Sound-Induced Synchronization of Neural Activity Between and Within Three Auditory Cortical Areas

Jos J. Eggermont

Department of Physiology and Biophysics and Department of Psychology, Neuroscience Research Group, University of Calgary, Calgary, Alberta T2N 1N4, Canada


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Eggermont, Jos J.. Sound-Induced Synchronization of Neural Activity Between and Within Three Auditory Cortical Areas. J. Neurophysiol. 83: 2708-2722, 2000. Neural synchrony within and between auditory cortical fields is evaluated with respect to its potential role in feature binding and in the coding of tone and noise sound pressure level. Simultaneous recordings were made in 24 cats with either two electrodes in primary auditory cortex (AI) and one in anterior auditory field (AAF) or one electrode each in AI, AAF, and secondary auditory cortex. Cross-correlograms (CCHs) for 1-ms binwidth were calculated for tone pips, noise bursts, and silence (i.e., poststimulus) as a function of intensity level. Across stimuli and intensity levels the total percentage of significant stimulus onset CCHs was 62% and that of significant poststimulus CCHs was 58% of 1,868 pairs calculated for each condition. The cross-correlation coefficient to stimulus onsets was higher for single-electrode pairs than for dual-electrode pairs and higher for noise bursts compared with tone pips. The onset correlation for single-electrode pairs was only marginally larger than the poststimulus correlation. For pairs from electrodes across area boundaries, the onset correlations were a factor 3-4 higher than the poststimulus correlations. The within-AI dual-electrode peak correlation was higher than that across areas, especially for spontaneous conditions. Correlation strengths for between area pairs were independent of the difference in characteristic frequency (CF), thereby providing a mechanism of feature binding for broadband sounds. For noise-burst stimulation, the onset correlation for between area pairs was independent of stimulus intensity regardless the difference in CF. In contrast, for tone-pip stimulation a significant dependence on intensity level of the peak correlation strength was found for pairs involving AI and/or AAF with CF difference less than one octave. Across all areas, driven rate, between-area peak correlation strength, or a combination of the two did not predict stimulus intensity. However, between-area peak correlation strength performs better than firing rate to decide if a stimulus is present or absent.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Perception is important in the formation of a mental representation of the environment. Because sensory systems likely extract distinct features of the environment with spatially distinct sets of neurons, perception requires a regrouping of those features that belong together (Singer and Gray 1995). In that respect, "components of an auditory scene appear to be perceptually grouped if they are harmonically related, start and end at the same time, share a common rate of AM, or if they are proximate in time and frequency" (Cooke 1993). Acoustic transients, such as plosive consonants, can be viewed as analogous to visual contours and thus may represent the gross shape of a sound. Large parts of auditory cortex, regardless of their frequency tuning, respond vigorously to such transients (Phillips 1993). It is likely that a combination of temporal aspects of sound and a common harmonic structure may provide for "feature binding" on which perceptual grouping can be based. Feature binding may be based on enhanced neural firing synchrony between groups of neurons that extract different stimulus features (Singer and Gray 1995; von der Malsburg 1995). The best candidates for binding features extracted in different cortical areas by neural synchrony are those features that produce the largest changes in the correlation of activity between neurons in those areas compared with a nonstimulus condition. Among those features, stimulus onsets are the prime candidate (Brosch and Schreiner 1999; Eggermont 1997).

In cat auditory cortex, onset responses to transient sounds consist of short bursts, typically two to five spikes long, that are generally followed by postactivation suppression (Bowman et al. 1995; Eggermont 1994; Phillips and Sark 1991). As a consequence, neural activity at different recording sites tends to be correlated. The interneural synchrony that potentially is involved in the binding process may result from a correlation in modulated firing rate or from a precise correlation in the timing of neural discharges. If the modulation is fast, i.e., in case of narrow poststimulus time histograms (PSTH), the distinction can be difficult. For auditory cortical areas outside primary auditory cortex (AI), the time scale of neural correlation has not been investigated, either within individual areas or between different cortical areas. It is also not evident whether there is information in the strength of the onset cross-correlation histogram (CCH) that cannot be obtained from the onset firing rates. Potentially, a combination of firing synchrony with firing rate may increase the representational capacity of neural populations.

Steady-state sounds generally do not result in cortical firing rates that exceed spontaneous activity but the correlation of neural activity is enhanced during tones (deCharms and Merzenich 1996) and noise (Eggermont 1997). For these conditions, the bivariate distributions of firing rate and firing synchrony distinguished sound onsets from steady-state aspects and spontaneous activity (Eggermont 1997). Correlation of firing activity can represent the presence of a stimulus in potentially two ways. First, correlation can be a monotonically increasing function of stimulus level and in that way represent this level and thus the presence of a stimulus. Second, the correlation strength can have one level for spontaneous activity and another, higher, one for stimulus-induced activity. In the latter case, the correlation strength only points to the presence or absence of stimulation. For perceptual binding to occur on basis of correlated activity, either possibility is sufficient.

This paper presents simultaneous recordings from three auditory cortical areas---AI, anterior auditory field (AAF), and secondary auditory cortex (AII)---with emphasis on onset activity to tone pips and noise bursts. The paper addresses the following questions: does stimulation induces changes in neural correlation that are different from neural correlation during silence? Is the strength of stimulus-induced correlation dependent on stimulus intensity? Are spontaneous and stimulus-induced correlations dependent on the difference in characteristic frequency of the units? Is spontaneous and stimulus-induced correlation strength changing across area boundaries? Can correlated neural activity represent stimulus level or is it limited to sign the presence or absence of a stimulus? Is the strength of the interareal correlation useful in auditory feature binding?


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The care and the use of animals reported on in this study was approved by the Life and Environmental Sciences Animal Care Committee of the University of Calgary (No. P88095).

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 about one-half hour they received an intramuscular injection of 25 mg/kg of ketamine (100 mg/ml) and 20 mg/kg of pentobarbital sodium (65 mg/ml). Lidocain (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 was 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 · h-1 of ketamine. The wound margins were infused every 2 h with lidocain, 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

Stimuli were generated in MATLAB and transferred to the DSP boards of a TDT (Tucker Davis Technologies) sound-delivery system consisting of D/A boards, mixers, anti-aliasing filters, programmable attenuators, and power amplification. Acoustic stimuli were presented in an anechoic room from a speaker (Fostex RM765, flat <= 12 kHz then 3 dB/octave roll off to 25 kHz, measured at the cat's head) 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-burst, 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, each presented five times, 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, 225 noise bursts (1-s duration followed by 2 s of silence) were presented.

Recording and spike separation procedure

Three tungsten microelectrodes (Micro Probe) with impedances between 1.5 and 2.5 MOmega 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 waveform classes, each assumed to represent a particular neuron, were stored and coded for display.

The single-electrode recordings always consisted of spikes from more than one unit. Using spike separators to sort out the single-unit spike trains from a multiunit record has the inherent weakness of a "dead time" in the spike sorter. The result is that within one spike duration there will be no coincidences in the correlograms for single-electrode pairs. As a consequence the estimates of the correlation coefficients may be lower than they really are because spikes occurring during the spike sorter's dead time (~0.8 ms) may have failed to contribute to the correlation peak. Spikes of two units occurring nearly simultaneously would be classified as a distinct class if their time relationship did not vary through the course of the experiment. Because latencies tend to jitter, it was usually obvious that this class was not single unit. Another factor that could influence the cross-correlation results is misclassification of nonsimultaneously occurring spikes: in this case spikes with different waveforms are put in the same class because of amplitude variations. A random fraction (~10%) of spike waveforms was printed at the end of a recording. From this printed record, the waveforms assigned to a class, but clearly of different shape, were counted. If there are random misclassifications and only a fraction of the spikes is classified wrongly (say <5%), the effect is marginal (as simulations have shown) (unpublished data). Classes with more than this number of misclassifications were not further considered as single units. More serious problems could occur when spikes occur in bursts. Burst spikes typically show a gradual decrease in amplitude with spike order, and the smaller ones could be classified as another neuron. Effect of burst firing could result in a potential misclassification of second or third spikes in bursts. This was not observed in the single units that were used in the analysis.

The boundary between AI and AAF was explored by taking a series of multiunit measures 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 by its location and by the broader tuning curves and different response patterns (latency and bursting activity) 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. This, however, did not guarantee that the CF, the frequency with the lowest threshold, was also within 0.5 octave. Recordings were made between 600 and 1,200 µm below the cortex surface.

Criteria for including recordings in the correlation analysis

Trigger levels were generally 75-100 µV negative to baseline. The background noise level including smaller spikes never exceeded 50 µV peak to peak. The multiunit data presented in this paper represent only well-separated single units. 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 (MU) recording were added again to a form a MU spike train. Thus the term MU in this study refers strictly to a cluster of separated single units. The reason for forming MU clusters is the need for a sufficient number of spikes for the correlation analyses. We thus consider the following pair recordings for the cross-correlogram calculations: single-unit pairs on either the same electrode (SSU) or on different electrodes (DSU) or multiunit pairs on different electrodes (MU). The consideration of performing SSU or DSU pair correlations was only based on the average firing rate of the units; units with onset firing rates <1 spike/s were not considered as experience has shown that their CCHs are sparse and generally not significant. MU recordings with a sufficient number of spikes to produce a reliable CCH could be obtained in cases where none of the SUs individually would meet the criterion.

Data analysis

To obtain frequency-tuning curves, the number of action potentials in the first 100 ms after tone-pip onset was counted for each stimulus intensity. The counts for three adjacent frequencies were combined to reduce variability and divided by number of stimuli. This resulted in 27 frequencies covering 5 octaves so that the final frequency resolution was ~0.2 octaves. The frequency-tuning curve was defined at 25% of the maximum firing rate. The threshold was determined as 5 dB below the intensity that produced visible time-locked responses to the tone pip, i.e., between the stimulus that produced a response and the one that did not. This criterion was more sensitive than the rate criterion for units with high levels of spontaneous activity (cf. Fig. 2). In this example, there is visible locking down to 5 dB SPL for the AII recording, down to 25 dB for the AAF recording and down to 15 dB for the AI recording. The assigned thresholds would be 5 dB lower.

Periodic stationarity, i.e., the replication of firing activity as a function of time after the start of identical stimuli, of the recordings was checked by calculating averages of firing rates and cross-correlations over randomly selected segments of the total record. Nonstationary recordings were not included because they may induce artifactual correlations. Cross-correlograms were calculated using Stranger (Biographics) software for three epochs: for the first 100 ms after stimulus onset, for the last 0.5 s of the 0.9 s duration post tone-pip period, and for the last second of the 2-s poststimulus period in case of noise-burst stimulation. The analysis resulted in the strength of the correlation as a peak coincidence rate (in spikes/s); however, for reasons of compatibility with our previous publications, this was converted into a cross-correlation coefficient (R) using
<IT>R</IT><IT>=&Dgr;</IT><IT>T</IT> <FR><NU><IT>firing rate unit 1 </IT>(<IT>peak coincidence rate−firing rate unit 2</IT>)</NU><DE><RAD><RCD><IT>firing rate unit 1 ∗ firing rate unit 2</IT></RCD></RAD></DE></FR> (1)
Delta T is equal to the bin size. The peak coincidence rate is defined as the peak number of spikes of the second unit in a bin per spike of the first unit divided by the bin size. The peak cross-correlation coefficient was considered significant if its peak was within 20 ms from the zero-lag point and its value exceeded the baseline by 3 SD. For small firing rates (<10 spikes/s) and small bin sizes, SD = (N)-0.5, with N the number of bins in the recording (Eggermont 1992). Only significant R values were used. The 95% confidence levels for R were calculated after Fisher's R-to-z transformation (Hoel 1971). Then the 95% boundaries for z were calculated and transformed back to R values. In addition to peak coincident rate and cross-correlation coefficient, we also use the number of synchronized spikes per trigger spike within the cross-correlogram peak. CCH peak widths are expressed in milliseconds and were measured at half-amplitude between peak and baseline. The number of spikes in the CCH (area) also was used to quantify the strength of the correlation.

Cross-correlograms were calculated across all tone-pip frequencies and for a 100-ms window after tone-pip onset. The main reason for this is that size of receptive fields could differ substantially both in the frequency domain and in the latency and duration of the neurons' responses. Both factors were intensity-level dependent. The value of the peak coincidence rate is likely only determined by the number of coincidences produced by the frequencies within the unit's receptive fields, whereas the background is largely the result of activity outside the unit's receptive fields. This was tested by computing cross-correlograms for more limited time windows: by using all frequencies but only a window between 10 and 40 ms after tone-pip onset, by using only frequencies within the receptive field (this is intensity dependent) but with a 100-ms window, and by combining the restrictions in the two previous conditions. One hundred randomly selected pairs were obtained for this test using the random function in Statview. The peak coincident firing rates obtained for the three conditions were not significantly different (P < 0.0001), and their values were very strongly correlated: R2 = 0.96 for condition 1, R2 = 0.61 for condition 2, and R2 = 0.58 for condition 3. The only thing that changes is the 3 SD significance level, which increases for smaller windows. Thus making the window too small may actually result in cross-correlograms that are not significantly different from the prediction based on the firing rate of the units within the window. This suggests that in general the product of the two individual recording's PSTHs can predict the CCH. For the randomly selected pairs, there was no difference in their dependence on stimulus intensity for those three conditions compared with the original full time and frequency window. To avoid windows that have different duration and frequency spans for different stimuli and intensities, the cross-correlations reported here are calculated across all frequencies and for a 100-ms window after tone-pip onset.

Stimulus-induced firings generally enhance the amount of correlation between the two spike trains over that obtained under spontaneous firing conditions. During stimulation the CCH consists of a component due to stimulus correlation and one present during spontaneous firing, the spontaneous correlation. This latter component can be estimated under spontaneous firing conditions and is very low for auditory cortical neurons (Eggermont and Smith 1996a). This spontaneous correlation may be the result of cortical network properties resulting in synchronous local field potentials, such as spindles, or of direct horizontal neural connections (Eggermont and Smith 1995a,b). The stimulus-induced correlation (Rstim) can be estimated with the shift predictor representing the amount of correlation that can be attributed to the locking of the neuron's firings to the stimulus (Perkel et al. (1967). Under the assumption of additivity, the difference of the CCH and the shift predictor would be equal to the spontaneous correlation. In this study, stimulus induced correlation was defined as the difference between onset correlation and poststimulus correlation.

All statistical analyses were performed using Statview 5 (SAS Institute). Illustrations were made with Powerpoint Software (MicroSoft). In several graphs, locally weighted scatterplot smoothing curves (LOWESS) (Cook 1998) are drawn in. Note that R will be used for the peak correlation coefficient of the CCH and r for the correlation coefficients resulting from statistical comparisons between CCH parameters.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Recordings were made in 24 adult cats with either two electrodes in AI and one in AAF or one electrode each in AI, AAF, and AII. A total of 152 MU pairs, 99 SSU pairs, and 100 DSU pairs (only for tone pips), with sufficient onset and poststimulus firing rates and resulting in a significant correlation for at least one stimulus condition, were analyzed. CCHs were obtained for tone pips, noise bursts, and silence (i.e., poststimulus) and as a function of intensity level (15-75 dB SPL). Across stimuli and intensity levels, the total number of significant stimulus onset CCHs was 1,155 of 1,868 calculated (62%). Of the 1,155 significant onset CCHs, 593 were recorded for tone-pip stimulation and 562 for noise-burst stimulation. The number of significant poststimulus CCHs was 1,081 of 1,868 calculated (58%), 544 after tone pips and 437 after noise bursts.

In 13 cats, the responses for the same units to both tone pips and noise bursts were analyzed, in 2 cats only tone-pip stimuli were presented, and in 9 cats the firing rates for tone pips were too low and the analysis was restricted to noise bursts. Only correlations that were significant were used in subsequent analyses.

Individual cross-correlograms

Figure 1 shows CCHs, for a 1-ms binwidth and lead and lag times of 50 ms, obtained for simultaneous recordings at two sites in AI and one in AAF. Figure 1, top, shows the CCHs between a single unit in AI (unit 22) and another SU from the same electrode (unit 23). In Fig. 1, middle, CCHs are shown for the same single unit in AI and another AI unit on a separate electrode (unit 13). Figure 1, bottom, shows CCHs between unit 22 in AI and unit 33 in AAF. The CCHs are calculated for 100-ms time windows after tone-pip onset (left) and for the last 500 ms of the post-tone-pip period (right). Correlation strength is quantified by the firing rate (spikes/s) of the second unit for every spike in the first unit (unit 22). Dividing this peak firing-rate by 1,000 results in the peak probability of a spike of unit 2 in a 1-ms bin given a spike of unit 1. For the top left correlogram, this peak probability is 0.077. In addition, if every spike on unit 22 always produced a spike in the second unit in the same bin (say at -3 ms), the strength of the correlation would be 1,000 spikes/s (because of the 1-ms binwidth) and the peak probability would be 1. In that case, the peak correlation coefficient also would be close to 1 (for low firing rates). The actual peak correlation coefficients, calculated according to Eq. 1, are indicated in the top right corner of each plot. The 99% confidence boundary for a peak correlation occurring by chance based on overall firing frequency of the two units in the analysis window is shown by - - -. Peaks that exceed the 99% confidence boundary are considered significant. By chance alone 1 of the 101 bins in the CCH thus could be significant; however, we only considered peaks occurring between -20 and +20 ms to actually represent correlated activity. These peaks generally showed several significant bins. The example in the bottom right column, representing the correlation between unit 22 (AI) and unit 33 (AAF), has nine significant bins in that time window and was considered significant.



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Fig. 1. Examples of within- and between-area cross-correlograms (CCH) during stimulation and under poststimulus conditions. Top: single-electrode CCHs; middle: within-area dual electrode CCHs; bottom: between-area CCHs. Left: CCHs during tone-pip onset; right: during the last 500 ms of silence after the tone pips. Strength of the correlation is presented both as firing rate of the 2nd unit for each spike of the trigger unit (along vertical axes) and as a cross-correlation coefficient (top right corner of each CCH). Binwidth is 1 ms; - - -, 99% confidence boundaries for a peak to occur by chance based on the firing rates of the units in the analysis window.

For this example, CCH peaks between units on the same electrode are stronger and narrower. CCHs for units on separate electrodes within AI are broader, and those between units in two areas are still broader. Between-area CCHs calculated for the poststimulus period have a much broader central peak, and the correlation strength is lower.

To illustrate the dependence of the CCH on receptive field overlap and tone-pip level, a complete series of a three-electrode recording for tone-pip stimulation is shown in Fig. 2. Figure 2, top, represents a double-unit recording in AII, middle is a triple-unit recording in AAF, and bottom is a double-unit recording in AI. Each box represents a dot raster with a time base of 0-100 ms after tone-pip onset; the stimulus frequency is shown vertically and represented logarithmically between 625 Hz and 20 kHz. The CF for the AI units is 4.5 kHz; for the AAF recording, it is 2.6 kHz, and for the AII recording, it is 5 kHz. Tone-pip levels decrease from 75 dB SPL for the left-most column in 10-dB steps to 5 dB SPL for the right-most column. Visible locking of spikes to the tone pips is found down to 5 dB SPL for the AII recording, down to 25 dB for the AAF recording, and down to 15 dB for the AI recording. The assigned thresholds are 5 dB lower. Figure 3, top, shows the R values as a function of intensity for tone-pip onset (left) and post-tone pip (right). The poststimulus correlation is fairly constant, as expected, with potential exceptions for 65 dB SPL in the AI × AAF value, and the 5 dB SPL value for the AI × AII correlation. The onset correlation increases slightly with intensity for the AAF × AII condition but is actually highest at 15 dB SPL for the two other conditions. Comparison of the AI × AAF correlation for tone-pip onset and poststimulus conditions suggests an increase in R for decreasing intensity toward the poststimulus level. The geometric mean of the onset firing rates, equal to the square root of the product of the firing rates and related to the predictor for R under the condition of independence, of the units involved in the correlation (bottom) increases with stimulus intensity. For the poststimulus conditions, it is relatively constant. And for intensities <= 50 dB SPL, there is hardly a difference between the stimulus and poststimulus conditions firing rates. The correlation coefficients are lowest for the condition with the least overlap in frequency tuning (AAF × AII) and highest in case of smaller distances in CF.



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Fig. 2. Dot displays showing simultaneously recorded activity from 3 cortical areas in response to tone pips of various intensity levels. Vertical axes represent tone pip frequency on a logarithmic scale. Horizontal axes represent time after tone-pip onset. All recordings are composed of multiunit activity consisting of 2 or 3 well-separated single units. Individual units recorded from at each electrode had very similar response properties.



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Fig. 3. Between-area multiunit cross-correlation coefficients and geometric mean firing rates of the units in the correlation pairs as a function of tone-pip intensity during stimulus onset and after the stimulation for the recording from Fig. 2. Cross-correlation coefficients are highest between recordings with overlapping frequency-tuning curves, such as primary and secondary auditory cortex (AI and AII). Correlation coefficients also appear to be high for the spontaneous activity between the recording in anterior auditory field (AAF) and AI, and consequently also for the low-intensity onset conditions. Geometric mean of the firing rates increases with stimulus level.

Interactions among cortical area, stimulus type, and recording type

For the three-area recording condition, mean CCH peak correlation coefficients and widths (±1 SD), are presented in Figs. 4 (for SSU) and 5 (for MU) per cortical area involved and separately for tone pips and noise bursts, as well as for onset and poststimulus conditions. The number of pair correlations for each estimate, indicated in the lower graphs, were the same for R and peak width but generally different for tone pips and noise bursts. A more restricted analysis, for only those recordings where both tone pips and noise bursts were presented, was performed as well. This reduced the number of pair correlations involved in the calculations for the noise bursts to that for the tone pips but did not change the mean values significantly. Thus the data presented here are not biased by involving different cats for the two stimulus conditions. One observes that the peak correlation coefficients are about twice as high for SSU (Fig. 4) than MU (Fig. 5; coinciding with a within- and between-cortical area distinction) and that the difference for stimulus onset and poststimulus correlation is much smaller for SSU than for MU. Peak widths are about three to four times larger for MU (note the different scale) than for SSU for both onset and poststimulus condition. Onset Rs were significantly higher for noise burst compared with tone pips for SSU as well as MU (P < 0.0001). Onset and poststimulus CCH peak widths were also significantly different for tone pips or noise bursts (P < 0.0001). Peak widths for the poststimulus conditions were significantly larger after stimulation with tone pips compared with noise bursts. This is likely the result of a shorter time between stimulus offset and start of the poststimulus window for tone pips (450 ms) than for noise bursts (1 s), allowing some residual effect of the previous tone-pip stimulation on the poststimulus firings. The differences for R between the single- and the dual-electrode pairs was significant (P < 0.0001). An ANOVA showed no interaction between the onset R for recording type (or area) and stimulus type (P = 0.24). Because of the inherent dependence of recording type on cortical area (between areas was never SSU), no interaction could be determined.



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Fig. 4. Mean ± SD peak cross-correlation coefficients (top) and peak widths (bottom) for single-unit single-electrode pairs for tone-pip and noise-burst stimulation (left) and for poststimulus conditions (right). Numbers above each bar represent the number of pair correlations involved.



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Fig. 5. Mean ± SD peak cross-correlation coefficients (top) and peak widths (bottom) for multiunit between-area pairs for tone-pip and noise-burst stimulation (left) and for poststimulus conditions (right). Numbers above each bar represent the number of pair correlations involved.

Comparison of dual electrode single-unit and multiunit pair correlograms

Each MU pair correlogram can be split up in a number of DSU pair correlograms. The relationship between the parameters of these CCHs for tone pips was investigated for DSUs consisting of single units with at least a 1-spike/s onset response. Figure 6A shows the comparison for both the onset and poststimulus R. A power curve fit resulted in exponents of 0.54 for onset and 0.56 for poststimulus conditions. The relationship is approximated here by drawing a power function with exponent 0.5, suggesting that R(SU) is approximately proportional to the square root of R(MU). A pair-wise comparison indicated that the mean R(SU) was significantly smaller (mean difference 0.02, P < 0.0001) than R(MU). For the poststimulus condition, the difference was much smaller at 0.004 but still significant (P < 0.0001).



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Fig. 6. Scattergrams of single-unit pair and multiunit pairs correlation strength (A) and single-unit pair and multiunit-pair CCH peak width (B). Single-unit-pair parameters were isolated from and compared with their respective multiunit pairs. Single-unit peak correlation coefficients are proportional to the square root of the multiunit peak correlation coefficients. Peak widths for both conditions were not significantly different.

CCH peak widths for DSU and MU conditions (Fig. 6B) were significantly correlated for both stimulus onset (P < 0.0001) and poststimulus conditions (P < 0.0001), and a pair-wise comparison did not show a significant difference in the values (P > 0.1).

Spontaneous and stimulus-induced correlation is different for within- and between-area unit pairs

ANOVA showed a highly significant effect of area (P < 0.0001) on R for stimulus onsets. The largest mean onset correlation was found for the within-area conditions, largest in AII and AAF and slightly smaller in AI. Post hoc Scheffe tests showed that the Rs computed for the three possible between-area MU pairs, however, were not significantly different and that the Rs for within-area SSU pairs were also not different for the three areas. For the between-area correlations, the largest was between AI × AAF, but differences were again small and not significant.

For the poststimulus conditions, ANOVA showed no interaction between preceding stimulus type and recording type (P = 0.5). For the between-area correlations, the largest was between AI × AAF, but differences were minute. Post hoc Scheffe tests showed that the three within-area correlation strengths were not significantly different and neither were the between-area values for the poststimulus conditions.

This suggests that within-area SSU correlations can be combined and also the between-area dual electrode MU correlations. Figure 7 shows results for the three essentially different recording conditions: single-electrode single-unit (SSU) pairs combined across the three within-area conditions, dual-electrode (MU) pairs in AI, and dual-electrode (MU) pairs combined for the three between-area conditions. The number of pair correlations involved is indicated in the lower part of the graphs; the number for tone pips was the same for onset and poststimulus conditions. One observes that the dual electrode pair correlation in AI takes a position intermediate to the other two for correlation strength, regardless of stimulus condition. In contrast, the peak widths for the dual-electrode pairs are similar for within and between area CCHs. Thus both stimulus-onset and poststimulus R decreases with distance, exemplified by the change from the single-electrode pair value to the dual electrode pair value in AI. This is followed by a further decrease in R across area boundaries. The differences between the Rs for single- and dual-electrode pairs are much larger for poststimulus conditions. CCH peaks were narrow for the single-electrode pairs and three to four times as wide for dual-electrode pairs, both within-area as well as between-areas. Except for the SSU condition, where no difference was found, peak widths were significantly smaller in the stimulus onset condition (P < 0.0001).



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Fig. 7. Mean ± SD peak cross-correlation coefficients (top) and peak widths (bottom) for single-unit single-electrode pairs (within), dual-electrode pairs in AI, and dual-electrode pairs between areas for tone-pip and noise-burst stimulation (left) and for poststimulus conditions (right). Numbers above each bar represent the number of pair correlations involved.

Onset and poststimulus correlation coefficients were correlated negatively with CCH peak width. Regression lines showed a similar slope for onset and poststimulus conditions
log (<IT>R</IT><SUB><IT>onset</IT></SUB>)<IT>=</IT>−<IT>1.032−.018 ∗ peak width </IT>(<IT>onset</IT>)<IT>; </IT><IT>r</IT><SUP><IT>2</IT></SUP><IT>=0.41</IT>
and
log (<IT>R</IT><SUB><IT>post</IT></SUB>)<IT>=</IT>−<IT>1.342−.017 ∗ peak width </IT>(<IT>post</IT>)<IT>; </IT><IT>r</IT><SUP><IT>2</IT></SUP><IT>=0.20</IT>
Both correlations were significant at P < 0.0001. Thus the narrowing of a relatively wide CCH peak will generally also mean an increase in the value of R. The similarity of the dependence for onset and poststimulus conditions suggests that this inverse relationship is not the result of a modulating effect of the stimulus. An ANOVA of the peak area (the total number of synchronized firings per firing in the trigger unit) by stimulus type and recording type did not show an interaction but did show a significant difference between the SSU condition and the dual-electrode conditions (P < 0.0001). The SSU correlograms showed a much smaller area (mean, 0.34 spikes) than the dual AI electrode pairs (mean, 0.52 spikes) and the between-area pairs (mean, 0.78 spikes). The number of synchronized spikes in the onset and poststimulus CCH peaks was not different for the within-area SSU, and dual-electrode in AI conditions, however, was significantly different for the between-area pairs (P < 0.0001).

Between-area correlation is only weakly dependent on the amount of overlap of the units' frequency tuning curves

Onset and poststimulus firing rates and peak correlation coefficients for tone pips and noise bursts did not depend on the CF of the units or on their geometric means. The amount of response area overlap of the between area pairs expressed in number of octaves difference in CF, the CF distance, had also only a minor effect on the CCH strength (Fig. 8). The mean peak area of the CCH (Fig. 8A) is between 0.4 and 1.5. The value did not change significantly with CF distance. The peak correlation coefficients were smaller for tone pips (Fig. 8B) than for noise bursts (Fig. 8C). For tone pips, a small but significant decrease with CF distance for AAF × AII was found but not for the correlation between the other areas. For noise bursts, R was independent of CF distance. For poststimulus conditions, the same qualitative picture is found (Fig. 8D) but with some enhanced peak values, clearly in the range of the onset correlation coefficients, between 0.3 and 1.2 octaves separation in CF for the AI × AAF correlation. An ANOVA also showed a significantly larger R for AI × AAF compared with the other between area conditions (P < 0.005) for CF distances <0.5 octaves.



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Fig. 8. Between-area cross-correlation measures. A: scattergrams of the number of spikes in the CCH peak as a function of the characteristic frequency (CF) distance between the units in the pair for stimulus onset locally weighted scatterplot smoothing curves (LOWESS) suggest that the dependence on CF distance is modest. B: scattergrams of peak cross-correlation coefficients as a function of the CF distance between the units in the pair for stimulus onset. LOWESS curves suggest that for tone pips the onset R is independent on CF distance. C: for noise-burst stimulation R onset is independent of CF distance. D: postnoise R value for AI × AAF correlations shows some high values for CF distance ~1 octave, but as a whole is independent of CF distance.

Stimulus induced correlation is weakly dependent on stimulus intensity

The typical response pattern to a tone pip or noise burst is a transient increase in firing rate followed by a postactivation suppression, the duration of which depends on the strength of the onset response (cf. Fig. 2).

The Rs for poststimulus conditions were not correlated with preceding stimulus intensity for either stimulus. The stimulus correlation (Rstim) for tone pips was dependent on stimulus intensity but only for AI × AI and AI × AAF MU pairs (Fig. 9A). For these pairs, Rstim increased for intensities >50 dB SPL. The CF distance had an effect as well: pairs within AI and between AI and AAF, with CF distance <1 octave, showed a significant dependence on intensity whereas pairs with larger CF separation (Fig. 9B) did not. If a distance of 0.5 octaves was used as the dividing criterion, both CF-distance groups showed intensity dependence. For noise bursts, no dependence of Rstim on stimulus level was found, and separating the recordings into pairs with small or large CF distance made no difference. Note, this applies to the Rs that were significantly different from zero. For lower intensity levels, the number of significant Rs decreased substantially. For poststimulus conditions, the 5-10 times longer analysis window allowed significant correlations at lower mean values than during stimulus onset (cf. Fig. 5).



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Fig. 9. Dependence of the stimulus correlation strength on tone-pip intensity for multiunit pairs. Only for dual-electrode pairs in AI and for pairs between AI and AAF was stimulus intensity dependence observed (A). Splitting according to CF distance suggests that only pairs with CFs within 1/2 octave show an intensity dependence (B).

The peak width for dual-electrode CCHs (both MU and DSU) for tone-pip onset, but not for SSU correlograms, decreased significantly (P < 0.005) with intensity as shown by the regression lines (Fig. 10A), whereas the poststimulus CCH peak width was independent of the intensity of the preceding stimulus (Fig. 10B). The CCH peak width for noise burst onsets and for post noise conditions was independent of stimulus level.



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Fig. 10. Dependence of the CCH peak width on tone-pip intensity (A) and poststimulus (B) separated by correlation pair type. Onset CCH peak width decreased with intensity level for dual-electrode pairs [single-unit pair (DSU) and multiunit pair (MU) different electrodes] and not for single-unit single-electrode (SSU) pairs. Regression lines are drawn in. No dependence on the level of the preceding tone pip was found in poststimulus conditions.

For tone-pip stimuli, the number of coincident spikes/trigger spike in the CCH peak was independent of stimulus intensity but increased with peak width according to a power function with exponent 0.6 (Fig. 11).



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Fig. 11. Dependence of the number of spikes in the CCH peak on peak width. Number of spikes in a peak increased with peak width.

Does stimulus-induced correlation provide additional information to that of firing rate for SPL coding?

Stimulus-induced correlation can provide additional information to firing rate if R cannot be predicted fully from the firing rates. Because the strength of cross-correlation depends on the firing rates of two units, the geometric means of the driven firing rates were used for comparison. The geometric mean firing rates are dependent on stimulus intensity (Fig. 12A) for tone pips as well as noise bursts. The dependence for noise burst is stronger for the lower intensity levels. The precipitous drop of the curve through the noise data are the result of a tendency of the LOWESS curve fitting procedure to preserve curvature. The small number of data points for intensity 15 dB SPL is not sufficient to bend the curve upward. The stimulus correlation (Fig. 12B) also depends on intensity and is stronger for tone pips. The dependence of the Rstim's on the geometric mean of the driven firing rates is shown for MU pairs in Fig. 13. LOWESS curves are drawn in, but power-function curve fits also were calculated and had slopes of 0.51 for tone pips and 0.26 for noise bursts. Thus the dependence of Rstim on the driven rates is stronger for tone pips. The squared correlation coefficients for the dependence of Rstim on the geometric mean of the driven rates were 0.14 for tone pips and 0.11 for noise bursts. This suggests that for tone pips, only 14% of the variance in R can be explained by variation in the driven firing rates. For noise bursts, the explained variance is only 11%. Consequently the Rstim's and driven firing rates are sufficiently independent to allow their use in a combined coding of stimulus intensity.



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Fig. 12. Stimulus dependence of the geometric mean of the firing rates (A) and the peak correlation coefficient (B) for tone pips and noise bursts.



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Fig. 13. Scattergram between stimulus correlation and the geometric mean of the driven firing rates of the units in the correlation pair. LOWESS curves suggest a stronger dependence for tone pips than for noise bursts.

From a closer inspection of the dependence on intensity for the driven rate and Rstim, one observes an interesting contrast: whereas the driven rate for noise bursts shows a stronger intensity dependence than for tone pips, the Rstim dependence on intensity is stronger for tone pips. In effect, intensity explains 14% of the variation in driven rates for noise bursts and only 5% for tone pips. For the Rstim, the situation is that only 2% is explained by intensity in case of noise bursts but 9% for tone pips. A prediction of intensity on basis of the combination of Rstim and driven rates by multiple regression resulted in r2 = 0.13 for noise bursts and r2 = 0.10 for tone pips. In fact the prediction for noise bursts was better when only the driven rate was used and only marginally better for tone pips compared with using only Rstim. This suggests that a population code for stimulus intensity, across areas, based on a combination of firing rates and synchrony is unlikely. Instead, the coding of tone-pip and noise-burst intensity appears to be complementary: tone-pip intensity may require dominantly synchronized activity whereas noise-burst intensity coding may rely mostly on firing rates.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

A comparison was made for the peak correlation strength and width between single-electrode pairs, dual-electrode pairs in AI, and pairs from electrodes in two cortical areas for stimulation with tone pips, noise bursts, and under spontaneous conditions. The single-electrode pairs consisted of single units, whereas the dual-electrode pairs could be either single- or multiunit. The R values to stimulus onsets were higher for single-electrode pairs compared with dual-electrode pairs and higher for noise bursts compared with tone pips. Averaged across stimuli the peak cross-correlation coefficients for single-electrode pairs is ~0.1. This means that locally ~10% of the firings of two units, on average, occur within a few ms of each other but with an accuracy of 1 ms. The number of synchronized spikes in the CCH peak was on average 0.4 spikes per trigger spike for single-electrode pairs. For single-electrode pairs, there was no difference between the three cortical areas and the onset correlation was only a little higher than the poststimulus correlation. For pairs recorded with electrodes positioned across area boundaries, the onset correlations were a factor 3-4 higher than the poststimulus correlations. The within-AI dual-electrode R was larger than that across areas, especially for spontaneous firing conditions. The determining factor in the change of R with distance between electrodes within a cortical area was the CF distance. For between-area MU correlation pairs, the number of synchronized spikes per trigger spike in the CCH peak, was, except for the correlation between AI and AAF for tone pips, largely independent of CF distance. The mean values of the between-area Rs were larger for noise bursts than for tone pips regardless of the CF distance.

Simultaneous recording and separation of multiunit spike trains

The advantage of simultaneous recordings from the three cortical areas is that it eliminates potential depth-of-anesthesia effects and individual animal differences and allows pair-wise comparisons. With the large range of values for R, typically encompassing a factor 100, potential area differences thus can be detected easier. Recordings with relatively large (1.5-2.5 MOmega ) tungsten microelectrodes are most likely from cells with extended dendritic trees that provide open fields with large dipole moments, i.e., from pyramidal cells. The spikes in the present study invariably were of "regular spike" appearance (McCormick et al. 1985), adding weight to the assumption that the recordings were likely from pyramidal cells.

To assess the observed differences in R and CCH width for the within- and between-area pairs, it is important also to assess the differences in correlations between single and multiunits. Comparing single-unit pair CCHs with the multiunit pair CCHs in which they take part could provide that assessment. Take the very simple condition that one electrode records from a single unit and that the other records from two units with the same number of spikes per unit. It is easy to show that the correlation coefficient between the single- and the multiunit recording is <RAD><RCD>2</RCD></RAD> larger than the sum of the correlation coefficients between the two possible single-unit dual-electrode pairs. Correlation coefficients are not additive. Thus the observed nonlinear dependence of the DSU correlation on the MU correlation in the data is expected. Hence, the MU correlations will overestimate the strength of the global correlation compared with the SU estimate of the local correlation.

Quantifying correlation strength

In this study, as in our previous ones (Eggermont 1992, 1994; Eggermont and Smith 1996a), the strength of the correlation between the firings of different units was quantified by the peak cross-correlation coefficient. In addition we used the number of synchronized spikes in the CCH peak. Alternatively, the strength of correlation can be quantified by the coincident firing rate for the largest bin (cf Fig. 1) (Abeles 1982). The peak cross-correlation coefficient is equal to the largest value for an individual bin in the CCH that, in this study, is of 1-ms duration. The R is a normalized value, which takes into account the expected correlation under statistical independence of the firings in the two spike trains and assumes that bin filling occurs according to a Poisson process. This assumption implies that the probability of having simultaneously two coincidences in the same bin should be vanishingly small. Burst firing, with interspike intervals that are as small as 3 ms (Bowman et al. 1995; Eggermont et al. 1993), limits the binwidth to 1 or 2 ms. For low firing rates, the value of R can be interpreted as the percentage of times that a spike in one unit is followed by a spike in the other unit in the most significant bin, i.e., within a certain delay but within 1 ms around that delay. The value of R depends on the background correlation that is estimated by the product of the firing rates of the units in the analysis window and the binwidth. For low firing rate conditions as in the present study, R closely approximates the probability of a spike of unit 2 in the peak bin given a spike on unit 1. If the analysis window is reduced in time and only captures the onset response, the expected value based on firing rates increases and the R value may be entirely explained on basis of the short-term firing rates of the units in the pair. This suggests, that in these cases, the CCH is the result of the close locking of the firings in the two units to stimulus onset and not necessarily of an increased firing synchrony. This was the case for all between-area CCHs and for most within-area correlograms.

In the present study we used the uncorrected CCH, i.e., no shift corrector was subtracted nor was a joint peristimulus time histogram (JPSTH) correction performed, to estimate the correlation of firing between neurons. A previous study (Eggermont 1994) has shown that for auditory stimulation in general the shift predictor or JPSTH predictor is generally equal to the onset correlation as a result of strong synchronization to stimulus onset. It is unlikely that the nervous system performs a correction for stimulus-induced correlation as estimated by the various predictors. Thus our raw correlations may effectively estimate cofirings between two neurons or two MU groups that could be useful in coding of sound properties.

Comparison with other studies

Several previous studies in visual cortex (Nelson et al. 1992; Nowak et al. 1995; Schwarz and Bolz 1991; Ts'o et al. 1986) and auditory cortex (Brosch and Schreiner 1999; Eggermont 1992; Eggermont and Smith 1996; Vaadia et al. 1991) have reported long-distance correlations between the firings of individual units. The correlograms obtained in these cases were often rather broad with peaks centered at zero lag time. The larger the distance between the units in the pair, the broader the peaks tended to be (Nelson et al. 1992; Nowak et al. 1995), but broad peaks were found for single-electrode pairs as well (Eggermont 1992). Nelson et al. (1992) attributed the wide correlograms for neurons in separate areas (A17 and A18) in visual cortex largely to diffuse cortico-cortical interactions. Similar to findings in the visual system, the between-area CCH peak width in the present study was largely independent of receptive field separation.

This independence of the correlation strength on CF separation is not what would be expected from the divergent tonotopic cortico-cortical projections between AI and AAF which suggest that cortico-cortical fibers from an injection site in one area terminate in a band of activity similar to an isofrequency contour (Imig and Reale 1980). This would suggest potentially strong monosynaptic connections between recording sites with the same or nearly the same CF and hence increased strength of their CCHs. This could show up in the spontaneous correlations between AI and AAF, compared with those between AI and AII or AAF and AII. Although there was an indication for enhanced R values, comparable with those for single-electrode pairs, around 1 octave separation in CF, the expected increase for CF differences of zero was not clear. A sizeable fraction of AI × AAF correlation strengths for recordings with the same CF was indeed larger than for the 0.2- to 0.5-octave difference range but did not reach into the upper range of the single electrode pair correlations (cf. Fig. 4).

Correlograms in this study only considered lag-times from -50 to 50 ms., therefore obtaining peak widths in excess of ~50 ms was generally impossible. This may have been the reason that in this study the within-area dual electrode CCHs were of the same width as the CCHs for across area conditions. Previously, spontaneous CCH peak widths in AI were also obtained for lag times between -500 and +500 ms (Eggermont 1992), but even in that case the dual-electrode pairs CCHs in AI were generally of the same width as in the present study. In contrast to across the area 17 and 18 border correlations in visual cortex (Nelson et al. 1992; Nowak et al. 1995), peak widths <5 ms were not observed in our across-area pair CCHs. Some exploratory correlations with 5-ms binwidth suggests that under spontaneous conditions, peaks widths <= 500 ms can be found but the corresponding Rs are very small (unpublished data). Calculating CCHs with 5-ms bins for the stimulus onset conditions in the present study violates the Poisson assumption for bin filling and will thus not allow statistical testing of the significance of the R values.

The peak CCH correlation coefficients obtained here cannot directly be compared with results from other studies because most used coincident firing rates for indicating the correlation strength and used binwidths that differed from those in the present study. In addition, they often (Nowak et al. 1995) only quantified the strength of the difference histogram obtained after subtraction of the shift predictor. In the present study, the amount of correlation produced by the stimulus is the factor of interest not the small effect left after subtraction. In case the spikes are closely time locked to the stimulus, as is nearly always the case for auditory neurons under transient stimulation, the value of the shift predictor is generally very close to the value of the onset correlation strength. However, one could compare the poststimulus CCHs from the present study with the difference histogram peak values from Nowak et al. (1995) for visual cortex. They only investigated the correlations between units in areas 17 and 18 and found that the mean peak values for the "castle type" of CCH, comparable with nearly all our MU CCHs, were 3.36 spikes/s. The largest peak value that was reported was ~12 spikes/s and the smallest was ~0.4 spikes/s. One can convert these values, obtained for a 5-ms binwidth, to the 1-ms binwidth used in the present study by multiplication with a factor 5. This results in a range of 2-60 spikes/s. We found a mean poststimulus, between-area, MU CCH peak coincident rate value of 20.5 spikes/s, the largest value was 55 spikes/s and the smallest was 3 spikes/s. These cross-correlation strengths under spontaneous conditions in our study are thus very similar to the difference correlations obtained for visual cortex.

Effects of receptive field overlap

During tone-pip onsets, coincident spikes will reflect the amount of overlap in the spatiotemporal receptive fields of the two units or recording sites. One could consider a model in which there is convergence from several narrowly tuned neurons from the medial geniculate body (MGB) onto cortical cells. One of those MGB units could send axon collaterals to two cortical cells, whereas the other inputs onto each cell are from different MGB cells or other cortical cells. The coincidence detection operation then in effect estimates the receptive field of the MGB unit that provides the common input to the two cortical cells. Thus sharp tuning can be conserved for synchronous firing as demonstrated previously (Eggermont and Smith 1996b). At the same time, the convergence of afferent activity from several MGB (or other cortical cells) onto a single cortical cell allows the effects of neurons from neighboring frequency ranges to influence the response to the narrowly tuned "core." The absence of an effect of CF distance on R for between area correlations involving AII thus suggests that there is no common MGB afferent. The dominant input to AI, AAF, and AII is, respectively, from the ventral part of the MGB, the posterior group of thalamic nuclei (PO), and the caudal dorsal nucleus of the MGB (Winer 1992) and thus likely not to provide much overlap. In contrast, for broadband stimuli coincident firings only reflect the degree of near simultaneous activation of the neurons, regardless the receptive field overlap. As the results show, the value of R does not change with intensity or with CF distance for noise-burst stimulation. Natural sounds, specifically phonemes in human speech, often contain noise bursts that are well suited to bring out this synchronization between neurons with large CF distance, i.e., over large cortical areas.

Poststimulus as well as onset CCHs showed a large difference in the values of R and width between single-unit single-electrode pairs and all other recording conditions. In addition, the presence of a stimulus had very little effect on the strength of the SSU correlation. Clearly neurons within a cortical column are stronger coupled, likely by common input, than those in different columns. Spontaneous input and stimulus driven input to these cells affects the same neurons and the only effect of stimulation may be an increase in firing rate without a change in R. Thus in contrast to what is found in dual electrode correlograms, stimulation does not result in better synchrony for local neuronal groups.

What causes long-range correlation?

Long-range correlation of neural activity may result from structural connectivity such as common input from long range axon collaterals (Schwarz and Bolz 1991; Ts'o et al. 1986; Wallace et al. 1991) that provide monosynaptic excitation. It also can result from network properties that cause a global synchronization in firing rate by covariation in neural resting potentials (as reflected in local field potentials) (Eggermont and Smith 1995a) or by covariation in neural excitability or latency (Brody 1999). Long-range collaterals preferably follow the isofrequency sheets in AI (Wallace et al. 1991), so one would expect synchrony to be stronger within iso-frequency sheets than perpendicular to it. In the group of recordings with two electrodes in AI, electrodes were oriented along a caudal-rostral line, i.e., aimed perpendicular to the iso-frequency contours. So this cannot shed much light on a potential anisotropy of R; however, for closely spaced electrodes in AI, resulting in recordings with similar CFs, the Rs were of the same size as for larger CF distances. This corroborates our previous findings of isotropic R values in AI (Eggermont 1992, 1994). Global synchronization during spontaneous conditions likely is a network property related to electroencephalographic (EEG) synchronization or oscillation (Contreras et al. 1997; Singer and Gray 1995). The correlation properties of neurons in the cerebral cortex are also dependent on the attentive or sleep state of the animal: during slow-wave sleep or anesthesia strong correlations in the firing rate of simultaneous recorded neurons are obtained, whereas during arousal, wakefulness, or REM sleep, neurons may discharge quite independently. Ketamine, similarly to barbiturates, results in prolific spindling that is nearly simultaneous across large cortical areas (Contreras et al. 1997). So it cannot be excluded that the anesthesia used in the present study is favorable for inducing correlative firing in neighboring and distant units. However, the fact that the poststimulus CCH peak strengths are fully comparable with those for visual cortex in animals anesthetized without ketamine makes this unlikely.

Correlation strength is not a code for SPL

Cross-correlation studies have been used previously for assessing the representational role of changes in correlated firings (deCharms and Merzenich 1996; Eggermont 1997). As a potential example of such a representational role, the relationship of the Rstim to stimulus intensity was studied. A representation of stimulus intensity by the correlation strength would require a gradual and monotonic change of Rstim with intensity. Such an effect was observed for the stimulus-induced part of the correlation but only for tone-pip stimulation and only for within AI and between AI and AAF recordings. The different intensity dependence of firing rates and correlation strengths for tone pips and noise bursts makes a bivariate representation of SPL in firing rate and synchrony unlikely. Previously, for the auditory midbrain of the frog, it was shown that stimulus intensity was not represented in the correlated onset activity to tone pips, whereas it was in the population firing rate for units tuned to the sound (Eggermont 1989).

A potential code for intensity based on population firing rate or firing synchrony may be a priori unlikely. This is largely because of the nonmonotonic change in the size of the area activated by tone pips of increasing intensity (Phillips et al. 1994; Schreiner 1998), which has been attributed to the nonmonotonic nature of the rate-intensity functions of single neurons. Potentially, the posterior auditory field, where nonmonotonic units are abundant, may code intensity by a distributed representation of best intensities of these nonmonotonic rate-intensity functions (Phillips and Orman 1984; Phillips et al. 1995).

Correlation strengths for stimulus onsets distinguish between the presence and absence of a stimulus

The proposed role of synchronized firing in the feature binding process (Gray and Singer 1995) ideally requires that the distributions of R for onset and poststimulus conditions do not overlap. This would guarantee that binding is present or absent and not ambiguous. For the present data, the most favorable condition for detecting significant differences between the presence and absence of a stimulus, either from firing rate or from the R values, likely is the comparison of onset activity to poststimulus activity in between-area pairs. For this purpose, a receiver-operating characteristic (ROC) curve has been constructed (Fig. 14) based on the distribution of MU firing rates and, between cortical area, R values. The distributions of the logarithm of MU firing rate and R for onset and poststimulus conditions are shown as well. The distribution of the logarithm of the MU firing rates is shown in Fig. 14, left, top and middle. One observes that there is a substantial overlap, but that log onset rates >1 (MU firing rate >10 spikes/s) do not occur frequently in the poststimulus condition. The distributions for the logarithm of R for onset and poststimulus conditions are in Fig. 14, right, top and middle. The distribution for onset R is lognormally distributed (best fit drawn in), that for poststimulus conditions is not. In Fig. 14, bottom, on the y axis, 100% of false positives (cumulative percentage of the MU firing rate or R under poststimulus conditions) is plotted and on the x axis, the percentage false negative for stimulus present is plotted (cumulative percentage of MU firing rate or R for onset conditions). The criterion MU firing rates (multiplied by 2 so that the same y-axis scale can represent them) and the R values (multiplied by 1000), based on the combined distributions for stimulus onset and poststimulus conditions, are also drawn in. One can thus select a value of R (or MU firing rate) so that when the onset R (or MU firing rate) is less than this value one would decide that a stimulus is absent; for a larger value the stimulus is considered present. Similarly, on basis of the poststimulus R values (or MU firing rates), one calculates the percentage of falsely assigning the label "stimulus present." These pairs of percentages are indicated (diamond ). This is repeated for a range of R values (or MU firing rates) and the combination represents the ROC. The optimum decision criterion is typically taken as the point closest to the 0% false positive (where 100 - % false positive = 100) and 0% false-negative point. For the MU firing rate, the optimum criterion value is for a 12.2% false-positive rate and a 35% false-negative rate found for a MU firing rate of 7.94 spikes/s. For R, a 12% false positive and a 17% false negative represents the near optimum point for best decision criterion and is obtained for an R = 0.022. This suggests that a decision "stimulus present" on basis of R could be made with some confidence by requiring a very modest amount of synchronized firing, i.e., >= 2.2%. Note that the mean poststimulus R is equal to ~1%. A comparison between decisions based on MU firing rate and R indicates that the optimal criteria result in the same number of false positives but that the decision based on R has a much smaller number of false negatives. Hence using synchronized firings results in better performance.



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Fig. 14. Receiver operating characteristic (ROC) for deciding between stimulus presence on basis of the value of the MU firing rate (left) and onset peak correlation coefficient (right). Horizontal axis represents the percent false negative and the vertical axis represents 100 - % false positive as well as the value of the cross-correlation coefficient multiplied by 1,000. Optimal decision point is the one closest to the top left corner.

In conclusion, synchronization between areas or within distant points in the same cortical area may be more important than local synchronization (of activity recorded on single electrode pairs) to provide feature binding. Local activity appears to be synchronized regardless the level of stimulation, whereas specifically the very low between-area synchronization under spontaneous conditions is considerably elevated during stimulation. This holds for stimulus onsets (this paper) as well as steady-state stimulation (Eggermont 1997). Specifically this applies to broadband stimuli such as noise that are able to synchronize across a wide range of CFs. Communication sounds often contain noise, e.g., plosive consonants in speech, and these sections are imminently suited as grouping features on basis of enhanced synchrony.


    ACKNOWLEDGMENTS

K. Ochi, M. Kenmochi, and M. Kimura assisted in the data collection. G. Shaw provided programming assistance.

This work was supported by the Alberta Heritage Foundation for Medical Research, the Natural Sciences and Engineering Research Council, and the Campbell McLaurin Chair for Hearing Deficiencies.


    FOOTNOTES

Address for reprint requests: Dept. of Psychology, The University of Calgary, 2500 University Dr. N.W., Calgary, Alberta T2N 1N4, Canada.

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 18 November 1999; accepted in final form 28 January 2000.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

0022-3077/00 $5.00 Copyright © 2000 The American Physiological Society