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. Author manuscript; available in PMC: 2012 Aug 1.
Published in final edited form as: Neurosci Res. 2011 Apr 21;70(4):401–407. doi: 10.1016/j.neures.2011.04.003

Auditory cortical tuning to band-pass noise in primate A1 and CM: a comparison to pure tones

Yoshinao Kajikawa 1, Corrie R Camalier 2, Lisa A de la Mothe 3, William R D’Angelo 2, Susanne J Sterbing-D’Angelo 2, Troy A Hackett 2,3
PMCID: PMC3130097  NIHMSID: NIHMS290888  PMID: 21540062

Abstract

We examined multiunit responses to tones and to 1/3 and 2/3 octave band-pass noise (BPN) in the marmoset primary auditory cortex (A1) and the caudomedial belt (CM). In both areas, BPN was more effective than tones, evoking multiunit responses at lower intensity and across a wider frequency range. Typically, the best responses to BPN remained at the characteristic frequency. Additionally, in both areas responses to BPN tended to be of greater magnitude and shorter latency than responses to tones. These effects are consistent with the integration of more excitatory inputs driven by BPN than by tones. While it is generally thought that single units in A1 prefer narrow band sounds such as tones, we found that best responses for multi units in both A1 and CM were obtained with noises of narrow spectral bandwidths.

Keywords: auditory cortex, bandpass noise, bandwidth, frequency tuning, primate, multiunit

Introduction

Cortical processing of acoustic information is assumed to originate in the primary auditory cortex, A1, and proceeds to other areas organized hierarchically into regions of core, belt and parabelt (Hackett, 2002, 2011; Hackett and Kaas, 2004; Kaas and Hackett, 2000; Rauschecker and Tian, 2000). Connections between areas preserve tonotopic organization (Imig and Reale, 1980; Morel and Kaas, 1992), which provides a basis for neurophysiological identification of non-primary auditory cortical areas (Kajikawa et al., 2005; Recanzone et al., 2000). However, the lateral belt areas prefer spectrally complex sounds, and are weakly responsive to tones (Rauschecker et al., 1995). Accordingly, band-pass noises (BPN) instead of tones are often used to explore tonotopic organization in belt areas (Kusmierek et al., 2009; Maier and Ghazanfar, 2007; Rauschecker and Tian, 2004; Romanski et al., 1999; Tian et al., 2001).

In core A1, responses to tones are stronger than responses to broad-band noise across species (Kusmierek and Rauschecker, 2009; Pandya et al., 2008; Phillips and Cynader, 1985; Recanzone, 2000; Schwarz and Tomlinson, 1990) indicating a preferential processing of tones over noise in A1. However, a systematic examination of responses to BPN in A1 has been scarce. Brief descriptions of responses to BPN in A1 appear sporadically. In rat A1, Doron et al. (2002) described that BPN with center frequencies matching the characteristic frequency (CF) were “very effective stimuli”. Rauschecker et al. (1995) noted that BPN evoked stronger responses than tones in macaque A1, though tuning to the center frequency of BPN or other areas’ responses were not examined (see also Kusmierek and Rauschecker, 2009). In marmoset A1, Barbour and Wang (2003) reported that many tone-responsive neurons in auditory cortex respond poorly to wide-band noise or even BPN except at narrow bandwidths. Kayser et al. (2007) examined tuning of spike signals and local field potential (LFP) to BPN and tones in macaques. However, while they compared tuning properties between spikes and LFP, they did not compare tuning properties between responses to tone and BPN. Even though technical details about the BPN were not provided in these cases, results generally agreed that A1 responses to BPN at certain bandwidths could be robust. A recent study has investigated neurons that are weakly responsive to tones in marmoset A1 (Sadagopan and Wang, 2009). The spectro-temporal receptive fields of these neurons were studied using a two tone paradigm, and showed the presence of a secondary receptive field (RF). Depending on the structure of the secondary RF, some neurons that responded only weakly to tones responded robustly to BPN at a preferential bandwidth around the best frequency (BF). However, while this study and others examined BPN responses when it was centered around the neuron’s BF, they did not examine A1 responses to BPNs centered away from the BF. The main excitatory RF are shaped by inhibition apparent on side bands (Shamma et al., 1993; Sutter et al., 1999; Sutter and Loftus, 2003), though underlying synaptic inhibition is co-tuned with synaptic excitation to tone frequency (Ojima and Murakami 2002, Tan and Wehr 2009, Wehr and Zador 2003). Thus, while previous studies suggest that BPN responses are enhanced over PT responses, suppression of responses to BPN could occur near the edge of the RF. Then, it would be expected that the bandwidth of tuning to the center frequency of BPN is narrower than the tuning bandwidth determined by tones.

Additionally, we examined BPN responses in non-core areas, such as the caudomedial belt area (CM). In CM, neurons respond robustly to sound at latencies and temporal precision comparable to neurons in A1 (Kajikawa et al., 2005; 2008; Kusmierek and Rauschecker, 2009). Given the similarities to A1, one might expect to find enhancement of BPN response over PT responses in CM. However, CM neurons are more broadly tuned to tones than A1 (Kajikwa et al. 2005, Recanzone et al. 2000) and receive afferents from different thalamic nuclei from A1 (de la Mothe et al. 2006b). Therefore, it is unknown whether CM performs spectral integration in a manner similar to A1 or not. In the present study, we investigated the responsiveness of tone sensitive multiunits in A1 and CM to BPN across a wide range of center frequencies and intensities and how it differed from their responses to tone.

Materials and Methods

All experimental procedures were conducted in marmoset monkeys (Callithrix jacchus jacchus) in accordance with NIH Guidelines for the Use of Laboratory Animals under a protocol approved by the Vanderbilt University Institutional Animal Care and Use Committee. Multiunit recordings were obtained from auditory cortex in 3 ketamine-anesthetized marmoset monkeys. Details of the surgical preparation can be found in Kajikawa et al. (2005, 2008).

Experiments were conducted in a double-walled sound-proof chamber (Acoustic Systems RE247). Multiunit (MU) recordings were made with either a single 1 MΩ tungsten microelectrode insulated with Parylene-C or polyimide tubing (MicroProbe), or four of the electrodes affixed to each other in a bundle. Waveforms and timestamps of spikes were saved on a computer hard drive for off-line analysis (Plexon Inc., Dallas, TX). At the end of each recording procedure, electrolytic marker lesions (10 µA for 10 s) were made in key locations to facilitate reconstruction of recording sites. Areal identification of recording sites was verified in reconstructed serial sections of each brain using established architectonic criteria and tonotopic gradients (de la Mothe et al., 2006a, b; Kajikawa et al., 2005).

Acoustic stimuli were generated by Tucker–Davis Technologies (TDT) System II hardware and software (SigGen TDT). Auditory stimulation was delivered through an electrostatic transducer (STAX SR-X/MK3, modified by M. Fong, UCSF) coupled to a hollow ear bar inserted into the ear contralateral to the recording sites. Stimuli were calibrated using a one-quarter-inch microphone (Model 7017 ACO Pacific) and custom software (SigCal TDT). Microphone calibration was verified using an ACO Pacific 511E Acoustic Calibrator. Stimulus presentation was controlled by the data acquisition software. All stimuli had duration of 50 ms, gated with 5 ms cos2 ramp. For two marmosets, the stimulus battery had tones and 1/3 and 2/3 octave BPN with 22 center frequencies spanning 0.3 to 38.4 kHz in 1/3 octave steps, with 7 sound levels from −20 to 40 dB SPL in 10 dB steps (where sound level was controlled with a TDT PA2 programmable attenuator). All frequency-intensity combinations to derive the frequency response areas (FRA) of 3 sound types were presented eight times in random order at a rate of 1 Hz. In another initial marmoset, a smaller stimulus battery was examined which contained tone and 1/3 octave BPN with 11 center frequencies spanning 0.2 to 31.7 kHz in 0.73 octave steps, with 5 sound levels from 0 to 60 dB SPL in 15 dB steps. These frequency-intensity combinations were presented eight times at a rate of 2 Hz. BPN sounds were created using an inverse FFT of a spectrum for each sound (SigGen), not by bandpass filtering of broadband noise, followed by root-mean square normalization.

All analyses were done using Matlab (Mathworks) in a manner similar to our previous reports (Kajikawa et al., 2005, 2008). Responses were sorted by stimulation parameters and plotted in raster format and peristimulus time histograms (PSTHs) using moving bins of 1 ms stepped at 0.1 ms. To obtain FRA, firing rate (spikes/sec) per frequency-intensity combination was calculated from the number of spikes summed over all trials from response onset to 50 ms after the stimulus onset and divided by the number of trials (figure 1, bottom). Here, response onset latency was defined as the minimum time at which the firing rate first became greater than six times the standard deviation (SD) of the baseline spontaneous firing rate, where baseline was calculated from a period of 200 ms prior to stimulus onset to 5 ms after stimulus onset. In some units, when spontaneous activity was low, many spikes were detected as a response. For such units, spikes apparently off from the tuning were considered as false positive detection and excluded from further analyses of responses.

Figure 1.

Figure 1

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Spike raster and FRA plots of an exemplar MU recorded from A1. Each column shows responses to tones (left column), 1/3 octave BPN (center), and 2/3 octave BPN (right). Seven rows of raster plot show responses at −20 to 40 dB SPL in 10 dB steps with center frequencies ranging from 0.3 kHz to 38.4 kHz. The FRA, representing mean firing rate between 10 and 30 ms from the onset of sound at all frequencies/intensities, is plotted in gray scale at the bottom of each column.

For each FRA, we defined the lowest sound level at which tone of any frequency or BPN of any center frequency evoked a response, determined by the criterion described above. At the sound level threshold in tone FRA, the CF was defined as the tone frequency that evoked response. Similarly, we identified the characteristic center frequency of BPN that evoked response at the threshold and expressed it as CFBPN. When responses were evoked by tones of multiple frequencies at the threshold, the CF was calculated as exp(∑{FRi × ln-Fi}/∑{FRi}), in which ln-Fi is log-transform of each frequency that evoked firing response and FRi is the magnitude of evoked response. When BPN of multiple center frequencies evoked responses at the threshold, the center frequencies of effective BPN took place of Fi in the above expression to derive CFBPN. The tuning bandwidth at every sound intensity was expressed in octaves with a step of 1/3 octave. We also estimated differences in the tuning bandwidth of the responses to tones and BPNs at each sound intensity in units of octave. That was expressed as ΔBW = log2(hiFBPN) − log2(hiFTone) or ΔBW = log2(loFBPN) − log2(loFTone), in which hiFTone and loFTone are the highest and the lowest tone frequency that MU responded and hiFBPN and loFBPN are the highest and the lowest center frequency of BPN that MU responded. Similar difference in tuning between 1/3 oct. BPN and 2/3 oct. BPN were also derived (figure 4).

Figure 4.

Figure 4

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Distributions of differences in the tuning bandwidth of responses to tone and 1/3 octave BPN (left column), tone and 2/3 octave BPN (center), and 1/3 octave BPN and 2/3 octave BPN (right) at sound intensities from 0 to 40 dB SPL in A1 (the first row) and CM (the second row). The magnitude of change (ΔBW, unit of octave) was estimated at low and high frequency sides of tuning separately, and shown in dark gray and light gray histograms, respectively. At lower sound intensities, fewer MUs were counted in histograms in the left and center columns, because a small number of MU responded to tones. Asterisks indicate where distribution of ΔBW was significantly below or above zero for low (dark gray) or high (light gray) frequency sides separately (Wilcoxon’s signed rank, P < 0.05), indicating significant expansion of tuning to BPN relative to tone. Vertical scale in the bottom row is common to all rows in each column.

Results

We examined responses from 45 sites in A1 and 52 sites in CM (yield per marmoset case: A1: 5, 12, 28; and CM: 4, 20, 28 units, respectively). Some sites were excluded from analysis because they were unresponsive to any stimulus (A1: 19; CM: 23). A small fraction were excluded because they had no response to tones and were only weakly responsive to broad-band noise or BPN (A1: 3; CM: 4), or had incomplete battery sets for the FRA (CM: 3). Here, we present results of BPN and tone responses from 23 and 22 locations in A1 and CM. The first case was exploratory and was examined with a less extensive stimulus set (details above), but recordings in all cases provided qualitatively similar results, so we show results from the second and the third animals for convenience.

Figure 1 shows raster plots and FRAs of typical example A1 MU’s onset type response to tone, 1/3 octave BPN and 2/3 octave BPN. The lowest intensity at which this MU responded was 10, −10, and −10 dB SPL for 7.6 kHz tone, 1/3 octave BPN and 2/3 octave BPN sounds centered at 7.6 kHz. Above 10 dB SPL, the range of BPN center frequencies that evoked a response were wider than that of the tones. The response onset was earlier for BPN than for tones. Latencies of responses to all sound types became shorter as the sound level increased. In most FRA, the sound level at which the minimum response latency was detected was the maximum tested, 40 dB SPL, regardless of sound types. However, the percentages of detecting the minimum latency at 40 dB SPL significantly declined with the bandwidth of sound (88.9, 75.0 and 63.9% for tone, 1/3 and 2/3 octave BPN, Fisher’s exact test, p<0.05). Therefore, observation of the minimum response latency at lower sound levels was more frequent for BPN than tone responses. These results suggested that BPN evoked stronger responses with shorter latency, closer to their saturating limit, at lower sound levels.

Across MUs, the FRA parameters were compared between responses to tones and BPN. In both A1 and CM, the CFBPN did not differ from the CF (Friedman’s non-parametric repeated measures ANOVA, A1: χr2(2, N=18) = 3.09, P = 0.21; CM: χr2(2, N=18) = 0.35, P = 0.84, figure 2). However, BPN was more effective in evoking MU responses at lower sound intensity and across a wider frequency range. Figure 3 shows distributions of sound intensity threshold of response to tones, 1/3 and 2/3 octave BPN in A1 and CM (see also Table 1). Thresholds were significantly different as a function of stimulus type in both A1 and CM (Friedman’s test, A1: P < 0.05, CM: P < 0.05). In both A1 and CM, this effect was attributable to the difference between tone and both BPN responses (Tukey’s HSD test, P < 0.05), while there was no difference between 1/3 and 2/3 octave BPN in either area (P > 0.05).

Figure 2.

Figure 2

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The relationship between CF and CFBPN in A1 (top row) and CM (bottom row). CFBPN of responses to 1/3 octave BPN (left) and 2/3 octave BPN (right) are plotted against CF of tone responses. In all plots, CFBPN was proportional to CF and did not significantly differ from CF (P > 0.05, see text).

Figure 3.

Figure 3

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Distributions of response thresholds in A1 (top row) and CM (bottom row). Threshold of MU responses to tone (left column), 1/3 octave BPN (center), and 2/3 octave BPN (right) are shown. Asterisks indicate pairs of distributions that were significantly different from each other (P < 0.05, see text).

Table 1.

Mean threshold, latency and spontaneous spiking rate (SD) of responses to tone, and to 1/3 octave and 2/3 octave BPNs in A1 and CM

A1 CM
Threshold
(dB SPL)		 Latency
(ms)		 Spont. rate
(Hz)		 Threshold
(dB SPL)		 Latency
(ms)		 Spont. rate
(Hz)
Tone 23.3 (12.4) 13.9 (1.9) 5.0 (4.8) 15.0 (12.0) 13.3 (5.7) 4.8 (4.9)
1/3 octave BPN 5.0 (13.8) 12.5 (2.2) 4.8 (4.1) 0 (15.7) 12.6 (5.8) 4.6 (4.5)
2/3 octave BPN 5.6 (13.8) 12.3 (2.0) 4.7 (3.8) −4.4 (14.2) 12.4 (5.8) 4.4 (4.1)
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Frequency tuning widened as a function of increasing stimulus intensity. Figure 4 shows distributions of the relative tuning response range quantified as the difference (ΔBW, see Methods and Materials) in the lowest (dark gray) and highest (light gray) frequencies of tuning between different sounds. The distributions were obtained separately at different sound levels (0–40 dB SPL) in A1 (top) and CM (bottom). Comparison of responses to 1/3 octave BPN with tones (figure 4, left column) revealed that ΔBW was negative in general for the lowest frequencies of tuning (dark gray). It indicated that the lowest center frequencies of BPN to which MUs responded were lower than the lowest frequencies of tones those evoked MU responses. Similarly, ΔBW was mostly positive for the highest frequencies of tuning (light gray), indicating that the highest center frequencies of BPN to which MUs responded were higher than the highest frequencies of tones those evoked MU responses. Widening of tuning to the 1/3 octave BPN compared with tuning to tones were significant at both the low and high frequency sides in general at 20–40 dB SPL (Wilcoxon’s signed rank, P < 0.05). There was a similar expansion of the response to 2/3 octave BPN compared to tones (figure 4, center column). At 0–10 dB SPL, expansion of tuning was small and not significant. However, note that few MU responded to tones at those sound levels (figure 3), thereby there were no tone responses to compare with BPN responses for such MUs. No significant expansion of tuning bandwidth at those weak sound levels could be due to a low number of samples. Changes in tuning from 1/3 octave to 2/3 octave BPN were less prominent, with a tendency for 2/3 octave BPN responses to have wider tuning than 1/3 octave BPN responses (figure 4, right column). Taken together, these results indicate that while the most effective spectral frequency was similar for BPNs and tones, BPNs evoked responses at lower sound levels than tones, and BPNs evoked responses even when centered at frequencies where tones did not in both A1 and CM.

In addition to changes in the shape of the FRA, BPN evoked a greater response magnitude than tones. Since we did not find significant change in the CF of both PT and BPN, we focused the analysis of response magnitude on the rate-level function at or near the CF for individual MU. To compare the response magnitudes across different types and levels of sounds, we normalized responses by the maximum tone response for each MU. Also, to set a common reference sound level across MUs that had different response thresholds, normalized rate-level functions of BPN responses were aligned to the tone response threshold (THTONE). For example, the response thresholds of the MU shown in figure 1 were 10 dB SPL for tones and −10 dB SPL for 1/3 BPN. In this case, the tone response threshold was set to 0 dB, and the threshold of 1/3 BPN response was set to −20 dB.

Figure 5 shows the distributions of normalized and aligned response magnitudes at sound levels relative to THTONE. Below 0 dB in the figure (which corresponds to relative sound levels below THTONE) many MU responded to BPN, in particular at −20 and −10 dB relative to THTONE. Even at THTONE, or 10 to 20 dB above THTONE, responses to BPN were larger than to tones in general. Differences at high sound levels did not reach significance. One reason could be that response magnitude became asymptotic at high sound levels. Another possibility could be that fewer MU being counted in histograms at higher sound levels relative to THTONE, because data for relatively high sound levels were not available for MU whose THTONE was close to 40 dB SPL. There was no significant difference in response magnitude between 1/3 octave and 2/3 octave BPNs at all intensities (Wilcoxon’s signed rank, all comparisons P > 0.05).

Figure 5.

Figure 5

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Distributions of response magnitudes at or near the CF and CFBPN. Top and bottom rows show distributions of A1 and CM multiunits, respectively. Intensity relative to tone threshold is labeled at the top of each column. At all intensities, distributions of normalized response magnitudes were shown in histograms of hollow, light and dark gray bars for tones, 1/3 octave and 2/3 octave BPNs, respectively. Asterisks indicate intensities at which BPN responses were distributed at higher magnitude than tone response for 1/3 octave BPN (light gray) or 2/3 octave BPN (dark gray) (Wilcoxon’s signed rank, P < 0.05).

Figure 6 shows response latencies of tones and to 1/3 and 2/3 octave BPN in A1 and CM (see also Table 1). Response latencies across stimuli were significantly different in both areas (Friedman’s test, A1: P < 0.05, CM: P < 0.05). This difference in latency was attributable to significantly longer response latencies to tone than 1/3 and 2/3 octave BPN in both areas (Tukey’s HSD test, all comparisons P < 0.05). Since all tones and BPN were included and randomly ordered in single recording sessions, there was no significant difference in spontaneous spiking rate between data sets of tone responses and BPN responses within each MU (Friedman’s non-parametric repeated measures ANOVA, A1: χr2(2, N=18) = 1.44, P = 0.49; CM: χr2(2, N=18) = 1.10, P = 0.58, Table 1). Therefore, shorter latency of responses to BPN than tone was not due to difference in spontaneous spiking rate. There were also no significant correlation between latency and spontaneous spiking rate across MU within each sound type (Spearman’s rank correlation, rs = −0.038, 0.044 and 0.081 for tone, 1/3 and 2/3 octave BPN, respectively, P > 0.5 for all sound types). In conclusion, BPN evoked stronger and faster cortical responses than tones.

Figure 6.

Figure 6

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Response latency to tone, 1/3 octave and 2/3 octave BPNs in A1 (top) and CM (bottom). Latencies of each multiunit’s response to three sounds were connected by lines. Asterisks indicate pairs of distributions that were significantly different from each other (P < 0.05, see text) in either A1 or CM.

Discussion

In the present study, we showed how FRAs to 1/3 or 2/3 octave BPN differed from that of responses to tones in areas A1 and CM of the marmoset auditory cortex. While the use of BPN did not alter the CF appreciably, it lowered response thresholds, widened tuning bandwidth, and evoked stronger responses with shorter latencies than the use of tones in both A1 and CM. We discuss the physiological mechanisms that could underlie our results, considering the acoustic properties of BPN.

A plausible hypothesis that accounts for stronger response to BPN is excitatory synaptic summation. It is well known that auditory cortical synaptic potentials have spectral tuning broader than associated action potentials (Ojima and Murakami, 2002; Ribaupierre et al., 1972; Tan et al., 2004; Volkov and Galazjuk, 1991; Wehr and Zador, 2003). The spread of spectral power in BPN should activate inputs tuned to different spectral frequencies collectively at once. This activation presumably happens in frequency ranges within or out of the excitatory domain of the FRA estimated from action potentials. Consequently, synaptic summation of excitatory inputs, individually ineffective, may drive membrane potentials to reach firing threshold and expand tuning bandwidth. The lower thresholds to BPN are also compatible with this. Shorter response latencies to BPN compared to tones also suggests greater activation of excitatory inputs occurs at all sound pressure levels, even at supra-threshold levels.

BPN has a wider spectral peak than tones. The simplest comparable type of stimulation would be the simultaneous activation by two tones. Many studies of A1 used two tones, either simultaneous or successive, to study inhibitory interactions between tones (Calford and Semple, 1995; Pelleg-Toiba and Wollerg, 1989; Shamma et al., 1993; Sutter and Schreiner, 1991; Sutter et al., 1999; Sutter and Loftus, 2003). Such inhibition was also shown using random spectral stimulation (Barbour and Wang, 2003). Even though those inhibition manifested at side bands of tuning and were called side band inhibition, underlying inhibitory synaptic conductance are spectrally co-tuned with excitatory ones (see below). However, inhibitions at side bands were also shown using random spectral stimulation that had, like BPN, more distributed spectral power over frequencies than two tones (Barbour and Wang, 2003).

Considering the possibility of overlap between spectra of BPN sounds and side bands presumed to exhibit inhibition, one may expect BPN is more efficient in recruiting inhibitory inputs, thereby narrowing the tuning bandwidth and raising the threshold. Instead, we saw that BPN evoked A1 responses over a wider spectral range and at a lower intensity than tones. Our results thereby seemed inconsistent with the prediction that BPN would drive more inhibition than tones. However, to verify whether BPN does not evoke more inhibition than tones, there were two shortcomings in our data. One was the absence of strongly nonmonotonic MU responses to tones in our sample. It is known that non-monotonic neurons are particularly unresponsive to broad band noise, and more susceptible to inhibition (Calford and Semple, 1995; Phillips et al., 1985; Shamma and Symmes, 1985; Wu et al., 2006). Therefore, it is still possible to observe the inhibitory effect of BPN in neurons exhibiting non-monotonic rate-level functions to tones. However, under the condition of multiunit recording in the ketamine-anesthetized marmoset, it was rare to observe non-monotonic rate-level functions to tones in A1 and CM (Kajikawa et al., 2005). Also, since our stimulation paradigm was not completely similar to two tone suppression, our data cannot address the question whether BPN actually evoked more or less inhibition. We speculate that the most likely reason why we did not observe non-monotonic tuning to sound level was related to anesthesia, since non-monotonic responses were observed along with sharper tuning to tones more often in awake marmoset A1 (Sadagopan and Wang, 2008, 2009).

Multiunit recordings might obscure non-monotonicity of the rate-level functions in contrast to single-unit (SU) recordings, because they are composed of a mixture of monotonic and non-monotonic SU. Then, non-monotonic SU and their weaker response to BPN compared to tones could be masked by other monotonic SU. Therefore, to observe BPN-driven inhibition, it may be necessary to isolate non-monotonic SU. In the present study, we did not sort recorded spikes into single units because they were not separable into clusters using conventional methods. Also, we did not find clusters that were specific to responses to BPN. The latter finding also suggested that the stronger response evoked by BPN was not attributable to neurons that were unresponsive to tones.

Other studies have directly evaluated the changes in membrane conductance underlying synaptic potentials in rat A1 neurons (Tan et al., 2004; Wehr and Zador, 2003). They showed concurrent excitatory and inhibitory currents (EPSC and IPSC) evoked by tones. IPSC frequency tuning was balanced with that of EPSC, so the two currents have a common best frequency. They concluded that widened IPSC tuning may be partly responsible for side band inhibition (Wu et al., 2008). These findings may be incompatible with the hypothesis that summation of EPSP underlies stronger A1 responses to BPN than tones, because larger EPSP should be accompanied with larger inhibitory postsynaptic potential (IPSP) if EPSP and IPSP are tuned to tone frequency in balanced manner. Our results suggest that BPN presumably evoked relatively larger and faster summation of EPSP before that of IPSP, which is compatible with polysynaptic nature of IPSP (Tan et al., 2004; Wu et al., 2008; Wehr and Zador, 2003).

The same patters of stronger and faster responses to BPN than PT sounds were also observed in belt CM. Since onset latencies of responses to tone and BPN were equivalent in both areas, inputs driving synaptic potentials by those sounds were presumably of thalamic origins. Whereas CM receives thalamic afferents from different sources than A1 (de la Mothe et al., 2006b), CM responses to tones are robust with short latency, similar to A1 (Kajikawa et al., 2005, 2008; Kusmierek and Rauschecker, 2009; Recanzone et al., 2000). The spectral tuning properties of thalamic nuclei projecting to CM still await investigation, but would certainly affect tuning properties in CM.

Note that the use of anesthesia could affect auditory cortical responses by suppressing the sustained component (Wang et al., 2005), even though recurrent activity could still occur (Campbell et al., 2010). Ketamine selectively suppresses NMDA receptors (Thomson et al., 1985), while leaving fast-activating AMPA receptor function intact (Honore et al., 1988) or enhanced (Moghaddam et al., 1997). Thus, observation of only fast onset response and its summation is consistent with the pharmacological properties of ketamine that leaves AMPA receptor-mediated synaptic transmission.

Our data showed auditory cortical neurons could respond to sounds attenuated to −20 dB SPL. While this sound intensity is extremely low, it is near the established behavioral threshold of hearing of the marmoset (Seiden, 1958). In the cat, similar thresholds were reported at the levels of behavioral hearing (Neff and Hind, 1955), auditory nerve (Miller et al., 1997), and primary auditory cortex (under pentobarbital anesthesia that enhances GABAA receptors, Sutter, 2000). Therefore, the use of BPNs as stimuli under ketamine anesthesia may have promoted responses at low sound intensity.

In conclusion, stronger responses to BPN than tone in both A1 and CM were consistent with previous descriptions of BPN responses. More detailed properties of BPN responses additionally showed lower threshold intensity, wider tuning bandwidth and shorter onset latency, are suggestive of more excitatory summation than tone. Such mechanisms may also underlie BPN responses in the lateral belt areas.

Acknowledgements

This work was supported by NIH/NIDCD R01 04318 to T.A.H, and NIH K01 MH082415 to Y.K.

Footnotes

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References

  1. Barbour DL, Wang X. Auditory cortical responses elicited in awake primates by random spectrum stimuli. J Neurosci. 2003;23:7194–7206. doi: 10.1523/JNEUROSCI.23-18-07194.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Calford MB, Semple MN. Monaural inhibition in cat auditory cortex. J Neurophysiol. 1995;73:1876–1891. doi: 10.1152/jn.1995.73.5.1876. [DOI] [PubMed] [Google Scholar]
  3. Campbell RA, Schulz AL, King AJ, Schnupp JW. Brief sounds evoke prolonged responses in anesthetized ferret auditory cortex. J Neurophysiol. 2010;103:2783–2793. doi: 10.1152/jn.00730.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. de la Mothe LA, Blumell S, Kajikawa Y, Hackett TA. Cortical connections of the auditory cortex in marmoset monkeys: core and medial belt regions. J Comp Neurol. 2006a;496:27–71. doi: 10.1002/cne.20923. [DOI] [PubMed] [Google Scholar]
  5. de la Mothe LA, Blumell S, Kajikawa Y, Hackett TA. Thalamic connections of the auditory cortex in marmoset monkeys: core and medial belt regions. J Comp Neurol. 2006b;496:72–96. doi: 10.1002/cne.20924. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Doron NN, LeDoux JE, Semple MN. Redefining the tonotopic core of rat auditory cortex: physiological evidence for a posterior field. J Comp Neurol. 2002;453:345–360. doi: 10.1002/cne.10412. [DOI] [PubMed] [Google Scholar]
  7. Hackett TA. The comparative anatomy of the primate auditory cortex. In: Ghazanfar AA, editor. Primate Audition: Behavior and Neurobiology. Boca Raton, FL: CRC Press; 2002. pp. 199–226. [Google Scholar]
  8. Hackett TA. Information flow in the auditory cortical network. Hear Res. 2011;271:133–146. doi: 10.1016/j.heares.2010.01.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hackett TA, Kaas JH. Organization of the primate auditory cortex. In: Gazzaniga M, editor. The Cognitive Neurosciences. Third Edition. Cambridge, MA: The MIT Press; 2004. pp. 215–232. [Google Scholar]
  10. Honore T, Davies SN, Drejer J, Fletcher EJ, Jacobsen P, Lodge D, Nielsen FE. Quinoxalinediones: potent competitive non-NMDA glutamate receptor antagonists. Science. 1988;241:701–703. doi: 10.1126/science.2899909. [DOI] [PubMed] [Google Scholar]
  11. Imig TJ, Reale RA. Patterns of cortico-cortical connections related to tonotopic maps in cat auditory cortex. J Comp Neurol. 1980;192:293–332. doi: 10.1002/cne.901920208. [DOI] [PubMed] [Google Scholar]
  12. Kaas JH, Hackett TA. Subdivisions of auditory cortex and processing streams in primates. Proc Natl Acad Sci USA. 2000;97:11793–11799. doi: 10.1073/pnas.97.22.11793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Kajikawa Y, de la Mothe LA, Blumell S, Hackett TA. A comparison of neuron response properties in areas A1 and CM of the marmoset monkey auditory cortex: tones and broad band noise. J Neurophysiol. 2005;93:22–34. doi: 10.1152/jn.00248.2004. [DOI] [PubMed] [Google Scholar]
  14. Kajikawa Y, de la Mothe LA, Blumell S, Sterbing-D’Angelo SJ, D’Angelo W, Camalier CR, Hackett TA. Coding of FM sweep trains and twitter calls in area CM of marmoset auditory cortex. Hear Res. 2008;239:107–125. doi: 10.1016/j.heares.2008.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Kayser C, Petkov CI, Logothetis NK. Tuning to sound frequency in auditory field potentials. J Neurophysiol. 2007;98:1806–1809. doi: 10.1152/jn.00358.2007. [DOI] [PubMed] [Google Scholar]
  16. Kusmierek P, Rauschecker JP. Functional specialization of medial auditory belt cortex in the alert rhesus monkey. J Neurophysiol. 2009;102:1606–1622. doi: 10.1152/jn.00167.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Maier JX, Ghazanfar AA. Looming biases in monkey auditory cortex. J Neurosci. 2007;27:4093–4100. doi: 10.1523/JNEUROSCI.0330-07.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Miller RL, Schilling JR, Franck KR, Young ED. Effects of acoustic trauma on the representation of the vowel /ε/ in cat auditory nerve fibers. J Acoust Soc Am. 1997;101:3602–3616. doi: 10.1121/1.418321. [DOI] [PubMed] [Google Scholar]
  19. Moghaddam B, Adams B, Verma A, Daly D. Activation of glutamatergic neurotransmission by ketamine: a novel step in the pathway from NMDA receptor blockade to dopaminergic and cognitive disruptions associated with the prefrontal cortex. J Neurosci. 1997;17:2921–2927. doi: 10.1523/JNEUROSCI.17-08-02921.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Morel A, Kaas JH. Subdivisions and connections of auditory cortex in owl monkeys. J Comp Neurol. 1992;318:27–63. doi: 10.1002/cne.903180104. [DOI] [PubMed] [Google Scholar]
  21. Neff WD, Hind JE. Auditory thresholds of the cat. J Acoust Soc Am. 1955;27:480–483. [Google Scholar]
  22. Ojima H, Murakami K. Intracellular characterization of suppresive responses in supragranular pyramidal neurons of cat primary auditory cortex in vivo. Cereb Cortex. 2002;12:1079–1091. doi: 10.1093/cercor/12.10.1079. [DOI] [PubMed] [Google Scholar]
  23. Pandya PK, Rathbun DL, Moucha R, Engineer ND, Kilgard MP. Spectral and temporal processing in rat posterior auditory cortex. Cereb Cortex. 2008;18:301–314. doi: 10.1093/cercor/bhm055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Pelleg-Tobia R, Wollberg Z. Tuning properties of auditory cortex cells in the awake squirrel monkey. Exp Brain Res. 1989;74:353–364. doi: 10.1007/BF00248869. [DOI] [PubMed] [Google Scholar]
  25. Phillips DP, Cynader MS. Some neural mechanisms in the cat’s auditory cortex underlying sensitivity to combined tone and wide-spectrum noise stimuli. Hear Res. 1985;18:87–102. doi: 10.1016/0378-5955(85)90112-1. [DOI] [PubMed] [Google Scholar]
  26. Phillips DP, Orman SS, Musicant AD, Wilson GF. Neurons in the cat's primary auditory cortex distinguished by their responses to tones and wide-spectrum noise. Hear Res. 1985;18:73–86. doi: 10.1016/0378-5955(85)90111-x. [DOI] [PubMed] [Google Scholar]
  27. Rauschecker JP, Tian B, Hauser M. Processing of complex sounds in the macaque nonprimary auditory cortex. Science. 1995;268:111–114. doi: 10.1126/science.7701330. [DOI] [PubMed] [Google Scholar]
  28. Rauschecker JP, Tian B. Mechanisms and streams for processing of “what” and “where” in auditory cortex. Proc Natl Acad Sci USA. 2000;97:11800–11806. doi: 10.1073/pnas.97.22.11800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Rauschecker JP, Tian B. Processing of band-passed noise in the lateral auditory belt cortex of the rhesus monkey. J Neurophysiol. 2004;91:2578–2589. doi: 10.1152/jn.00834.2003. [DOI] [PubMed] [Google Scholar]
  30. Recanzone GH. Response profiles of auditory cortical neurons to tones and noise in behaving macaque monkeys. Hear Res. 2000;150:104–118. doi: 10.1016/s0378-5955(00)00194-5. [DOI] [PubMed] [Google Scholar]
  31. Recanzone GH, Guard DC, Phan ML. Frequency and intensity response properties of single neurons in the auditory cortex of the behaving macaque monkey. J Neurophysiol. 2000;83:2315–2331. doi: 10.1152/jn.2000.83.4.2315. [DOI] [PubMed] [Google Scholar]
  32. DE Ribaupierre F, Goldstein MH, Jr, Yeni-Komshian G. Intracellular study of the cat's primary auditory cortex. Brain Res. 1972;48:185–204. doi: 10.1016/0006-8993(72)90178-3. [DOI] [PubMed] [Google Scholar]
  33. Romanski LM, Tian B, Fritz JB, Mishkin M, Goldman-Rakic PS, Rauschecker JP. Dual streams of auditory afferents target multiple domains in the primate prefrontal cortex. Nat Neurosci. 1999;2:1131–1136. doi: 10.1038/16056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Sadagopan S, Wang X. Level invariant representation of sounds by populations of neurons in primary auditory cortex. J Neurosci. 2008;28:3415–3426. doi: 10.1523/JNEUROSCI.2743-07.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Sadagopan S, Wang X. Nonlinear spectrotemporal interactions underlying selectivity for complex sounds in auditory cortex. J Neurosci. 2009;29:11192–11202. doi: 10.1523/JNEUROSCI.1286-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Schwarz DWF, Tomlinson RWW. Spectral response patterns of auditory cortex neurons to harmonic complex tones in alert monkey (Macaca mulatta) J Neurophysiol. 1990;64:282–298. doi: 10.1152/jn.1990.64.1.282. [DOI] [PubMed] [Google Scholar]
  37. Seiden HR. PhD dissertation. Princeton, NJ: Princeton University; 1958. Auditory acuity of the marmoset monkey (Hapale jacchus) [Google Scholar]
  38. Shamma SA, Fleshman JW, Wiser PR, Versnel H. Organization of response areas in ferret primary auditory cortex. J Neurophysiol. 1993;69:367–383. doi: 10.1152/jn.1993.69.2.367. [DOI] [PubMed] [Google Scholar]
  39. Shamma SA, Symmes D. Patterns of inhibition in auditory cortical cells in awake squirrel monkeys. Hear Res. 1985;19:1–13. doi: 10.1016/0378-5955(85)90094-2. [DOI] [PubMed] [Google Scholar]
  40. Sutter ML. Shapes and level tolerances of frequency tuning curves in primary auditory cortex: quantitative measures and population codes. J Neurophysiol. 2000;84:1012–1025. doi: 10.1152/jn.2000.84.2.1012. [DOI] [PubMed] [Google Scholar]
  41. Sutter ML, Loftus WC. Excitatory and inhibitory intensity tuning in auditory cortex: evidence for multiple inhibitory mechanisms. J Neurophysiol. 2003;90:2629–2647. doi: 10.1152/jn.00722.2002. [DOI] [PubMed] [Google Scholar]
  42. Sutter ML, Schreiner CE. Physiology and topography of neurons with multipeaked tuning curves in cat primary auditory cortex. J Neurophysiol. 1991;65:1207–1226. doi: 10.1152/jn.1991.65.5.1207. [DOI] [PubMed] [Google Scholar]
  43. Sutter ML, Schreiner CE, McLean M, O'Connor KN, Loftus WC. Organization of inhibitory frequency receptive fields in cat primary auditory cortex. J Neurophysiol. 1999;82:2358–2371. doi: 10.1152/jn.1999.82.5.2358. [DOI] [PubMed] [Google Scholar]
  44. Tan AYY, Wehr M. Balanced tone-evoked synaptic excitation and inhibition in mouse auditory cortex. Neuroscience. 2009;16:1302–1315. doi: 10.1016/j.neuroscience.2009.07.032. [DOI] [PubMed] [Google Scholar]
  45. Tan AYY, Zhang LI, Merzenich MM, Schreiner CE. Tone-evoked excitatory and inhibitory synaptic conductances of primary auditory cortex neurons. J Neurophysiol. 2004;92:630–643. doi: 10.1152/jn.01020.2003. [DOI] [PubMed] [Google Scholar]
  46. Thomson AM, West DC, Lodge D. An N-methylaspartate receptor-mediated synapse in rat cerebral cortex: a site of action of ketamine? Nature. 1985;313:479–481. doi: 10.1038/313479a0. [DOI] [PubMed] [Google Scholar]
  47. Tian B, Reser D, Durham A, Kustov A, Rauschecker JP. Functional specialization in rhesus monkey auditory cortex. Science. 2001;292:290–293. doi: 10.1126/science.1058911. [DOI] [PubMed] [Google Scholar]
  48. Volkov IO, Galazjuk AV. Formation of spike response to sound tones in cat auditory cortex neurons: interaction of excitatory and inhibitory effects. Neuroscience. 1991;43:307–321. doi: 10.1016/0306-4522(91)90295-y. [DOI] [PubMed] [Google Scholar]
  49. Wang X, Lu T, Snider RK, Liang L. Sustained firing in auditory cortex evoked by preferred stimuli. Nature. 2005;435:341–346. doi: 10.1038/nature03565. [DOI] [PubMed] [Google Scholar]
  50. Wehr M, Zador AM. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature. 2003;426:442–446. doi: 10.1038/nature02116. [DOI] [PubMed] [Google Scholar]
  51. Wu GK, Li P, Tao HW, Zhang LI. Nonmonotonic synaptic excitation and imbalanced inhibition underlying cortical intensity tuning. Neuron. 2006;52:705–715. doi: 10.1016/j.neuron.2006.10.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Wu G, Arbuckie R, Liu B-H, Tao HW, Zhang LI. Lateral sharpening of cortical frequency tuning by approximately balanced inhibition. Neuron. 2008;58:132–143. doi: 10.1016/j.neuron.2008.01.035. [DOI] [PMC free article] [PubMed] [Google Scholar]

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