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INTRODUCTION |
Differences and similarities in the representation of both spectral and temporal aspects of sound have been demonstrated in about half a dozen auditory cortical areas. Differences support the idea that auditory cortex is organized functionally in a segregated manner, i.e., where different areas respond to different aspects of sound. As a result of such differences, two segregated pathways, similar to the "what" and "where" pathways in the visual system, have been proposed for monkey auditory cortex (Rauschecker 1997). Similarities emphasize that multiple cortical representations of certain stimulus properties are preserved thereby allowing a synthesis of spatially distributed responses. In addition, local integration of the property that is preserved in each area with other sound attributes that are mapped differently in these areas can be performed independently.
Spectral stimulus properties
In the cat, the primary auditory cortex (AI) and the anterior auditory field (AAF) show very similar single-unit frequency tuning (Knight 1977
; Phillips and Irvine 1982). In contrast, the depth-recorded local field potential in AI of the cat was more sharply tuned than in AAF (Knight 1977). In AI and AAF, the percentages of monotonic and nonmonotonic units was also very similar (Kowalski et al. 1995; Phillips and Irvine 1982) but AAF featured multipeaked frequency response areas that rarely were observed in the central and ventral parts of AI but frequently were seen in the dorsal part (Knight 1977; Sutter and Schreiner 1991; Tian and Rauschecker 1994). In the ferret, the single-unit frequency-tuning curve bandwidth at 20 dB above threshold in AAF (mean 2.5 octaves) was approximately twice as large as that in AI (mean 1.3 octaves) (Kowalski et al. 1995). In the Mongolian gerbil, no differences were found in the responses to tone bursts and slow FM sweeps in AI and AAF (Schulze et al. 1997).
Secondary auditory cortex (AII) in cat appeared not as well organized tonotopically as AI, and the units showed broader frequency-tuning and higher thresholds to tone-burst stimulation than in AI. In parts of AII, responses were more sustained than in AI and latencies could be >100 ms; however, phasic responses with relatively short latencies (<50 ms) were also common (Schreiner and Cynader 1984). Recently a double representation of the cochlea within the classical boundaries of AII in cat was demonstrated (Volkov and Galazyuk 1998). One tonotopic representation was found in the dorsocaudal region (2.6-3.2 mm long) with a spatial orientation similar to that described in AI (low frequencies caudal, high frequencies rostral), the second ventrorostral region was smaller (1.4-2.5 mm) and had the opposite tonotopic orientation (i.e., as in AAF). The dorsocaudal region may be close to the transition zone between AI and AII as defined by Schreiner and Cynader (1988). Thus AII, not unlike AI, may consist of several specialized subregions.
Latencies
Conflicting reports exist about the minimum latencies in AI and AAF. Whereas Knight (1977) and Phillips and Irvine (1982) found the same latencies in the cat, Kowalski et al. (1995) reported a mean AAF latency of 16.8 ms that was substantially shorter than the mean latency in AI of 19.4 ms. These latencies were, however, several milliseconds longer than found in cat AI (mean 12.5 ms) by Raggio and Schreiner (1994) and Mendelson et al. (1997) but comparable with those (mean 17.7 ms) reported for free-field studies by Eggermont (1996). In cat, AI and AAF receive different anatomic projections from the auditory thalamus: AI receives the heavier projections from the ventral medial geniculate body (MGB), whereas AAF receives the heavier projections from the posterior group of thalamic nuclei, PO (Morel and Imig 1987). AII receives its input largely from the caudal dorsal nucleus of the MGB (Andersen et al. 1980) and is part of the extra lemniscal pathway. AI, AAF, and AII also receive input in layer I from the nontopographical organized medial part of the MGB (Winer 1992). This different innervation may in part be responsible for the observed differences in latencies and frequency tuning.
Temporal stimulus properties
Periodic amplitude modulated (AM) sounds such as sine- or square-wave modulated tone bursts also are represented differently in separate cortical fields in cat (Schreiner and Urbas 1988). The average best modulation frequency (BMF) in AAF of the paralyzed and lightly barbiturate anesthetized cat was higher than in AI. This was largely the result of higher BMFs (
100 Hz) for units with characteristic frequencies (CFs) >10 kHz, whereas for lower CFs, all BMFs were <20 Hz and similar to those in AI. In AII, a range of "normal" BMFs close to 10 Hz was found but supplemented by a large group of BMFs with values <5 Hz. Such low BMFs were found less frequently in AI or AAF (Schreiner and Urbas 1988).
Comparison of the effect of various periodic stimuli such as sinusoidal AM and rectangular AM of tones at the CF (Schreiner and Urbas 1986, 1988) with periodic click trains (Eggermont 1991, 1993; Schreiner and Raggio 1996) in cat AI suggests that the BMFs are highest for sinusoidal modulation than for rectangular modulation and lowest for periodic click trains. Phillips et al. (1989) used continuous repetitive tone pips and analyzed them in terms of the response per tone pip, i.e., in terms of entrainment. Eggermont (1991) showed a similar analysis for periodic click stimulation and Schreiner and Raggio (1996) also presented their data in this alternative format in addition to using the temporal modulation transfer function (tMTFs). These entrainment functions are low-pass functions of click (tone pip) repetition rate and were very similar in these three studies (Eggermont 1997).
Internal neural representations
Regardless of whether entrainment functions or tMTFs are used, these measures are constructed on basis of full knowledge of the stimulus presented to the animal. The stimulus is, of course, unknown to the animal, and its nervous system cannot extract temporal information from aspects such as the stimulus onset and repetition rate. As a consequence, period histograms, as used in the construction of the tMTFs, cannot be computed by the nervous system. However, information similar to that in period histograms is available in the interspike interval (ISI) histograms (Horst et al. 1986) or autocorrelograms (Cariani and Delgutte 1996a). These internal neural representations can be used to extract stimulus periodicity information and may even be preferred. As Cariani and Delgutte (1996b) remarked "period histograms or their derived measures (synchronization and modulation indexes), for example, detect only stimulus-locked time patterns, and will miss more subtle, asynchronous codes .... Until better methods are used to study responses to complex stimuli in more central auditory stations, complex temporal pattern codes cannot be ruled out." In response to harmonic stimuli, the most frequent ISI present in a population of auditory nerve fibers appears to correspond to the perceived pitch (Cariani and Delgutte 1996a). This ISI calculation can be done for individual neurons and then pooled across the entire population or across a local group of neurons recorded on the same electrode. A direct comparison between tMTFs and ISI histograms for cortical coding of periodic stimuli has so far not been made. It is one of the purposes of this study to compare this for three sets of stimuli: periodic click trains, amplitude-modulated noise bursts (AM noise), and amplitude-modulated tone bursts (AM tone) in three areas: AI, AAF, and AII.
Anesthesia, cortical rhythms, and temporal coding
Recently, we (Kenmochi and Eggermont 1997) reported a strong correlation between the dominant oscillation (spindle) frequency in the spontaneous local field potential (LFP) and the BMF for periodic click train stimulation in primary auditory cortex. The mean spontaneous rhythm was generally in the frequency range of 8-14 Hz but could be as high as 30 Hz, and one expects this LFP rhythm to be similar in other auditory cortical fields. This suggests that the BMFs in AI, AAF, and AII will be very similar when recorded under identical anesthesia levels. Spontaneous EEG rhythms are strongly dependent on the level of arousal or drowsiness, thus one expects the tMTFs to be similarly effected by changes in anesthesia level. However, Schulze and Langner (1997) did not find substantial effects of anesthesia on coding of AM tones in AI in the gerbil.
In awake squirrel monkeys, Bieser and Müller-Preuss (1996) investigated periodicity coding for AM sounds in eight auditory cortical areas and found a broad range of BMFs ranging from 2 to 128 Hz. Low modulation frequencies (2-64 Hz) produced mostly phase-locked neural responses whereas higher AM (128-512 Hz) sounds showed only a distinction in overall-spike-rate variations. For instance in field Pi, the highest firing rates were found for modulations of 128 Hz, whereas in field T1 they were highest for 256 Hz. Specifically in fields AI, Pi, and T1 the sensitivity to AM frequency was large enough to encode envelope fluctuations (modulation rates between 4 and 64 Hz) found in monkey calls. Steinschneider et al. (1980, 1982) found phase-locked activity in the depth-recorded local field potentials in auditory cortex of awake macaques up to 250 Hz and for multiunit spike activity 100 Hz.
For periodic click trains, Eggermont (1993) found for ketamine-anesthetized cats a best repetition rate of 7.9 ± 2.1 (SD) Hz, and in barbiturate-anesthetized cats Schreiner and Raggio (1996) obtained a similar value of 6.5 ± 3.8 Hz. A comparison of BMFs for AM sound in cat (Eggermont 1994, 1997; Schreiner and Urbas 1988) with those from awake squirrel monkeys (Bieser and Müller-Preuss 1996) and awake macaques (Steinschneider et al. 1980, 1982) suggests that anesthesia affects the BMF. This conclusion also was drawn by Goldstein et al. (1959) and Eggermont and Smith (1995a) on the basis of evoked potential (EP) studies. Under barbiturate anesthesia the limiting rate (50% of maximum EP amplitude) in cats was 6-8 Hz, down from 15-20 Hz in awake cats (Goldstein et al. 1959), whereas for light ketamine anesthesia the limiting rate was only slightly <15 Hz (Eggermont and Smith 1995a).
Comparing the results in awake and anesthetized animals suggests that level and type of anesthesia are likely having an effect, potentially by influencing the frequency range of the dominant autonomous cortical rhythms. It is thus desirable to eliminate potential effects of differences in anesthesia level between recordings when comparing spectral and temporal coding properties in various cortical fields. For this purpose, simultaneous recordings were made from AI, AAF, and AII in lightly ketamine-anesthetized cats.
Perceptual attributes of periodic sounds
Amplitude-modulated tones or noise produce various hearing sensations depending on the modulation frequency. These are rhythm and fluctuation strength for AM frequencies below ~20 Hz, and roughness and pitch for AM frequencies >20 Hz. The sensation of roughness disappears >300 Hz and is strongest at 70 Hz, whereas pitch related to periodicity in the stimulus envelope loses much of its perceptual strength >3 kHz (Zwicker and Fastl 1990). The click repetition rates and AM frequencies used in this study straddle the boundary between rhythm and roughness, and the limiting rate of phase-locked cortical responses may be related to that perceptual boundary. All results obtained for AM stimuli in auditory cortex so far point to a potential temporal code up to frequencies of ~20-30 Hz (Schulze and Langner 1997). However, no direct evidence for an internal temporal code based on ISIs so far has been demonstrated in auditory cortex nor has its AM frequency range been determined.
This study addresses the following questions: 1) is there, within each cortical area, a difference in the temporal coding of periodic click trains, AM noise bursts, and AM tone bursts? 2) Is there a difference in this coding among the three cortical fields? 3) Is the repetition rate range of temporal coding as inferred from stimulus referenced measures such as the tMTF and internal neural representations such as the all-order ISI histogram the same? 4) Is the perceptual distinction between rhythm and roughness for AM stimuli related to the upper limit of a temporal representation of AM frequency in auditory cortex? And 5) are interarea differences in temporal response properties related to differences in frequency tuning and response latencies?
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METHODS |
The care and the use of animals reported on in this study was approved (No. P88095) by the Life and Environmental Sciences Animal Care Committee of the University of Calgary.
Animal preparation
Cats were premedicated with 0.25 ml/kg body wt of a mixture of 0.1 ml acepromazine (0.25 mg/ml) and 0.9 ml of atropine methyl nitrate (5 mg/ml) subcutaneously. After ~0.5 h, they received an intramuscular injection of 25 mg/kg of ketamine (100 mg/ml) and 20 mg/kg of pentobarbital sodium (65 mg/ml). Durocain (20 mg/ml) was injected subcutaneously and rubbed in gently, then a skin flap was removed and the skull cleared from overlying muscle tissue. A large screw was cemented upside down on the skull with dental acrylic. An 8-mm-diam hole was trephined over the right temporal cortex so as to expose parts of AI and AII. A 4-mm hole was drilled over the AAF. The dura was left intact, and the brain was covered with light mineral oil. Then the cat was placed in a sound-treated room on a vibration isolation frame and the head secured with the screw. Additional acepromazine/atropine mixture was administered every 2 h. Light anesthesia was maintained with intramuscular injections of 2-5 mg·kg
1·h1 of ketamine. The wound margins were infused every 2 h with durocain, and also every 2 h new mineral oil was added if needed. The temperature of the cat was maintained at 37°C. At the end of the experiment the animals were killed with an overdose of pentobarbital sodium.
Acoustic stimulus presentation
Acoustic stimuli were presented in an anechoic room from a speaker placed 55 cm in front of the cat's head. The sound-treated room was made anechoic for frequencies >625 Hz by covering walls and ceiling with acoustic wedges (Sonex 3") and by covering exposed parts of the vibration isolation frame, equipment, and floor with wedge material as well. Calibration and monitoring of the sound field was done using a B&K (type 4134) microphone placed above the animal's head and facing the loudspeaker. A search stimulus consisting of random-frequency tone pips, noise bursts, and clicks was used to locate units. Characteristic frequency (CF) and tuning curve of the individual neurons were determined with 50-ms-duration, gamma-shape-envelope, tone pips presented randomly in frequency once per second (Eggermont 1996). The 81 different frequencies used were equally spaced logarithmically between 625 Hz and 20 kHz (or between 1.25 and 40 kHz) so that 16 frequencies were present per octave. After the frequency-tuning properties of the cells at each electrode were determined, periodic click trains (1-s duration followed by 2 s of silence) and amplitude-modulated noise or tone bursts (0.5-s duration followed by 2.5 s of silence) were presented once per 3 s. The modulation frequencies were between 1 and 32 (click trains) or 2 and 64 Hz (AM sounds) at logarithmically equal distance with four values per octave and were presented randomly. The AM wave form was an exponentially transformed sine wave (Epping and Eggermont 1986) with a maximum modulation depth of 17.4 dB so that the envelope, when expressed in dB, was sinusoidally modulated. The sequences of 21 click trains or 21 AM noise or AM tone bursts were repeated 10 times resulting in a total stimulus ensemble duration of 630 s per stimulus type. The click trains and AM sounds were presented at peak intensities of 35, 55, and 75 dB SPL, and results for the intensity where the firing rate was the highest are presented. Clicks consisted of single polarity electric pulses 0.1 ms in duration.
Recording and spike separation procedure
Three tungsten micro electrodes (Micro Probe) with impedances between 1.5 and 2.5 M
were advanced independently perpendicular to the AI, AAF, and AII surfaces using remotely controlled motorized hydraulic microdrives (Trent-Wells Mark III). The electrode signals were amplified using extracellular preamplifiers (Dagan 2400) and filtered between 200 Hz (Kemo VBF8, high-pass, 24 dB/oct) and 3 kHz (6 dB/oct, Dagan roll-off) to remove local field potentials. The signals were sampled through 12 bit A/D converters (Data Translation, DT 2752) into a PDP 11/53 microcomputer, together with timing signals from three Schmitt-triggers. In general the recorded signal on each electrode contained activity of two to four neural units. The PDP was programmed to separate these multiunit spike trains into single-unit spike trains using a maximum variance algorithm (Eggermont 1992). The spikes from well-separated wave-form classes, each assumed to represent a particular neuron, were stored and coded for display. The multiunit data presented in this paper represent only well-separated single units that, because of their regular spike wave form, likely are dominantly from pyramidal cells (Eggermont 1996). Thus contrary to the common use of the term multiunit as a cluster of not well separable units, in this analysis the separable single-unit spike trains extracted from the multiunit recording were added again to a form a multiunit spike train with better statistics and that likely consists of contributions from only one type of neurons.
In addition, the electrode signals were band-pass filtered between 10 and 100 Hz to obtain spike-free signals of ongoing local field potentials (LFP). These signals also were passed through Schmitt-triggers set at ~2 SD (i.e., at about 
100 µV) below the mean value of the ongoing signal during silence. The "spikes" of these LFPs were processed in the same way as single-unit spike data. We have shown previously that these level crossings represent most of the temporal (Eggermont and Smith 1995a) and spectral (Eggermont 1996) response properties of the single units recorded at the same electrode.
The boundary between AI and AAF was explored by taking a series of LFP and multiunit measures (with the high-pass filter set at 10 Hz with 6 dB/oct) from caudal to rostral and assuring that there was a gradual increase in CF, which reversed in direction when advancing to the AAF. The AII was identified anatomically and electrophysiologically based on the broader tuning curves and different response patterns compared with those in the central and ventral parts of AI. Recordings in AII were generally made from the ventrorostral part. Recording electrode positions in the three cortical areas were chosen such that recordings with approximately similar best frequencies (within 0.5 octave) at 50-70 dB SPL were obtained. Recordings were made between 600 and 1,200 µm below the cortex surface.
Data analysis
The number of action potentials in the first 100 ms after each tone-pip presentation were counted for each intensity. The counts for three adjacent frequencies were combined to reduce variability and divided by number of stimuli and presented as a firing rate per stimulus. This resulted in 27 frequencies covering 5 octaves so that the final resolution was ~0.2 octaves. The results per stimulus intensity were combined into a rate-frequency-intensity profile from which tuning curves, rate-intensity functions, and iso-intensity-rate contours could be derived (Eggermont 1996). The frequency-tuning curve was defined for a firing rate at 25% of the maximum firing rate. The threshold was determined as 2.5 dB below the intensity that produced visible time locked responses to the tone pip, i.e., midway between the stimulus that produced a response and the one that did not. The tuning curve bandwidth was measured at 20 dB above threshold and expressed in octaves.
Latency measures were corrected for the acoustic travel time from the speaker to the cat's ears by subtracting 1.6 ms (speaker distance 550 mm divided by the speed of sound 340 mm/ms).
The following capacity of the neurons for click repetition rate and AM frequency was estimated from the temporal modulation transfer functions and ISI histograms. The temporal modulation transfer functions were obtained by Fourier transformation of the period histograms (Eggermont 1991). Each modulation period was divided into 16 bins, and only recordings with at least five counts in the maximum bin per 10 stimulus presentations at a rate of 8 Hz were analyzed further. The rate-tMTF was defined as the number of spikes per 10 presentations of the 1-s duration click train or 0.5-s-duration modulated noise or tone burst, as a function of click repetition rate or modulation frequency. The synchronization-tMTF was defined as the amplitude of the first harmonic of the period histogram, as a function of the click-repetition rate or AM frequency. The BMF was defined as the click or AM rate for which the synchronization-tMTF was maximal. The limiting rate was defined as the highest rate at which the response was 50% of that at the BMF. The significance of the vector strength for each modulation frequency (defined as P < 0.005) was calculated using the Raleigh test (Mardia 1972).
ISI histograms and autocorrelograms for lags up to three periods were constructed with 16 bins per stimulus period for each individual click rate or AM frequency. The accuracy was thus proportional to the stimulus period, and the capacity to represent click repetition rates or AM rates in an interval code is reflected in a peak in the same position in the histograms regardless of the click repetition rate or AM frequency. Population autocorrelograms and interval histograms were obtained by adding all the individual single-unit ones. Multiunit (MU) autocorrelograms per recording site were also calculated, they reflect both single-unit autocorrelograms as well as cross-correlograms between units on the same electrode. The autocorrelograms is formally identical to the all-order ISI histogram, i.e., formed by adding the interval histogram for adjacent spikes, for one but adjacent spikes, etc., (Cariani and Delgutte 1996a) and I use the terms interchangeably.
All statistical analyses were performed using Statview 4.5. Illustrations were made with Horizon and Powerpoint Software.
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RESULTS |
Results presented for periodic click trains are from 62 simultaneous recordings with an electrode in each of AI, AAF, and AII in 14 cats, resulting in 186 MU records and the same number of LFP records. The MU records could be separated into 552 single-unit (SU) spike trains of which 250 had large enough firing rates to permit analysis of the tMTF (as defined in METHODS). In nine of these cats, responses also were obtained for AM noise and AM tones for a total of 102 MU records (separable into 156 SU spike trains with sufficient firing rates). We also made 23 simultaneous recordings for click train stimulation with two electrodes in AI and one in AAF resulting in 69 MU records (88 single units with sufficient firing rates) and 69 LFP recordings in seven additional cats. In 27 of these recordings, we also stimulated with AM noise and AM tones (27 MU records resulting in 33 single-unit spike trains with sufficient firing rates). The total number of single units presented in this study is 338 (of 740 single units recorded from) in 21 cats. In the MU data, all units with well-defined spike wave form were included regardless of their firing rate.
Representative examples for periodicity coding
Figure 1 shows dot displays from electrode sites in AI (left), AAF (middle), and AII (right) with similar CF of ~5 kHz in response to periodic click trains (top), AM noise bursts (center row), and AM tone bursts (carrier 5 kHz; bottom). Click repetition rates were between 1 and 32 Hz, whereas AM frequencies were between 2 and 64 Hz. Both LFP triggers (orange dots) and single units (green, blue, or black dots) are shown for a time base of 600 ms after stimulus onset. As a consequence only the response to the first half of the click train is shown. The recording site in AI did not show strong single-unit responses to click trains (and is therefore not representative of our sample of AI units) but good following for the LFP. For AM stimuli, however, three single units were responding up to AM rates of 16 Hz, with the stronger responses for AM noise. No onset unit responses coinciding with the LFP triggers were noted for AM noise bursts and unit onset responses were limited to modulation frequencies between 8 and 32 Hz for AM tone bursts. For AAF, the LFP triggers showed similar following as in AI, whereas the unit activity was much more pronounced especially for the AM stimuli. The best following was found for AM tone bursts (32 Hz) albeit that the more vigorous responses were obtained for AM noise burst stimulation. A pronounced OFF response is noted for noise with AM frequencies of 16-64 Hz. In AII, LFP activity showed weak following as did most unit activity, and the strong long latency unit activity for noise bursts was largely independent of AM rates and likely a rebound response after initial suppression of firing.
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| FIG. 1.
Dot displays for simultaneous local field potential (LFP) and single-unit (SU) activity in primary auditory cortex (AI), anterior auditory field (AAF), and secondary auditory cortex (AII) in response to periodic click trains, and amplitude-modulated noise and tone bursts (5 kHz). LFP triggers are indicated by the orange dots, SU spikes by blue, green, or black dots. Click repetition rate (1-32 Hz) and the AM frequencies (2-64 Hz) are represented on a logarithmic axis. First 600 ms after stimulus (click train or AM burst) is shown. characteristic frequencies (CFs) of the recording sites were 5.2 kHz (AI), 6 kHz (AII), and 6 kHz (AAF) This example illustrates the consistent modulation following of the LFP triggers in all areas. SU responses besides AM following include OFF responses (AAF for AM noise at high AM frequencies) and rebound activity related to spindle rhythms (AII, AM noise).
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The second example (Fig. 2) from recording sites, with a best frequency of ~2.5 kHz in AI and AAF and 3-7 kHz (broadly tuned) in AII, illustrates preferential locking of the unit firing to the modulation frequency for AM sounds in AI, to the broadband sounds (click train and AM noise) in AAF and to the AM tones (2.5 kHz) for AII. The LFP triggers in AI and AAF are found to follow repetition rates for clicks up to ~10 Hz and with skipping one period 16 Hz. AM rate following was slightly higher: up to ~16 and 20 Hz with skipping one period. OFF responses again were noted for AM noise stimulation but this time in AI.
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| FIG. 2.
Dot displays for simultaneous LFP and SU activity in AI, AAF, and AII in response to periodic click trains, and amplitude-modulated noise and tone bursts (2.5 kHz). Format is similar to Fig. 1. CFs of the recording sites were ~2.5 kHz in AI and AAF and 3-7 kHz in AII (broadly tuned).
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From these dot displays the next step is to generate period histograms and from these the rate tMTF (mean number of spikes per 10 stimuli), the vector strength (Goldberg and Brown 1969), and the synchronization tMTF [the product of rate and vector strength (VS)]. Figure 3 presents this for three MU conditions shown in Fig. 2. Figure 3A present results for the AAF recording in response to click trains, the period histogram (the 1st 2 panels show this twice, useful to track peak phase changes that exceed 1 period as in Fig. 3C) illustrates the gradual shift of the peak response in its relative position in the click-repetition period (i.e., its phase). The third panel shows the rate tMTF, and an abrupt drop in the response is noted >9.56 Hz. The VS (4th panel) appears significant for all repetition rates except at 11.3 and 32 Hz. Finally, the synchrony tMTF peaks at 4.76 Hz (its BMF) although it is only marginally larger than at 8 Hz. The limiting rate (50% of the value at BMF) is found for 9.52 Hz and coincides with the abrupt drop in firing rate. Figure 3B shows the same analysis for AM noise in the AI recording. The nearly constant phase of the peak response in the period histograms for AM frequencies <8 Hz forms a clear exception to the normal monotonic increase of phase. The abrupt drop in mean firing rate at 8 Hz results in both a BMF and limiting rate at 6.72 Hz. Figure 3C shows one of the infrequent instances where a recording in AII showed good locking to the period of AM tones. The mean number of spikes per 10 stimuli decreases monotonically with AM frequency, the VS shows a band-pass dependence on AM frequency, the BMF is 9.52 Hz, and the limiting rate is 11.3 Hz.
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| FIG. 3.
A: period histograms, rate modulation transfer function, vector strength, and synchrony modulation transfer function for the response to clicks in AAF as shown in Fig. 2. Period histogram is shown twice. Preferred phase of the response shifts from the beginning to little over half of the period for increasing click rates. Rate temporal modulation transfer function (tMTF, middle panel) shows an abrupt reduction at a click rate of 11.28 Hz. Vector strength (VS) is relatively strong throughout the entire range of click rates. Synchrony tMTF peaked at 4.76 and at 8 Hz, the best modulation frequencies (BMF) was estimated at 8 Hz. Limiting rate, at 50% of the value at the BMF, was at 11.28 Hz. B: following the same format, the result for AM noise in AI, for data shown in Fig. 2, is presented. Note that AM rates now are between 2 and 64 Hz. Firing rate and VS as well as number of synchronized spikes drops substantially at 8 Hz (limiting rate) for a BMF of 6.72 Hz. C: again in the same format, shows results for AM tone stimulation in AII (data same as in Fig. 2). Number of spikes per tone burst is a slowly decreasing function of AM frequency, the VS is high only between 6.72 Hz and 9.52 Hz. BMF is 9.52 Hz, and the limiting rate is 11.28 Hz.
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Group data
DISTRIBUTION OF BEST MODULATION FREQUENCIES AND LIMITING RATES FOR PERIODIC CLICK TRAINS.
The BMFs for multiunits in response to periodic click train stimulation were approximately normally distributed (Fig. 4A). No significant effect of area was noted (Table 1, paired t-test, P > 0.08). For individual single units, the distribution of BMFs generally was comparable with that for the MU data (Fig. 4B) but there were relatively more values in the 2- to 3-Hz range. There were no significant differences between the BMF values for single units with those for the MU data (Table 1). The BMF value for the tMTF based on LFP triggers (Table 1) was significantly larger (paired t-test, P = 0.008) than that for MU spikes in AI and also in AII (paired t-test, P = 0.04) but not in AAF. The BMFs for LFP triggers and MU spikes were correlated positively (R = 0.205; Fisher's r to z test, P = 0.0025).
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| FIG. 4.
Distributions of the BMFs for multiunit (MU) and SU activity in response to periodic click trains in the 3 areas. A: BMFs are very similarly distributed for MUs. B: same for single units.
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The distribution for the limiting rates for multiunit is shown in Fig. 5A. No effect of area was found on the mean values (Table 1, paired t-test, P > 0.5). For single units, the distribution of limiting rates was generally more predominant in the 9- to 12-Hz range than for the MU data (Fig. 5B). Again higher mean values were found for LFP triggers but these were not significantly different between areas. For all areas, the limiting rates were higher for the LFPs than for the corresponding MUs (paired t-test, P < 0.05). The limiting rates for both measures were correlated positively (R = 0.139; Fisher's r to z test, P = 0.034).
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| FIG. 5.
Distributions of the limiting rates for MU and SU activity in response to periodic click trains. A: limiting rates for MU data are very similar for the 3 cortical areas. B: same for SU data.
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Besides taking all LFP triggers, a separate analysis was run on only the first LFP trigger after a click (LFP1) to avoid effects of secondary LFP triggers (within 50 ms of the first, as shown in Fig. 2) that are never accompanied by spikes. The BMFs for these LFP1 triggers were not different from those where all triggers were used, however, the limiting rate for LFP1 triggers was significantly higher in AI (19.3 vs. 17.2 Hz; paired t-test, P < 0.0001).
DISTRIBUTION OF BEST MODULATION FREQUENCIES AND LIMITING RATES FOR AM STIMULI.
Best modulation frequencies and limiting rates for MU recordings were not significantly different for AM noise and AM tone stimulation (Table 1; analysis of variance with post hoc Scheffe test, at the P < 0.05 level) and therefore the data were combined in the frequency distributions shown in Fig. 6. Most BMFs (Fig. 6A) were found for AM frequencies <20 Hz in all three areas but in contrast to click train stimulation, BMFs >20 Hz were found as well. As a result, the standard deviation in the BMFs is very large and the modal BMFs are significantly smaller than the mean (Table 1). Limiting rates (Fig. 6B) were relatively uniformly distributed between 2 and 64 Hz.
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| FIG. 6.
Distribution of BMFs and limiting rates for MU responses to AM stimuli in 3 areas. A: most frequent BMF values found are <10 Hz but the range of values is much wider than for click stimulation (cf. Figs. 4 and 5), thus the mean value is considerably larger than the mode. B: fairly uniform distribution of limiting rates for AM stimuli in all areas.
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As shown in Table 1, in each cortical area, the BMFs obtained for LFP triggers were generally similar to those for MU spikes, and the only significant difference was that the LFP measure was significantly higher than that for MUs in AAF for AM tone stimulation (paired t-test, P < 0.05). The limiting rates were significantly higher for LFP triggers than for MU spikes for AAF (both AM noise and AM tone, P < 0.05) and AII (AM noise only, P < 0.05). In AAF the BMFs and limiting rates were significantly (P < 0.05) lower than those for AI and AII, whereas there was no difference between AI and AII.
Limiting rates between AM stimuli were not significantly different within a cortical area, but for both AM stimuli they were significantly (P < 0.0001) higher than for click train stimulation. This was found for all cortical areas, both for MU spikes and LFP triggers. The BMFs for LFP triggers were significantly higher for AM stimuli than for clicks in AI and AII but not in AAF. For MU spikes, the BMFs were significantly higher for AM stimuli than for clicks in AI and AII, but again not in AAF.
MEAN TEMPORAL MODULATION TRANSFER FUNCTIONS FOR PERIODIC CLICK TRAINS.
Across all recordings, the mean number of spikes during the click trains was computed for LFP triggers, LFP1 triggers, and MU spikes and shown in Fig. 7. This figure thus represents the rate-tMTFs. The LFP triggers show a weak band-pass dependence on click rate, the LFP1 triggers show a high-pass dependence (as a consequence of only taking at most 1 trigger per click) and the MU spike rate is also weakly band-pass. The highest number of spikes per click train on average was obtained in AAF.
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| FIG. 7.
Comparison of mean rate modulation transfer functions in the 3 areas. Top: results based on LFP triggers. tMTF is a weakly band-pass function of click rate and very similar for the three areas. Middle: same but only taking the 1st LFP trigger per click into account. Three functions are now high-pass functions of click rate. Bottom: results for MU data, again a weakly band-pass function is obtained. AAF on average shows the largest firing rates across all click rates.
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The mean VS for the three response measures is shown per cortical area in Fig. 8. The general trend is the same for each area and shows a very high VS for LFP1 triggers at low click rates, because of the large binwidth (1/16th of the period) at these rates. At click rates >16 Hz, the result for LFP1 triggers approaches the VS values for all the LFP-trigger conditions and for MU spikes. This suggests that the jitter in both LFP triggering and spike firing is limiting the VS at high click rates. In AI, the VS for MU spikes was equal to that for the LFP triggers <4 Hz and >16 Hz but was higher in between. In AAF, the VS was very similar for MU spikes and LFP triggers. For AII, the VS for MU spikes was slightly higher than for LFP triggers at all click rates. The highest VSs for MU recordings was obtained in AI for click rates of 8 and 9.56 Hz.
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| FIG. 8.
Comparison of the mean VS as a function of click rate in the 3 areas. Top: results for AI, the highest VS is found for the 1st LFP trigger after a click (LFP1), where only 1 trigger per stimulus period is used, the lowest is for the case where all LFP triggers are used and the MU data are in between. At high click rates, all 3 measures converge to the same level. For AAF (middle), one observes the same basic pattern but the difference between MU and LFP triggers is now very small. For AII (bottom), again the same basic finding is presented.
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The product of the number of spikes per click train and the VS results in the synchronization tMTF (Fig. 9). All the tMTFs were band-pass with the BMF at 8 or 9.52 Hz regardless of cortical area or response measure. The maximum number of synchronized spikes or LFP triggers per click train was highest in AI.
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| FIG. 9.
Comparison of the mean synchronization modulation transfer functions in the 3 areas. Top: results for LFP triggers. Number of synchronized triggers at low and high click rates is very similar in the 3 areas, but the peak number of synchronized triggers is largest in AI and about the same in AAF and AII. Taking only 1st triggers for the LFP shows basically the same result with AII slightly better than AAF. For MU spikes (bottom), again the results are qualitatively the same in the three areas but with AI producing more synchronized spikes than AAF and AII. Note that the average number of spikes per stimulus was highest in AAF (cf. Fig. 7).
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The ratio of the number of MU spikes and the number of LFP triggers was largely independent of click rate (Fig. 10A), whereas the ratio for the synchronized rate dropped somewhat >10 Hz (Fig. 10B). This again suggests that LFP triggers can be used to predict the unit response to periodic click trains up to a scaling factor depending likely somewhat on the setting of the trigger level.
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| FIG. 10.
Comparison of mean number of MU spikes and mean number of LFP triggers. Similarity of the tMTFs shown in the previous 3 figures is further illustrated in the nearly constant ratio of number of MU spikes and number of LFP triggers in the 3 areas. This suggests a simple threshold model of spike generation.
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Averaging across all units to obtain mean tMTFs such as shown in Fig. 9 inevitably is dominated by units with the highest firing rates. Thus we also normalized each single-unit tMTF on its value at the BMF and then averaged. The so-obtained mean normalized-tMTF did not differ essentially from the normalized mean-tMTF for AI but was slightly different for AAF and AII (Fig. 11). The differences suggest that in AAF and AII, units with BMFs <8 Hz and >16 Hz had on average lower firing rates than units with BMFs in between.
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| FIG. 11.
Comparison of a normalized mean tMTF for MU data with the mean of normalized MU tMTFs. For the normalized mean tMTFs, the same graphs shown in Fig. 9 were normalized to a peak level of 1. In the 2nd case, all individual MU tMTFs first were normalized, then averaged, and then the peak value again was adjusted to 1. For AI, the 2 curves are identical; for AAF and AII, some differences are noted both at low and high click rates.
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The VS and tMTF curves averaged across the single units were similar to those for the MU data. The BMF of the mean SU tMTFs was 9.52 Hz for AI and AII and 8 Hz for AAF.
MEAN TMTFS FOR AM STIMULI.
Average synchronization tMTFs based on LFP triggers evoked by AM noise and AM tone stimuli are very similar, but they are distinctly different from those for periodic click stimulation. Figure 12 shows this for each area separately. The number of LFP triggers for the click trains has been divided by two because the click trains are twice as long as the AM bursts. For the higher repetition and AM rates, the responses for all three stimuli appear to asymptote to the same level, slightly below one trigger per stimulus. The nearly linear rise in number of triggers up to AM rates of 10 Hz suggests that adaptation is not playing a big role for low modulation rates. Above 10 Hz, click responses appear to be affected stronger than AM responses, likely because the click train stimuli last longer and thus allow for more adaptation. The tMTFs to AM stimuli that do not produce a large peak but have the same response floor in these cases would tend to have a shallower slope.
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| FIG. 12.
Comparison of LFP trigger and MU-spike based tMTFs for 3 stimulus types in 3 cortical areas. A: in all 3 areas, click trains evoke  3 times more LFP triggers per stimulus (or half the stimulus in case of the click trains) for modulation rates <30 Hz. No noticeable difference was found between the 2 AM stimuli. For MU spikes, the difference in response size was smaller (B). Also in this case the click stimuli produce considerably larger responses than the AM stimuli, especially in AI and AAF. In AII, the mean number of synchronized spikes per AM tone was <0.5 across the AM frequency range. In AI, the response to AM noise was also very low.
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For MU spikes, similarly normalized, click stimulation is again more effective, but now some interareal differences between the responses to the two AM stimuli are emerging (Fig. 12B). In AI, the tMTF to the AM tones shows a clear peak at 9.5 Hz, the same frequency as for the click BMF, whereas for AM noise, the tMTF is much shallower with a broad maximum between 8 and 11 Hz. In AAF, both AM stimuli show a clear peak in the 8- to 11-Hz range, similar to that for click stimulation. In AII, the average tMTF for AM tones does show a very weak response without any preference for AM, whereas the AM noise stimulus is AM rate sensitive. Across cortical areas, AM noise is the most effective stimulus (in terms of number of synchronized spikes at the BMF) in AAF followed by AII and AI. AM tones evoke the same peak activity in AI and AAF, but at higher AM rates the activity in AAF is the largest, whereas AM tones are very ineffective in activating units in AII. The click stimuli evoke qualitatively the same tMTFs but with the number of spikes at the BMF largest in AI and smallest in AII. Note that the BMF of the mean tMTFs for AM stimuli is similar to the modal BMF and thus much smaller than the mean BMF. This is because units with high BMFs generally had lower firing rates.
ISI and autocorrelogram representation
PERIODIC CLICK TRAINS.
First-order ISI histograms and autocorrelograms (all-order ISI histograms) were calculated for all SU and separately for all MU spike trains. As an example, we show the period histograms (triggered with reference to the clicks and the basis for the tMTFs) and all-order ISI histograms for simultaneous MU recordings in the three areas with all CFs ~5 kHz. In Fig. 13A, the period histograms show visible click following 32 Hz with the largest amplitudes in the 4- to 8-Hz range in each area. In contrast, the all-order ISI histograms (Fig. 13B) show preferred activity at ISIs equal to one period of the click repetition rate only up to 9.52 Hz, possibly to 11.28 Hz with an absence of ISIs for higher click rates. This suggests that the firings locked to the clicks at these rates were isolated spikes or spike pairs that were at least more than three click periods apart. Note that the peak width at the one- and two-period marks are the same relative to the period duration, suggesting an accuracy of firing that is proportional to the time interval between clicks.
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| FIG. 13.
Examples of period histograms and all-order interspike-interval (ISI) histograms for simultaneous MU recordings in AI, AAF, and AII. CFs of the 3 recording sites were ~5 kHz. A: period histograms with clear synchronized responses 32 Hz in all areas. B: ISIs equal to the click repetition period cease to exist for click rates >9.52 Hz.
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Population ISI histograms and autocorrelograms (all-order ISI histograms) were constructed by adding the 338 individual SU ISI histograms and autocorrelograms. For the entire population, the pooled all-order ISI histogram is shown in Fig. 14A. The highest detectable preferred interspike intervals equal to one click period are for click repetition rates of 13.44 Hz (AI) and potentially for 16 Hz in both AAF and AII. Evidence for intervals equal to two periods of the stimulus is found 19.04 Hz for all three areas. Considering only first-order ISIs (Fig. 14B) also shows that, especially for "optimal" click rates such as between 8 and 11.28 Hz, ISIs equal to twice the click period frequently occur. Such firings contribute to the tMTF at those repetition rates but not to the "correct" ISI. This suggests that the period histogram method resulting in tMTFs sets the upper limit for a temporal representation of periodicity.
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| FIG. 14.
Population ISI histogram for click train stimulation obtained by adding SU ISI histograms. A: all-order ISI histograms; intervals with periodicity equal to the click period are found in all 3 areas 14.33 Hz. Evidence for intervals equal to 2 periods of the stimulus is found 19.04 Hz for all 3 areas. B: 1st-order ISIs only. It is clear that even at "optimum" click rates, a substantial portion of ISIs is equal to twice the click period.
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The mean values of the BMF and limiting rate calculated for the all-order ISI histogram representation for single units are very similar to corresponding values obtained from the temporal modulation transfer functions (Table 1). For those single-unit recordings where the all-order interval histogram showed visible peaks at lag times equal to one stimulus period (in 178 of the 338 units for which a tMTF was calculated), scattergram plots of BMFs and limiting rates from tMTFs and from all-order ISI histograms (Fig. 15, A and B) show that these measures are positively correlated in all three areas (P < 0.05). The regression lines are labeled with the area name. For this subgroup of single units, the BMF for the all-order interval histogram was significantly lower (~1.7 Hz; P < 0.0005) than the BMF from the tMTF in all areas. A paired t-test showed no significant differences in the limiting-rate measures in either of the three cortical areas. Thus the mean values for BMF and limiting rate obtained with the tMTF and the all-order ISI histograms are very similar.
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| FIG. 15.
Comparison of BMFs and limiting rates estimated from tMTFs and autocorrelograms (ACH). Both measures are correlated positively, but the BMF for the all-order interval histogram was ~1.7 Hz lower than the BMF from the tMTF across all areas. No significant differences in the limiting rate measures in any of the 3 cortical areas were found.
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AM STIMULI.
Whereas the all-order population ISI histograms for click train stimulation (cf. Fig. 14A) are very similar for the three cortical areas, clear interareal differences are found for AM noise and AM tone stimulation. Figure 16A shows the population results for AM noise based on individual SU all-order ISI histograms; in AI, ISIs equal to the AM period are found for rates 38 Hz, whereas for AAF and AII, this is at most 16 Hz. For AM tones (Fig. 16B), in AI ISIs equal to the AM period are again found 32-38 Hz and in AAF at most 16 Hz, whereas in AII hardly any preferred intervals are seen. This does not mean that no single units can be found that show ISIs equal to a stimulus period; in fact the mean and SD shown in Table 1 clearly indicates this presence. The difference is that only 10% of the units in AII showed detectable periodic ISIs for AM tones, whereas nearly all did for AM noise. The mean BMFs and limiting rates calculated from the individual all-order ISI histograms (Table 1) are strikingly lower than for the values obtained from the tMTFs (except the BMFs for AAF), but somewhat similar to those obtained from the population ISI histogram. As can be seen, the SDs for the limiting rates for AM stimuli are much larger (and also relative to the mean) than for click stimulation.
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| FIG. 16.
Population ISI histogram for AM noise and AM tone stimulation obtained by adding SU ISI histograms. A: for AM noise in AI, detectable ISIs equal to the AM period can be found 38 Hz, whereas in AAF and AII this upper limit is at most 16 Hz. B: for AM tone stimulation in AI, detectable ISIs equal to the AM period again can be found 38 Hz, whereas in AAF the upper limit is at most 16 Hz, and in AII there is no clear representation in this population response.
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ALL-ORDER ISIS COMPARED FOR CLICKS AND AM STIMULI.
An illustration of the dependence of all-order interval histograms on stimulus type for three selected SU recordings is shown in Fig. 17, A-C. For primary auditory cortex (Fig. 17A), the results for click trains (click rates from 1 to 32 Hz) show that rates between 1 and 11.3 Hz produce preferred ISIs equal to the click interval, whereas for AM noise and AM tones (AM rates from 2 to 64 Hz) this range is shifted upward from ~5-8 to 32 Hz. Clearly low AM rates for noise and tones are not effective in generating a temporal representation of the AM period. In contrast to high click rates, high AM rates are much more effective likely because AM stimuli do not induce the strong postactivation suppression that is commonly found for clicks (cf. Fig. 2). For AAF (Fig. 17B), the picture is somewhat different. For clicks, preferred ISIs equal to the interclick interval are again found 11.3 Hz, but for AM noise and AM tones, the upper boundary is only slightly higher at ~16-19 Hz. Again a strong band-pass dependency is found for AM noise, but less so for AM tones. In AII (Fig. 17C), one of the limited number of examples for which AM tones produced some clear ISIs in single-unit activity at the AM period (mostly between 8 and 16 Hz), the result was not overly clear for the other stimuli: for AM noise and for click trains only rates ~8 Hz were effective.
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| FIG. 17.
MU all-order ISI histograms for click and AM stimuli in 3 areas. A: results for AI for the same set of units. Note the different repetition rate range for clicks compared with the AM stimuli. Upper limits for period-locked intervals for clicks are 13.44 Hz and for the AM stimuli 32 Hz. B: results for AAF. Here the upper limit for click stimulation is 16 Hz and for AM stimuli around 32 Hz. C: results for AII. One notices the poor representation of stimulus periods in the histograms. For click stimulation, ISIs equal to 1 period stand out only for rates at 8 and 9.52 Hz; for AM noise the range is limited from 6 to 11 Hz and for AM tone from 8 to 13 Hz.
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To construct an analogue to the tMTF based on ISIs, the maximum in the three bins surrounding and including the AM period for the all-order ISI population histograms was computed for all stimuli and areas. A correction for the background in the autocorrelogram was carried out by subtracting the mean of the four bins midway between period 1 and period 2. The stimulus driven number of periodic intervals in the all-order ISIs then was plotted (Fig. 18) as a function of click or AM rate. For click stimulation, the dependence on the repetition rate is qualitatively the same for all three areas with best response in AI. There is a sharp decrease >11.3 Hz and hardly any periodic intervals for repetition rates >13.4 Hz. In all three areas, there is a dip in the number of ISIs around a repetition rate of 6.5 Hz. Alternatively one could say that the number of ISIs is highest at 9.52 Hz and its subharmonic of 4.76 Hz (for AI and AII), and slightly lower at 8 and 4 Hz for AAF. This dip might be related to an interaction between depression periods in ongoing spindle activity and the activation introduced by the clicks with rates between 4 and 9 Hz (Eggermont and Smith 1995a).
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| FIG. 18.
Temporal modulation transfer functions derived from the population all-order ISI histograms. These functions were obtained by averaging the 3 bins around and including the 1 period value and subtracting the mean of 4 bins halfway in between period 1 and period 2. For click stimulation, the response in all 3 cortical areas is very similar with hardly any intervals >13 Hz. For AM noise stimulation, intervals are detectable up to ~30 Hz, and all areas respond with AAF providing about twice the number of ISIs than AII. For AM tone stimuli, AAF stands out, whereas only weak representation of the modulation period is found in AI and AII. Upper limit appears to be ~13 Hz.
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For AM noise stimulation, the elicited maximum number of periodic ISIs is largest in AAF than in AI and smallest in AII. There is a rapid drop in the number of ISIs >11.3 Hz for AAF and AII, whereas in AI the number only slowly decreases for higher AM rates. Again a dip in the number of ISIs is noted, this time at 4.76 Hz; however, there is no indication of a harmonic structure in the dependency on modulation rate. AM tone stimulation only produces significant numbers of periodic ISIs in AAF with very few intervals >13 Hz, whereas AI and AII both stay close to the background level. Thus with the exception of AM noise in AI, the highest AM rate for a significant number of ISIs is ~13 Hz. Comparison of, for instance, Figs. 11 and 12 (see also Table 1) shows that the BMFs and the limiting rates are generally higher for the tMTF-based characterization. This suggests that individual stimulus-locked spikes are evoked for rates >13 Hz and are distributed across stimulus duration but that there are no doublets with an ISI equal to the AM period generated for these higher AM rates.
TEMPORAL MODULATION TRANSFER FUNCTIONS AND LATENCY.
For single units, the first-click latencies obtained at peak equivalent SPL levels of 75 dB were correlated positively with the minimum latency for tone pips (significant in all 3 areas, P < 0.001) and were on average 1.3 ms longer than the minimum latency for pips (which typically was found also at 75 dB SPL, the highest intensity used). First-click latencies were shortest (P < 0.0001) in AAF and about equal in AI and in AII (Table 3). Minimum tone pip latencies were also shortest (P < 0.0005) in AAF and again about equal in AI and AII. The BMF for single units in response to periodic click trains was correlated positively with the first-click latency (Fig. 19A). The regression lines are labeled with the area name. This correlation was significant for AAF but not for AI and AII. The limiting rate was correlated positively with first-click latency for AI and AAF but not for AII (Fig. 19B).
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| FIG. 19.
Dependence of SU BMF and limiting rate on the latency of the response to the 1st click and on the temporal filter delay. A and B: BMFs (in AAF) and limiting rates (AI and AAF) are significantly positive correlated with 1st-click latency.
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CORRELATION OF SPECTRAL AND TEMPORAL PROPERTIES.
For all recording sites, the LFP trigger and multiunit intensity-frequency response areas were measured. Multipeaked response areas for MU recordings were found in 8% of recording sites in AI, for 26% of recording sites in AAF, and for 9% of recording sites in AII. The CF and bandwidth (BW) at 20 dB above threshold at CF was determined for both LFP and MU recordings. At the same recording sites, the CF values were, with some exceptions, very similar for LFP and MU (Fig. 20A). In 10 cases the differences in CF were >1 octave. However, the 20 dB BW was significantly larger in AI and AAF for frequency tuning of LFP triggers than for MU spikes recorded on the same electrode (Table 2, Fig. 20B). This amounted to 0.4 octaves (P < 0.01) for AI and to 0.8 octaves (P < 0.0001) in AAF. In AII the bandwidths were not significantly different for LFP-trigger- and MU-spike-based tuning curves. Between cortical areas, the MU tuning curve bandwidth in AAF and AII was not significantly different (P = 0.8). The BW in AAF was slightly higher by 0.3 octaves than in AI (P = 0.3). In contrast, the BW in AII was ~1 octave higher than in AI (P < 0.001). For the LFP measure, the tuning curve BW for AAF and AII were the same. The BWs in AAF and AII were 0.7 octaves higher than in AI (P < 0.0005).
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| FIG. 20.
Correlation of frequency-tuning curve properties obtained for MU spikes and LFP triggers. Whereas the CF of the recording site extracted from MU and LFP data are generally the same, the tuning curve bandwidth at 20 dB above threshold is nearly always substantially larger for LFP triggers.
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Abstract
Introduction
Abstract
