulative phase of the maximum peak and plotted this phase value as a function of SFM rate. The results in all cases could be fitted with a linear function. Figure 6 shows the results for four SFM-selective neurons. The slopes of the lines ranged from 2.85 to 3.24, yielding delays from 7.9 to 9.0 ms (Fig. 6). These values are probably roughly equivalent to latency from the excitatory frequency components of the SFM.

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| FIG. 6.
Phase of position of maximum spike count as a function of SFM rate. Phase is plotted cumulatively, because the response is assumed to extend beyond the 1st SFM cycle at high SFM rates. The data are from 4 SFM-selective neurons. Calculation of delays is described in the text.
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Tuning to modulation rate
All SFM-selective neurons responded well only over a specific range of modulation rates that varied among neurons. At 20 dB above threshold, all SFM-selective neurons had a best SFM rate at which the response reached a maximum probability of firing per cycle. Figure 7 shows a set of SFM-selective neurons from one animal. Each neuron has a characteristic best modulation rate. Most neurons had band-pass characteristics for modulation rate, but a few were high-pass or low-pass. The best SFM rate ranged from 20 to 150 Hz for 9 neurons in which the rate was measured by spike counts and from 20 to 175 Hz for 13 neurons in which the best SFM rate was estimated audiovisually. For the purpose of relating these results to delay-sensitive neurons in the IC, we note that these SFM rates have periods ranging from ~6 to 50 ms (see DISCUSSION).

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| FIG. 7.
Examples of tuning to SFM rate (left) and depth (right) for neurons that were selective for SFM. Pairs of graphs in each row contain data from the same neuron.
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We tested 52 units, 22 selective and 30 nonselective neurons, to determine the maximum SFM rate to which they would respond. Of the 22 SFM-selective neurons, 9 had an upper SFM rate limit of
100 Hz, 8 had an upper rate limit between 100 and 200 Hz, 2 had an upper rate limit between 200 and 300 Hz, and 2 were high-pass for rate, up to the maximum rates tested, ~700 Hz. At high rates these two units had a sustained response and were no longer phase locked. A comparable proportion of the nonselective neurons had the same limits. Of the 30 nonselective neurons, 15 had an upper SFM rate limit of
100 Hz, 9 had an upper rate limit between 100 and 200 Hz, and 3 had upper rate limits >400 Hz. Figure 8 shows examples of nonselective neurons that were tuned to SFM rate and depth. Thus most IC neurons are tuned to relatively low SFM rates, but this type of tuning is not a distinguishing characteristic of SFM-selective neurons.

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| FIG. 8.
Examples of tuning to SFM rate (left) and depth (right) for neurons that were not selective for SFM. Pairs of graphs in both rows contain data from the same neuron.
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Tuning to modulation depth
Neurons tuned to SFM rate were also tuned to modulation depth and had a characteristic best modulation depth. Some neurons, such as 1402-4 (Fig. 4) and 1403-7 (Fig. 7), were tuned to a low modulation depth, little more than ±1 kHz, and to a narrow range of depths, whereas others, 1403-10 (Fig. 7) required a deep frequency sweep but still responded only over a restricted range of depths.
For the population of SFM-selective neurons, the best depths ranged from ±0.4 to ±19 kHz. For 15 selective neurons, 6 (40%) responded best to SFM depths of ±2.5 kHz or less.
Of 37 nonselective neurons, 16 (43%) also responded best to SFM depths of ±2.5 kHz or less. We have no evidence that SFM-selective neurons are tuned more sharply to SFM depth than nonselective neurons.
We found no relationship between best SFM rate and best SFM depth. For each rate there were units at each modulation depth. Whether there is an interaction between tuning to modulation depth and modulation rate was not systematically tested in this study. We systematically tested one parameter while the other parameter was placed at the neuron's most responsive setting. However, during the course of testing we had to check several modulation depths and modulation rates and found no secondary areas of responsiveness. These results suggest that each cell has some combination of modulation rate and modulation depth to which it responds maximally.
 |
DISCUSSION |
In the present study, we found neurons that were selective for repetitive FM. These neurons were all tuned to SFM rate and SFM depth, and their response was phase locked to the SFM rate. The best rates of SFM tuning tended to be low (20-170 Hz), and most neurons had band-pass filter characteristics. Half of the SFM-selective neurons failed to respond on the first SFM cycle. This observation suggests that these cells were integrating information over more than one cycle, so that the crucial factor for these cells appears to be the repetition of FM sounds.
The results raise several issues for discussion. Do the SFM-selective neurons in the IC represent a class of cells not previously described? Are the band-pass tuning characteristics for SFM rate and depth general features of the mammalian IC? What might be the mechanism for SFM tuning? What is the biological relevance of SFM tuning? And is the relevance manifested in a link to motor performance?
Specialized cells for encoding FM sounds?
Specializations of IC neurons for certain classes of sounds have been reported previously. Suga (1969)
conducted the first systematic study in which pure tones, FM sweeps, and noise were used to determine whether neurons in the IC of Myotis were specialized for one or more of these stimulus types. Suga found a number of neurons that only responded to FM sounds. Similar results were reported later in the IC of other bats, including units that responded only to FM sweeps or had lower thresholds to FM sweeps than to other sounds (Casseday and Covey 1992
: Fuzessery 1994
; Vater and Schlegel 1979
). FM specialized neurons are also found in the IC of rats (Poon et al. 1991
). In these studies, a significant proportion of IC cells responded only to FM sweeps. In addition to the above specializations, there is also evidence that some IC neurons are specialized to detect faint broad-band noises (Schmidt et al. 1991
). The present findings add to the evidence that considerable specialization for specific patterns of sound occurs at the level of the IC.
An important new finding that should be compared with our results concerns combination-sensitive neurons in the IC of the mustached bat (Mittmann and Wenstrup 1995
; Yan and Suga 1996a
,b
). The neurons are selective for combinations of tones in which one tone precedes the other by a few milliseconds. The critical comparison with our results concerns the time delays to which the neurons are tuned. In one study (Mittman and Wenstrup 1995), the combination-sensitive neurons were tuned to delays up to 9 ms. In another study (Yan and Suga 1996a
), all but one best delay was <15 ms, and about two thirds of the best delays were <5 ms. A comparable measure in our neurons is the period of the best modulation frequency. The period ranged from ~6 to 50 ms. Therefore, by this comparison, most of our SFM-selective neurons respond to much longer time delays than did the combination-sensitive neurons. This comparison argues against an interpretation that SFM-selective neurons are actually the same as combination-sensitive delay-tuned cells. Of course, it could be argued that some neurons required combinations within a cycle, for example upward and downward sweeps, in which case the appropriate time measure would be half the period. Even in this case, the delay times, 3-25 ms, still extend beyond the range of most combination-sensitive neurons. However, this shorter time estimate is only appropriate for neurons that responded on the first SFM cycle. For those that responded only after the second or third cycle, the longer time estimate must be appropriate. Although we cannot exclude the possibility that those SFM-selective neurons that responded best at short SFM periods are the same class of neuron as combination-sensitive neurons, the majority of SFM-selective cells do not fit this classification. We therefore propose that these SFM-selective neurons are a previously undescribed population of cells that respond to slow repetitive FM. To test this hypothesis, it will be important in future experiments to study these neurons with the paradigm for combination-sensitive delay tuning, in addition to tests with FM stimuli.
We do not mean to imply that these neurons are selective for SFM stimuli per se. Rather, we suggest that the effective stimulus for these neurons is some repetitive frequency change contained within the SFM stimulus. For example, sequences of downward or upward sweeps alone might be the effective stimuli. Future experiments are needed to determine the specific patterns of frequency change that are essential.
Tuning to modulation rate
The results of this study and previous studies raise the question of whether or not tuning to low modulation rates is unique to the IC. In the present study most neurons that were tuned to SFM rate responded best to low modulation rates, <200 Hz. This limit was found in all SFM-tuned neurons, both selective and nonselective. Schuller (1979)
systematically studied responses to SFM rates between 10 and 500 Hz in the IC of the horseshoe bat. Most neurons responded best to rates of
100 Hz, and there was some indication that different units had different best rates. Comparable findings have been reported in the IC of rats, where neurons do not follow SFM rates >120 Hz (Rees and Møller 1983
). A similar rate limitation is also seen for repetitive amplitude modulations in the IC of several species (for review see Langner 1992
).
These low modulation rates stand in contrast to the modulation rates that some neurons below the IC can follow. Most neurons in the cochlear nucleus can follow SFM rates up to 800 Hz, and some can follow even higher rates (Vater 1982
). A similar ability to follow high rates of amplitude modulation has been seen in cochlear nucleus of rats (Møller 1972
) and gerbils (Frisina et al. 1985
). In the ventral and intermediate nuclei of the lateral lemniscus of Eptesicus, most neurons have best SFM modulation rates between 200 and 800 Hz, with a few at even higher rates, and in the dorsal nucleus of the lateral lemniscus most have best rates between 200 and 500 Hz (Huffman et al. 1995
). Thus, at the cochlear nucleus and nuclei of the lateral lemniscus, the limitation on rate of phase locking to FM seems to be imposed mainly by intrinsic properties of the neurons, so that there are no signs of filtering for modulation rate through neural circuitry. However, if we include amplitude-modulated sounds, there is an exception to the rule that subcollicular neurons can follow fast modulation rates. In the medial superior olive of the mustached bat and the Mexican free-tailed bat, units respond to SAM sounds only up to a maximum modulation rate of 300 Hz; the best rates of these neurons are <200 Hz (Grothe 1994
; Park and Grothe 1996
). These cells are not selective for modulated sounds, because they all respond well to continuous pure tones and other sounds. It may be that the medial superior olive is an early stage in the creation of modulation rate tuning, providing timing input to the IC. This proposed function adds to the list of hypotheses (Casseday et al. 1993
; Grothe 1994
) that may explain why the medial superior olive is hypertrophied in some species of bats (Covey and Casseday 1995
; Zook and Casseday 1982
). It is not known whether neurons in the medial superior olive are tuned to the rate of SFM sounds.
The question of whether SFM tuning is an active process that first appears in the IC or is due to preprocessing at an earlier stage can only be answered with other methods, either extracellular recording combined with antagonists to inhibitory transmitters or intracellular methods to observe synaptic potentials. These methods will also be necessary to determine whether or not SFM specialization is created in the IC.
Tuning for SFM depth
The question of whether IC units may be tuned to SFM depth does not seem to have been addressed previously. In prior studies of responses to SFM in the IC of bats, the intent has been to characterize the responses to an SFM stimulus in terms of absolute sensitivity to SFM depth. As far as we can tell, these previous reports concern neurons that were not SFM selective. Units in the cochlear nucleus are very sensitive to small modulation depths (Vater 1982
), but they do not seem to be tuned to specific ranges of depths. Some units in the IC of horseshoe bats are extremely sensitive to small modulation depths (Pollak and Schuller 1981
; Schuller 1979
), but it is not clear whether these units have an upper cutoff for modulation depth. Non-SFM-selective neurons in the rat IC seem to be rather tolerant to changes in modulation depth (Rees and Møller 1983
). In view of the fact that all of our SFM-selective neurons were tuned to SFM depth, some very sharply, it will be important in future studies to determine whether tuning for modulation depth is generated within the IC. SFM-selective units do not respond to single FM sweeps, so we cannot compare tuning to SFM depth versus tuning to parameters of single FM sweeps, such as rate of change. If inhibitory processes play an important role in SFM rate tuning, it may be that depth tuning is a byproduct of rate tuning. We address this and related questions next.
Mechanism for SFM tuning
Inhibition can be expected to play a major role in information processing in the IC. Inhibitory input, both GABAergic and glycinergic, arises from a number of different sources in the auditory pathways below the IC (see Covey and Casseday 1995
for review). The IC not only contains glycinergic receptors, but its density of
-aminobutyric acid-A(GABAA) receptors is as high as or higher than that in any other part of the brain (rat: Edgar and Schwartz 1990
; E. fuscus: Fubara et al. 1996
). The model proposed here for SFM tuning is a variation of the model for duration tuning (Casseday and Covey 1995
; Casseday et al. 1994
). The model employs inhibitory and excitatory components that are locked to stimulus events but are offset in time from one another by neural delays.
The fact that all SFM-selective neurons that we tested had nonmonotonic rate-level functions suggests that inhibition must play an important role in determining their response properties. The failure of many SFM-selective neurons to respond to the first cycle of SFM indicates that the temporal relationship of excitatory and inhibitory inputs, offset in time from one another, may play a role in the mechanism for SFM specialization. As has been shown for the medial superior olive in the mustached bat, a consequence of this sort of temporal interplay of inhibitory and excitatory inputs is a limit on the rate at which neurons can follow time-varying sounds (Grothe 1994
). Of special relevance is the observation that the excitatory frequency response areas of most IC neurons are bounded by inhibitory sidebands (Covey et al. 1996
; Faingold et al. 1991
; Pollak and Park 1993
). This observation holds even for neurons that do not have unusually narrow tuning curves. This last fact raises the question of whether the inhibitory inputs that create the sidebands could play a fundamental role in constructing tuning to SFM rate. If the inhibitory inputs and excitatory inputs were offset in time from one another, this offset would provide an opportunity for spike generation by the coincidence of subthreshold excitation and rebound from inhibition, as shown in Fig. 9. Suppose the excitatory input responds to the downward sweeping component of the SFM, and the inhibitory input responds to the upward sweeping component. If the SFM starts on a downgoing cycle, excitation will reach the cell first, but no spikes will be generated because the excitatory input by itself is subthreshold. The next sequential input is inhibition, in response to the upward portion of the SFM. The inhibition produces no response, but it does produce delayed depolarization or "rebound." If this rebound then coincides with the next excitatory input, a spike is generated. Notice that this hypotheses generates spikes only on N
1 cycles, because the first cycle always lacks one of the inputs. Of course the mismatch would occur on the last cycle, if the SFM cycle started 180° out of phase with the SFM cycle shown in Fig. 9. This mechanism can thus explain the "missing" response to the first cycle for half the SFM-selective neurons in our data. For the other half, one could assume that the same mechanisms operate, but the sequence of timing occurs within one cycle. This proposed mechanism depends on inhibitory sidebands, and a consequence of the inhibitory sidebands may be tuning to SFM depth.

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| FIG. 9.
Hypothetical mechanism for SFM specialization. Top: waveform of the modulating frequency. Trace labeled "Excitation": 2 excitatory postsynaptic potentials in response to the downward phases of the SFM. Trace labeled "Inhibition": inhibitory postsynaptic potential in response to the upward phase of the SFM cycle, followed by a "rebound." Trace labeled "Spike output": a spike results from the coincidence of the excitatory postsynaptic potential and the rebound from inhibition.
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Biological relevance
If SFM sounds simulate some aspect of biologically significant sounds, what might these sounds be? There are several possibilities: the approach "buzz," modulations of the searching signal, communication sounds, and sounds generated by insect wing beats.
The approach buzz that Eptesicus emits during hunting consists of a rapid series of downward FM sweeps that vary in rate from ~50 to ~200 Hz. These rates are within the range of best modulation frequencies of IC neurons. However, if echoes are added for each pulse, the rate may increase well above that to which most SFM-selective neurons respond best. Therefore, if these neurons are sensitive to the approach buzz, it is probably to the pulses or echoes alone rather than to the combination. A limitation in how well these neurons could process pulses or echoes would be imposed when the pulse and echo are nearly the same intensity, as when the bat is close to the echo source. The first step in further studies is to examine the response to sequences of FM sweeps to determine whether the cells are responsive to repetitive downward or upward sweeps. Response to upward sweeps would greatly reduce the possibility that the neurons are selective for echolocation sounds.
During the searching phase of hunting, Eptesicus and other "FM" bats emit a 20-ms pulse that is "quasi-constant frequency" (QCF) (Kalko and Schnitzler 1989
). Echoes of this signal reflected from the beating wings of insects could contain amplitude modulations and FM (Schnitzler et al. 1983
). The QCF of Eptesicus changes from ~28 to ~23 kHz. Many SFM-selective neurons had best center frequencies within this range. To produce two or more modulation cycles on a 20-ms signal would require a modulation rate of
100-Hz SFM. Because most SFM-selective neurons have best modulation rates at <100 Hz, they would be insensitive to modulations of the QCF, such as are produced by insect wing beats. Of course for units that respond on the first cycle, it is also possible that they respond to modulation of one cycle. In any case it is unlikely that SFM-selective neurons operate exclusively on processing the QCF, because the center frequencies of many of the neurons are well above the frequencies of the QCF signal.
Condon et al. (1994)
explored the idea that neurons could be tuned to amplitude modulations that would be imposed across a sequence of echoes by the fluttering wings of insects while the bat emits brief FM pulses during the approach phase of hunting. Those researchers found that neurons in the IC of the little brown bat responded to amplitude modulations that spanned FM sweeps presented at rates comparable with the bat's emission rates during the approach and capture phases of hunting. This observation raises the possibility that SFM-selective neurons could also respond selectively to FM imposed on multiple echoes of the big brown bat's pulse during the approach phase.
The communication sounds of some bats have carrier frequencies in the echolocation range and contain periodic FM. For example, the mustached bat emits long (
100 ms) signals that are SFM-like with a modulation rate of ~200 Hz (Kanwal et al. 1994
). The communication sounds of Eptesicus have not been studied with modern techniques (Gould 1971
). To determine whether SFM-selective neurons play a role in detection of communication sounds, it would be especially useful to analyze the spectral-temporal structure of the big brown bat's communication signals.
The sounds produced by the wing beats of June beetles can extend up to 20 kHz according to one recording (Hamr and Bailey 1985
). These sounds modulate at a rate of ~50 Hz. Whether they have sufficient FM to activate SFM-selective neurons remains to be determined.
Relation to motor performance
In the INTRODUCTION we pointed out that biologically important sounds that are modulated have a relatively slow modulation rate, usually <200 Hz, and our findings show that SFM-tuned neurons have best SFM rates that are in this biological range. Here we discuss the reasons why SFM-selective neurons in the IC are good candidates for a link between sensory processing and motor performance.
The question of whether some specialized aspect of sensory processing represents tuning for biologically relevant stimuli is closely related to the question of how proximal the sensory processing is to the motor systems that generate an appropriate response to the stimulus. The IC is at the interface between sensory and motor systems. The IC projects to motor coordination systems in the deep superior colliculus and, via the pontine gray, to the cerebellum (Schuller et al. 1991
). If SFM-selective neurons are among the population of IC cells that project to the deep superior colliculus or to the pontine gray and thence to the cerebellum (see Casseday and Covey 1996
for review), then activation of specific SFM-selective neurons could activate specific behavior patterns. In neuroethological terms, it is possible that some IC neurons are filters for sign stimuli and, through connections with motor systems, participate in the generation of fixed action patterns. A good example of a fixed action pattern in echolocating bats is the terminal buzz. The buzz can be elicited in the laboratory by manipulating the time at which artificial echoes follow the bat's echolocation pulse. If the echoes return within a minimum delay, the bat increases the repetition rate of its pulses and decreases the duration of each pulse (Roverud and Rabitoy 1994
). This stereotyped response, typical of a fixed action pattern, would be activated by a cue obtained from the temporal relationship between a series of echolocation pulses and the returning echoes.
Although the specific mechanisms whereby central auditory pathways activate motor patterns are unknown, we have argued elsewhere that one clue is found in the limited rates that some IC neurons can follow. The tuning for SFM rate in many IC neurons may be the best example so far of this limitation. Tuning to slow modulation rates necessarily limits the repetition rates that SFM-tuned neurons can follow. Does the rate reduction serve any purpose in motor performance? On the basis of the evidence that the initiation of motor output, such as vocalizations, occurs at a much slower pace than the rate at which auditory neurons below the IC can respond, Casseday and Covey (1996)
proposed that at some point in the interface between sensory input and motor response, the temporal pace of sensory processing is reduced to match the pace of motor action. This reduction could be an intrinsic part of the sensory pathway that projects to motor systems, or it could be imposed on the sensory pathway by some part of the motor system, the substantia nigra for example (Olazábal and Moore 1989
). Both mechanisms could operate in the IC of bats and other mammals, and SFM selection and tuning could be a reflection of these mechanisms.