Stimuli

Stimuli were 800-ms sinusoidally amplitude-modulated (AM) broadband noise bursts. Modulation frequencies were 2.5, 5, 10, 15, 20, 30, 60, 120, 250, 500, and 1,000 Hz. Depth sensitivity functions were collected at one fixed modulation frequency with modulation depths of 0% (unmodulated), 6%, 16%, 28%, 40%, 60%, 80%, and 100%. The unmodulated noise served as a comparison to determine how well neurons could determine whether or not a sound was modulated. For all these stimuli the noise carrier was “frozen”; the noise was created using the same random number sequence, producing a stimulus waveform that was identical on all trials, save for the modulation envelope.

Sound generation has been described previously (O'Connor et al. 2010). The sound signals were constructed with MATLAB (MathWorks), digital-to-analog (D/A) converted (Cambridge Electronic Design, model 1401), passed through programmable (TDT Systems PA5) and passive (Leader LAT-45) attenuators, amplified (Radio Shack MPA-200), and delivered to a speaker. We used two different sound booths (IAC: 2.9 × 3.2 × 2.0 m and 1.2 × 0.9 × 2.0 m) equipped with different speakers positioned at ear level. One booth had a Radio Shack PA-110 speaker 1.5 m in front of the subject, while the other had a Radio Shack Optimus Pro-7AV positioned 0.8 m in front of the subject. Sounds were generated at a sampling rate of 100 kHz and had cosine-ramped onsets and offsets with 5.0-ms rise and fall time. Sound intensity was calibrated with a sound-level meter (Bruel & Kjaer model 2231) to 63 dB sound pressure level (SPL) at the approximated midpoint of the animals' two pinnae.

Behavioral Task

The monkeys were trained to discriminate AM noise from unmodulated noise. This also can be regarded as AM detection because subjects detect whether a sound was amplitude modulated. Task specifics were as follows. First, monkeys began a trial by pressing and holding down a lever for at least 500 ms. Then, two 800-ms sounds were presented sequentially with a 400-ms intersound interval (ISI). Within a trial, the first (standard) sound was always unmodulated noise and the second sound (test stimulus) was either another unmodulated noise (nontarget) or an AM noise (target). Note that here we reserve the word “stimulus” for test sound (not the standard). During each recording session, the modulation frequency of target stimuli was set at the best modulation frequency (BMF) of the multiple-unit (MU) response from the same electrode (see Unit Recording). Target modulation depths were 6%, 16%, 28%, 40%, 60%, 80%, and 100%, presented with equal probability. Subjects were trained to release the lever in response to AM targets. They were required to wait for stimulus offset to respond and to respond within an 800-ms response window following the target stimulus offset. When the test stimulus was an unmodulated nontarget (12.5% of the trials), subjects were required to continue depressing the lever for the entire response window. Subjects were given liquid rewards for all correct responses: hits (a lever release for target trials) and correct rejections (withholding lever release for nontarget trials). We notified subjects of incorrect responses—misses (not releasing the lever on target trials) and false alarms (releasing the lever on nontarget trials)—by turning off an incandescent light placed in front of them. Subjects were also given a time-out period of 15–60 s after false alarms. This enabled us to keep the false alarm rates at the relatively low level of ~10% as reported in Niwa et al. (2012b). Behavioral thresholds were reported in Niwa et al. (2012a).

Unit Recording

The neural recording procedures were described previously (Niwa et al. 2012a, 2012b, 2013), so we will briefly recap them here. To allow AC access, a 2.7-cm-diameter CILUX chamber (Crist Instruments) was implanted over the portion of parietal cortex immediately above AC. The chamber held a plastic grid with 27-gauge holes, covering a 15 × 15-mm area at 1-mm intervals. Each recording day, a stainless steel transdural guide tube was inserted through a grid hole. A tungsten microelectrode (1–4 MΩ, FHC; 0.5–1 MΩ, Alpha-Omega) was put through the guide tube and lowered through parietal cortex into ML or A1 by a hydraulic microdrive (FHC). During this procedure macaques were head-restrained by a titanium post. Electrophysiological signals were amplified (A-M Systems model 1800), filtered (0.3–10 kHz; A-M Systems model 1800 and Krohn-Hite model 3382), and sent to an A/D converter (CED model 1401, 50 kHz sampling rate). Action potentials were assigned to individual neurons on- and off-line with SPIKE2's (CED) waveform-matching algorithm.

At each recording site, we first determined the BMF of MU activity by presenting unmodulated noise and 100% depth, 800-ms AM noise at all modulation frequencies. Receiver operating characteristic (ROC) areas (ROCas), based on both firing rate and vector strength (VS, a measure of phase-locking), were calculated at each modulation frequency (see Neurometric analysis for details). BMF_VS was defined as the modulation frequency with the greatest VS-based ROCa, while BMF_SC was the frequency with the firing-rate-based ROCa most deviant from 0.5. The farther the ROCa from 0.5, the larger the difference between responses to AM and unmodulated noise. This means that the BMF was the modulation frequency at which the unit could best distinguish AM from an unmodulated sound. When BMF_VS and BMF_SC were different, we chose either BMF_VS or BMF_SC as the modulation frequency for the subsequent “AM sensitivity” experiment. This selection was alternated from day to day to avoid a bias toward one of the BMF measures.

After BMF determination, the AM sensitivity of single units (SUs) and MUs was assessed by collecting response vs. modulation depth functions at the BMF during two behavioral conditions: 1) while animals performed the task (behaving condition) and 2) while they were presented with the same stimuli and received randomly timed liquid rewards for sitting quietly (passive condition). The sequence of behaving and passive experiments was alternated daily to eliminate order effects. For recording, the monkeys sat in an acoustically transparent primate chair, with their head restrained to the chair, in a double-walled, sound-attenuated, foam-lined booth.

Effect of Engagement on ML Neuron Rate-Based AM Discriminability Depends on Time Period of Stimulus Presentation

Animals' engagement in AM discrimination affects ML neurons' AM discriminability differently during the first (0–400 ms) and second (400–800 ms) halves of the test stimulus. This is exemplified by three ML neurons whose responses are depicted in Figs. 1–3. The neuron in Fig. 1 shows improvement in firing rate-based AM discriminability due to task engagement during the first half of the test stimulus but deterioration during the second half. The neuron in Fig. 2 shows a change in its firing rate response characteristics to modulation depth from monotonically increasing during the first half to nonmonotonic during the second. The neuron in Fig. 3 shows a change in its firing rate response characteristics to modulation depth, from monotonically increasing during the first stimulus half to monotonically decreasing during the second half.

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Fig. 1.

Example of the activity of a middle lateral belt (ML) single unit (SU) showing improvement in firing rate-based amplitude modulation (AM) discriminability due to task engagement during the 1st half of the test stimulus but deterioration during the 2nd half. A and B: raster plots of a SU response to 30-Hz AM as modulation depth is varied from 0% (bottom) to 100% (top) in the passive (A) and behaving (B) conditions. C and D: average firing rate during the 1st (C) and 2nd (D) halves of the stimulus plotted as a function of modulation depth for passive and behaving conditions. E and F: receiver operating characteristic (ROC) area (ROCa) based on firing rate during the 1st (E) and 2nd (F) halves of the stimulus plotted as a function of modulation depth for passive and behaving conditions. Neural ROCa yields the probability that an ideal observer would detect modulation based solely on the neural responses. An ROCa of 0.5 corresponds to chance performance. The farther ROCa is from 0.5, the better the neuron is at discriminating AM from an unmodulated sound. An ROCa of 1 or 0 = perfect AM detection, where 1 indicates higher firing rates for modulated sounds than to the unmodulated sound and 0 indicates lower firing rate to modulated sounds.

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Fig. 2.

Example of an ML SU that shows a change in its firing rate response characteristics to modulation depth from monotonically increasing during the 1st stimulus half to nonmonotonic during the 2nd half. Data are presented in the same format as Fig. 1.

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Fig. 3.

Example of an ML SU that shows a change in its firing rate response characteristics to modulation depth from monotonically increasing during the 1st stimulus half to monotonically decreasing during the 2nd half. Data are presented in the same format as Fig. 1.

For the ML SU in Fig. 1, in the passive condition the firing rate increases with depth during both first and second halves of the stimuli (Fig. 1, C and D). In the behaving condition the dependence of firing rate on modulation depth appears different than in the passive condition. During the first half, the firing rate roughly increases monotonically with depth (Fig. 1C), as in the passive condition. During the second half, firing rate decreases with increasing depth in the behaving condition (Fig. 1D). The decrease during behavior is largely due to increased activity 500–650 ms after stimulus onset for 0–40% depth in the behaving condition (Fig. 1B), which lessens at higher depths. This pattern of activity between 500 and 650 ms is not seen in the passive condition (Fig. 1A).

During the first half of the stimulus the neuron can better discriminate AM in the behaving than the passive condition. When the SU's firing rates during 0–400 ms in the behaving and passive conditions are compared, the rate response to both unmodulated noise and low-modulation-depth AM (0–16%) is slightly greater in the behaving than passive condition (Fig. 1C). However, at higher modulation depths (particularly at 28–40%) firing rate increases much more in the behaving condition (Fig. 1C). As a result, in this 400-ms window this SU exhibits greater response differences between AM and unmodulated noise in the behaving than the passive condition (Fig. 1C). This suggests that engagement in the AM task improves neural AM discrimination at modulation depths > 16% (which is approximately behavioral threshold) during this portion (the first 400 ms) of the stimulus, which is supported by neurometric analysis (Fig. 1E).

In contrast to the first half of the stimulus, this SU better discriminates AM from unmodulated noise in the passive condition during the second half of the stimulus (Fig. 1F). From 400 to 800 ms after stimulus onset, the response to unmodulated noise was still higher in the behaving than the passive condition, but the response to 60–100% AM was quite similar in both behaving and passive conditions (Fig. 1D). The result is that, during behavior, the firing rate response to 60–100% AM is slightly less than the firing rate response to unmodulated noise during the second half of the stimulus (the last 3 open circles, at 60–100%, are slightly less than first open circle, at 0%, in Fig. 1D). This leads to poorer neural discrimination ability (points closer to ROCa = 0.5) during the second half in the behaving than the passive condition. The neurometric functions appear to show that the neuron weakly discriminates AM with increased firing rate relative to the unmodulated noise response at low depths and with decreased activity relative to the unmodulated noise response at higher depths. This is seen in Fig. 1F as ROCa > 0.5 for 6–40% and < 0.5 for 60–100%. However, these points are all relatively close to ROCa = 0.5.

Figure 2 shows an example neuron that during the 2nd half of the stimulus discriminates AM with increased firing rate relative to the unmodulated noise response at low depths and with decreased activity relative to the unmodulated noise response at higher depths. In this example a permutation test showed that ROCa was significantly above 0.5 in the behaving condition during the second half at 16% depth and significantly below 0.5 at 60 and 80% depths. In the passive condition, this SU and the SU in Fig. 3 monotonically increase firing rate with modulation depth during early and late portions of the stimulus period (Figs. 2 and ​and3,3, C and D). Again, in the behaving condition, these SUs monotonically increase firing rate with depth only during the first half of the stimulus (Fig. 2C, Fig. 3C) and not in the second half (Fig. 2D, Fig. 3D). Also during the first 400 ms AM discrimination is better in the behaving condition (Fig. 2E, Fig. 3E), but comparisons of neural discriminability during the 400–800 ms period after stimulus onset are more complicated. The SU in Fig. 3 decreases firing rate relatively monotonically with depth (Fig. 3F). For all three example neurons, firing rate during the 400–800 ms period is greater in the behaving (than passive) condition for 0–40% AM and smaller in the behaving condition for 60–100% AM (Fig. 1D, Fig. 2D, Fig. 3D).

ML Population Summary: Behavioral State Affects Rate-Based AM Discriminability Differently for Neurons with Increasing and Decreasing Rate-Depth Functions

For the entire ML population, the dependence of performance on task engagement is strikingly different during the stimulus's first and second halves. During engagement, neuronal AM sound discrimination improves during the first half and worsens during the second half of the stimulus (Fig. 4, A–D). During the first half, firing rate-based ROCa is significantly greater in the behaving than the passive condition for MUs (Fig. 4A) and SUs (Fig. 4B). On the other hand, during the second half, ML ROCa is significantly worse in the behaving condition (Fig. 4, C and D). Note that Fig. 4, A–D, have a smaller scale than the rest of Fig. 4 to better display ML differences between behaving and passive conditions. Figure 4, A–D, appear to show that task engagement significantly improves rate-based AM discriminability during the first half of the stimulus but significantly impairs it during the second half. This differs from A1, where 1) ROCa monotonically increased with modulation depth for behaving and passive conditions and 2) population ROCa increased for all depths (although not significantly at all depths) in the behaving compared with the passive condition for both halves (albeit smaller improvements were seen for the second half in the behaving compared with the passive condition; Fig. 4, I–L).

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Fig. 4.

Population ROCa-depth functions for 1st and 2nd halves of the test stimulus in ML and primary auditory cortex (A1) neurons. A and B: ML population mean ROCas based on firing rate during the 1st half of the stimulus plotted as a function of modulation depth for the passive and behaving conditions for all recorded multiunits (MUs; A) and SUs (B) in ML. C and D: population mean, rate-based ROCas for the second 400 ms of the stimulus are shown for MUs (C) and SUs (D). E–H: plots are the same as in A–D except that ROCa is reflected around 0.5. This means the y-axis shows the absolute value of the difference in ROCa from 0.5. So, for example, an ROCa of 0.4 → reflected ROCa of 0.6 and an ROCa of 0.1 → reflected ROCa of 0.9, etc. I–L are the same as A–D except for A1. M–P are the same as E–H except for A1. In all panels, P values denote Wilcoxon signed-rank test comparing collapsed-depth ROC areas between behaving and passive conditions. *Individual modulation depths where a Wilcoxon signed-rank test yielded a value of P < 0.05.

However, when we consider the potential contribution of neurons that encode modulation by decreasing firing for modulated sounds, a different picture emerges. The farther ROCa is from 0.5, the better a unit is at discriminating a modulated from an unmodulated sound. An ROCa of 1 means perfect ability to discriminate AM from an unmodulated sound with increased firing rate for AM; an ROCa of 0 means perfect ability to discriminate AM from an unmodulated sound with decreased firing rate for AM. Therefore reflecting ROCa around 0.5 (Fig. 4, E–H and M–P) for each neuron reveals how well they discriminate AM from an unmodulated noise adjusting for neurons that encode AM with decreased activity. It should be noted that mathematically this is similar to taking the absolute value, so all ROCas will increase, just as the mean of a series of positive and negative numbers will always be smaller than the mean of their absolute values. What is important in Fig. 4, E–H and M–P, is how differences between active and passive change with reflection and how shapes of functions change. When we reflect ROCa in ML, we see significant improvement during the first, and now also during the second, half (Fig. 4, G and H) of the stimulus. Also, the function monotonically increases for reflected ROCa as opposed to the nonmonotonic functions for ROCa (Fig. 4, C and D). For A1, reflecting ROCa (compared to not reflecting) resulted in smaller or no improvements in the passive compared with the behaving condition (Compare Fig. 4, I to M, J to N, K to O, and L to P). We further probe this by analyzing population mean ROCas separately for increasing and decreasing cells, which will allow a clearer picture to emerge.

Separately analyzing cells whose firing rate either increases or decreases as a function of modulation depth is important to understand possible neural codes for AM. In ML some neurons increase firing rate with increasing modulation depth while others decrease firing rate with depth, but most A1 neurons increase firing rate with modulation depth and few decrease (Niwa et al. 2013). The brain could use the ML information for AM discrimination by separately decoding increasing and decreasing functions as two complementary populations of neurons (or subtracting them). Improved AM discriminability for neurons with increasing rate-depth functions would result in increased ROCa (from 0.5), while for neurons with decreasing rate-depth functions improved AM discrimination would yield decreased values below 0.5. If both populations show AM discrimination improvement, the net effect on ROCa of the summed activity across the entire population of neurons would be reduced because the increase in ROCa from cells with increasing functions would cancel the decrease in ROCa by cells with decreasing functions. Therefore, population summaries are presented separately for increasing and decreasing rate functions as well as for the entire ML population, i.e., combining “increasing” and “decreasing” cells (where an increasing cell refers to a neuron with an increasing rate-depth function).

Before showing the population summary for increasing and decreasing functions, we need to discuss how these two groups are defined. As shown by the three SU examples (Figs. 1–3), the slope of neurometric curves for one neuron can change signs between the first and second stimulus halves and between behaving and passive conditions. Since a neuron's response depends on both the time during the stimulus and task conditions, we needed a way to estimate their composite response to modulation depth. To do this, we first created a neurometric firing rate-based ROCa-depth function during the entire stimulus separately for behaving and passive conditions. We then averaged the two functions and calculated the slope with linear regression (Fig. 5, top). We defined a response as “increasing” if it had a positive slope and “decreasing” if it had a negative slope. It is important to note that increases in ROCa from 0.5 correspond to increases in firing rate and decreases in ROCa from 0.5 are decreases in firing rate. All ROCa-depth curves start at 0.5 for 0% depth by definition, so an increasing ROCa-depth curve also would result in an increasing firing rate-depth curve (and vice versa for decreasing). The SU examples from Figs. 1–3 are shown in red in Fig. 5, and all had positive slopes (Fig. 5B; Figs. 1, ​,2,2, and ​and33 neurons' slopes = 0.0023, 0.0041, and 0.0015, respectively). The distribution of the slopes is shown for MUs (Fig. 5A) and for SUs (Fig. 5B) in ML, where 72% of the MU and 63% of the SU recordings had increasing functions. For comparison, the distribution of slopes is also shown for MU (Fig. 5C) and SU (Fig. 5D) recordings in A1, where 81% of MU and 78% of SU recordings had positive slopes. The slope magnitudes in ML were significantly lower than in A1 (P = 0.0013, rank sum test), and the proportion of increasing SU functions in ML was significantly lower than in A1 (P = 0.0067, χ²).

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Fig. 5.

Top: demonstration of how the neurometric slope was calculated. A–D: distributions of the slopes of neurometric (ROCa vs. modulation depth) functions by linear regression for MUs in ML (A), SUs in ML (B), MUs in A1 (C), and SUs in A1 (D). The slopes of the 3 examples of Figs. 1–3 are shown in red (Fig. 1 neuron's slope = 0.0023, Fig. 2 neuron's slope = 0.0041, Fig. 3 neuron's slope = 0.0015).

For the population of increasing units in ML, AM discriminability is significantly better in the behaving than the passive condition during the first half (0–400 ms) of the stimulus. During this period, MU firing rate-based ROCa is significantly greater in the behaving than the passive condition for 16–100% AM (Fig. 6A). SUs were also more sensitive in the behaving condition, although fewer depths reached significance (Fig. 6B). To test the overall effect of task engagement, ROCa data were combined across depths in each condition, and a single Wilcoxon signed-rank test was performed. For MUs and SUs, we found a significant increase in across-depth, rate-based ROCa in the behaving compared with the passive condition (Fig. 6, A and B), indicating that task engagement significantly improves neuronal AM discriminability in the first half of the stimulus for increasing cells.

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Fig. 6.

Summary of AM discrimination ability for the population of ML cells with increasing rate vs. modulation depth functions. A–D: data are presented in the same format as in Fig. 4, A–D. *Individual modulation depths where a Wilcoxon signed-rank test yielded a value of P < 0.05. P values at bottom right are the P values obtained by collapsing data across all depths. E and F: scatterplots of firing rate-based ROCas during 1st half of the stimulus for 60% (left), 80% (center), and 100% (right) AM for MUs (E) and SUs (F). Each unit's ROCa in the passive condition is plotted on the x-axis and that for the behaving condition on the y-axis. G and H: same as E and F but for ROCa during 2nd half of stimulus. Diagonal line is where ROCa is equal for both conditions, and the quadrants are marked at ROCa = 0.5.

On the other hand, during the second half of the test stimulus there was no significant change in ROCa between the behaving and passive conditions at any individual depth or collapsed over depths for MUs and SUs with increasing rate-depth functions, except for a small difference at 16% (P = 0.0179) for SUs (Fig. 6D). Although no statistically significant difference in ROCa was found between the behaving and passive conditions at high modulation depths (60–100%) during the second half, the population mean ROCa in the behaving condition at these depths appears lower than the ROCa in the passive condition (Fig. 6, C and D). The lack of a significant difference might be due to the large unit-to-unit variability in the change in ROCa between the two conditions. Fig. 6, E and G (MUs) and F and H (SUs), show pairwise comparisons of ROCa between the behaving and passive conditions for all increasing MUs and SUs at 60%, 80%, and 100% depths. Each point on these plots represents a unit, whose ROCa in the behaving condition is given on the y-axis and the passive ROCa on the x-axis. Points falling on the diagonal line indicate no change in ROCa between the two conditions. Points above the diagonal line indicate increased ROCa in the behaving compared with the passive condition, and those below indicate a decrease during behavior. During the second half of the stimulus, points are spread widely across the diagonal line (Fig. 6, G and H), showing unit-to-unit variability in how ROCa changes between the two conditions. However, some neurons' ROCas lie far below the diagonal line, falling into the bottom right quadrant of the plots. These units are largely responsible for pulling down the population mean ROCa for the behaving condition. Units lying in this quadrant fire less to AM than to unmodulated noise in the behaving condition (ROCa < 0.5) but fire more to AM than to noise in the passive condition (ROCa > 0.5); they flip the sign of their response to AM from “positive” (excitatory) in the passive condition to “negative” (suppressive) in the behaving condition during the second half of the stimulus. It is important to remember that classifying a unit's response as increasing or decreasing is based on averaging ROCa for behaving and passive conditions over the entire 800-ms stimulus, which explains why units that decrease during the final 400 ms in the behaving condition can be classified as increasing. The examples shown in Figs. 1–3 are representative of SUs that decrease their activity during the second half of the test stimulus only in the behaving condition (see Figs. 1–3). Interestingly, when the same scatterplots are made for the first half of the stimulus (Fig. 6, E and F), few points fall into the fourth quadrant, suggesting that the reversal of response sign occurs only later during the test stimulus. This effect may occur because AM at the highest modulation depths can be discriminated very easily, and the decision to respond may be made during the stimulus. Also, the fact that the animals are not permitted to respond until after stimulus offset, requiring them to suppress early behavioral responses to AM at higher depths, may cause sustained inhibition of firing to these AM stimuli as the test stimulus continues in time.

For the population of ML units with decreasing rate-depth functions, AM discriminability is significantly better in the behaving than the passive condition during the second half of the stimulus (Fig. 7, C and D); during the first half the effect is weaker for MUs (Fig. 7A) and nonexistent for SUs (Fig. 7B). Remember that for decreasing units lower ROCa (toward 0) means better AM discriminability. During the first half, MU firing rate-based ROCa is significantly lower in the behaving than the passive condition, but the effect is relatively small and limited to near-threshold depths (Fig. 7A). During the second half of the stimulus, MU rate-based ROCa is significantly lower (better) in the behaving than the passive condition at most depths above behavioral threshold (Fig. 7C). For SUs, engagement in the task significantly improves AM discriminability only during the second half of the test stimulus (Fig. 7D).

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Fig. 7.

Summary of AM discriminability for the population of ML cells with decreasing rate vs. modulation depth functions. A–D: data are presented in the same format as in Fig. 6, A–D.

Together, these results demonstrate that rate-based AM discriminability for ML cells with increasing rate-depth functions improves under conditions of active engagement early during the stimulus, but the improvement disappears later (Fig. 6). On the other hand, the improvement in rate-based AM discriminability for ML cells with decreasing functions is more pronounced later during the stimulus (Fig. 7).

Comparison of Change in Rate-Based AM Discrimination Over Time Between A1 and ML

We conducted ROC analyses using several time windows for A1 and ML and found that 1) the AM discriminability of cells with increasing rate-depth functions in the behaving condition is more strongly dependent on the time course of stimulus presentation in ML than in A1 and 2) engagement in the AM task affects the AM discriminability of cells with decreasing functions differently in A1 and ML. For this analysis, rate-based ROC areas were calculated with 400-ms time windows beginning at 0, 100, 200, 300, and 400 ms after stimulus onset to determine the change in AM discriminability over time.

For ML increasing units, the discriminability of 100–60% AM stimuli in the behaving condition declines as the time window slides from the start to the end of stimulus presentation for both MUs (Fig. 8A) and SUs (Fig. 8B). Note that the ML population mean AM discriminability in the behaving condition over time actually becomes worse than in the passive, as indicated by the crossing of the two plots (60%-100% depth). In contrast to ML, A1's rate-based AM discriminability at higher depths in the behaving condition always stays at or above the level seen in the passive condition [Fig. 8, C (MUs) and D (SUs)]. The decline in AM discriminability over time in ML in the behaving condition is absent for 6–40% AM stimuli (Fig. 8, A and B). Instead, AM discriminability in the behaving condition appears constant or improves slightly over the time course of stimulus presentation.

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Fig. 8.

ROCas (based on firing rate) are calculated in 400-ms time windows starting at various times (0, 100, 200, 300, and 400 ms) after stimulus onset. A–D: population mean rate-based ROC area of “increasing” cells plotted as a function of start time of the windows at each modulation depth (from 100% on left to 6% on right) for the passive and behaving conditions for MUs in ML (A), SUs in ML (B), MUs in A1 (C), and SUs in A1 (D). E–H: population mean rate-based ROCa of “decreasing” cells plotted as a function of start time of the time windows at each modulation depth (from 100% on left to 6% on right) for the passive and behaving conditions for MUs in ML (E), SUs in ML (F), MUs in A1 (G), and SUs in A1 (H).

For ML decreasing units, the population mean ROCa in the behaving condition declines steeply toward 0 as the time window slides from the start to the end of stimulus for both MUs (Fig. 8E) and SUs (Fig. 8F). The decline of ML ROCa in the passive condition is not as steep as in the behaving condition. The result is an increase in the difference in ROCa between these two conditions over time; the improvement in AM discriminability due to engagement in the task is greater later during the stimulus at all depths, for both MUs and SUs (recall that lower ROCa means better discriminability for decreasing units). For A1 decreasing units, the population mean ROCa at 60–100% depth in the behaving condition also declines toward 0 as the time window slides from the start to the end of stimulus (Fig. 8, G and H), while a decline is not clearly seen for the passive condition. The key difference between A1 and ML is that for decreasing functions in A1 ROCa at 60–100% depths in the behaving condition starts much worse (closer to 0.5) than in the passive condition early during the test stimulus and goes toward the passive level over time. Thus in A1 AM discriminability during the early stimulus period is worse in the behaving than the passive condition and improves toward the passive level over time. In ML decreasing functions at higher depths, ROCas for behaving and passive conditions are similar early in the stimulus and over time the behaving condition gains an advantage.

The aggregate results of Fig. 8 suggest that, in ML, decreasing cells improve their AM discriminability throughout the test stimulus, although the improvement is larger later. One interpretation is that in ML both increasing and decreasing cells are suitable and relevant for encoding AM depth.

Rate Response to Unmodulated Noise Increases Later During Stimulus, Causing ROC Areas to Decrease Over Time

Our analysis revealed that in ML ROCas in the behaving condition appear to decrease over time at a faster pace than in the passive condition. This is true for higher modulation depths and for units with both increasing and decreasing rate-depth functions. The ROCa quantifies the overlap between firing rate distributions in response to AM and unmodulated noise, but it does not explicitly tell us what caused the decline in ROCa. Two possible sources that can cause ROCa to decline are 1) decreased firing rate in response to AM and/or 2) increased firing rate in response to unmodulated noise. The results presented below indicate that the increase in the response to unmodulated noise over time is a major contributor to the decline in ROCa in the behaving condition.

The population average firing rate (relative to spontaneous rate) in the behaving and passive conditions was analyzed with the same time windows as in Fig. 8 and shown separately for cells with increasing (Fig. 9, A–D) and decreasing (Fig. 9, E–H) rate-depth functions. For ML increasing units the population mean firing rate in the behaving condition shows a decrease over time in response to 60–100% AM for MUs (Fig. 9A), while the rate decrease in the passive condition is lesser than in the behaving condition. Thus this difference can contribute to the faster decline in ROCas (steeper slope in curves of Fig. 8) for the behaving condition compared with the passive condition (Fig. 8A). For SUs, the change in population mean firing rate in response to AM at higher modulation depth is similar between behaving and passive conditions (Fig. 9B for SUs), and it seems unlikely that the firing rate change to these AM stimuli is the sole cause of the faster decline (steeper slope) in ROCas in the behaving condition. In response to unmodulated noise (0%), the population average firing rate drops after the 0–400 ms time window, which includes the onset response, then increases over time in both behaving and passive conditions (Fig. 9, A and B). However, the slope of the increase is steeper in the behaving than passive condition for both MUs and SUs. This differential rate of increase for firing rate over time for unmodulated noise in behaving and passive conditions (Fig. 9, A and B, 0%) likely contributes to the steep decline in population average ROCa in the behaving condition for ML cells with increasing functions at higher (60–100%) modulation depths (Fig. 8, A and B).

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Fig. 9.

Firing rate relative to spontaneous rate is calculated in 400-ms time windows starting at various times (0, 100, 200, 300, and 400 ms) after stimulus onset. Layout is the same as Fig. 8 except that firing rate rather than ROCa is shown. The firing rate in response to unmodulated noise (0%) is included here, and changes in this contribute to changes in the neuron's ability to detect AM.

For ML cells with decreasing functions, the population average firing rate for unmodulated sound (0%) also increases steeply over time in the behaving condition, while the rate increase is less obvious in the passive condition [Fig. 9, E (MUs) and F (SUs)]. This steep increase in response to unmodulated noise over time (Fig. 9) likely contributes to the steep decline of population average ROCas in the behaving condition for decreasing ML units (Fig. 8, E and F).

In A1, we also observe a similar firing rate increase over time in response to unmodulated noise in the behaving condition for both increasing and decreasing units (Fig. 9, C, D, G, and H). Thus this property—increased responses to unmodulated noise late in the behaving condition—seems general because it is common to both A1 and ML. Note that we also observe a similar increase in firing rate at lower modulation depths (6–28%), suggesting that the increased activity over time may be related to difficulty in detecting AM in these stimuli (also refer to Figs. 1–3).

Which Area Discriminates AM Better Based on Firing Rate: ML or A1?

In addition to the difference in time dependence of AM discriminability, A1 and ML show a significant difference in the overall level of AM discriminability. For increasing units, rate-based ROCa in A1 is significantly greater than that in ML for both MUs and SUs, during both the first and second halves of the test stimulus and in both behaving and passive conditions. In Fig. 10, this can be seen as the increasing functions for all time periods having values significantly greater than 0 at all suprathreshold depths for all four conditions. In contrast, ML only shows an advantage in discrimination for decreasing units during the second half in the behaving condition, while no significant difference between A1 and ML was found for decreasing units during the first half in the behaving condition. In the passive condition, ROCa for 28% AM for decreasing MUs and 60% AM for decreasing SUs was significantly better in A1 than in ML during the first half of the test stimulus, while a significant difference was not found during the second half. Note that in Fig. 10 the scale is twice as large for MUs, reflecting that ROCa differences between A1 and ML were roughly twice as large for MUs as for SUs. The aggregate results show that engagement in the AM task worsens the AM discriminability of decreasing neurons in A1 but improves that of ML decreasing neurons, especially during the later portion of the stimulus, making AM discriminability in ML better than A1 during the later time period. This also implicates the importance of a decreasing rate code in ML but not in A1.

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Fig. 10.

Plot showing the advantage of A1 neurons' ability to discriminate AM compared with ML. For increasing units this is calculated by subtracting ML mean ROCa from A1 ROCa (A1 − ML). Therefore higher values mean that A1 neurons can better discriminate AM than ML neurons. For decreasing units this is calculated by subtracting A1 mean ROCa from ML ROCa (ML − A1). This is required because for decreasing units lower ROCas correspond to better discrimination ability. This was done for both MUs (A) and SUs (B) in the 8 conditions noted in the key. The conditions were created by splitting 3 categories (increasing vs. decreasing, behaving vs. passive, and 1st vs. 2nd half) into the 8 combinations. Filled symbols represent points with significant differences between A1 and ML by a 2-sample t-test (P < 0.05); open symbols represent values not significantly different between A1 and ML.

VS_pp-Based AM Discriminability in ML Improves Because of Animals' Engagement in AM Discrimination Task

We examined whether AM discrimination based on phase-locking improves when animals engage in the AM discrimination task. VS_pp was used as a measure of phase-locking (see materials and methods for details). Figure 11 shows an example SU in ML with improved VS_pp-based AM discrimination due to task engagement. The SU robustly phase-locks to 20-Hz AM at higher modulation depths (Fig. 11, A and B). VS_pp was calculated for each trial in the time window with onset response excluded (time window = 80–800 ms). Trial-averaged VS_pp increased in the behaving condition compared with the passive condition at all depths except 28% and 0% (Fig. 11C). VS_pp-based ROCa increased in the behaving compared with the passive condition at all modulation depths (Fig. 11D), indicating that the AM discrimination based on phase-locking improved for this SU when the animal engaged in the AM task.

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Fig. 11.

Improved neural discrimination in the behaving compared with the passive condition using vector strength (VS). A–D: example of ML SU that improved phase-locking in the behaving condition. A and B: raster plots of SU response to 15-Hz AM in the passive (A) and behaving (B) conditions. C: phase-projected vector strength (VS_pp) plotted as a function of modulation depth for the passive and behaving conditions. D: ROCa based on VS_pp plotted as a function of modulation depth. Note in D that the behaving point (white circle) is covering the ability to see the passive point (black square) at 0% depth (both behaving and passive are 0.5 for ROCa by definition). E and F: ML population mean ROCas based on VS_pp during the entire stimulus duration (80–800 ms, the onset response is excluded) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (E) and SUs (F). G and H: population mean ROCas based on VS_pp during 1st half of the stimulus (80–400 ms, the onset response is excluded) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (G) and SUs (H). I and J: population mean ROCas based on VS_pp during 2nd half of the stimulus (400–800 ms) plotted as a function of modulation depths for the passive and behaving conditions for all recorded MUs (I) and SUs (J). In all panels, P values are denoted showing Wilcoxon signed-rank test comparing collapsed-depth ROC areas between behaving and passive conditions. *Individual modulation depths where a Wilcoxon signed-rank test yielded a value of P < 0.05.

In ML, the population average VS_pp-based ROCa significantly improves in the behaving condition compared with the passive condition at 100%, 80%, and collapsed depths for MUs (Fig. 11E) and at 60% and collapsed depths for SUs (Fig. 11F). These results indicate that AM discriminability based on phase-locking significantly improves because of engagement in the AM task. Unlike rate-based ROCa, we did not find a decline in VS_pp-based ROCa in the behaving condition during the stimulus (Fig. 11, G–J). However, the improvement in VS_pp-based AM discrimination due to engagement appears to be greater earlier during the stimulus.

Similar to rate-based AM discrimination, A1 and ML show a significant difference in the overall level of AM discrimination based on phase-locking. When VS_pp-based ROCas from the behaving condition are compared, A1 had significantly greater values than ML at 100%, 80%, 60%, 40%, 28%, and 16% for MUs and at 100%, 80%, 60%, and 6% for SUs. These results show that neurons in A1 are significantly better at detecting the presence of AM with phase-locking than those in ML, regardless of the behavioral condition.

Comparing Neural Activity During Passive Listening and Active Behavior in A1

Our results show that engagement in the AM task improves A1 and ML neurons' ability to discriminate AM based on both firing rate and phase-locking. Compared with a passive condition, auditory task engagement can 1) change stimulus-evoked and spontaneous firing rates of AC cells (Benson and Hienz 1978; Hocherman et al. 1976; Miller et al. 1972; Otazu et al. 2009; Pfingst et al. 1977), 2) create facilitative frequency tuning of A1 neurons in tone detection (Fritz et al. 2003; Miller et al. 1972), 3) sharpen spatial tuning of A1 neurons in sound localization (Benson et al. 1981; Lee and Middlebrooks 2011), 4) sharpen frequency tuning (David et al. 2012), and 5) increase neural selectivity for complex sounds (Knudsen and Gentner 2013). Furthermore, Otazu et al. (2009) have shown that the sound-evoked and spontaneous firing rate of A1 neurons is modulated by engagement in either an auditory or an olfactory task, demonstrating that A1 neurons' firing rate can be modulated by attending to nonauditory attributes. It is largely unknown what components in task engagement cause the myriad of observed changes in neural response properties. There are multiple possible contributing sources to the changes observed in our study, including general attention and feature-selective attention.

In A1 attention might activate a multiplicative response increase. Jaramillo and Zador (2011) have shown that temporal expectation improves A1 neurons' frequency discrimination ability by increasing responses at the target frequency and decreasing responses at surrounding frequencies. They conclude that this is consistent with a multiplicative effect. The A1 data we present here and previously (Niwa et al. 2012a) are also consistent with a multiplicative effect, since the engagement effects appear larger for stimuli that evoke higher firing rates. Such multiplicative effects are often thought to be indicative of attention. Our ML data show something different, suggesting feature-selective attention. We believe this because some of the ML effects are most pronounced near threshold and occur for time windows at which stimulus-specific attention is more likely engaged.

Recently, larger neural discrimination improvements have been found for a feature that is selectively attended in contrast to simply attending to sound. Spatial tuning of A1 neurons improved when animals performed a sound periodicity detection task, suggesting a general attention effect rather than a spatial feature-attention effect. However, engagement in a sound localization task improved spatial tuning more than performing the periodicity detection task (Lee and Middlebrooks 2011). This suggests that attention's effects were feature specific.

These results suggest that both general and feature-selective attention influence A1 responses, and are consistent with our A1 results.

Relationship to Plasticity in AC

Our results show rapid changes in neural discrimination when an animal switches between behaving and passive conditions. In addition to immediate attention effects, long-term training could lead to plastic changes that are often highly interrelated with plasticity (Fritz et al. 2007; Polley et al. 2006) that can improve AC neuron discrimination ability (e.g., Jeanne et al. 2011; Thompson et al. 2013). In our animals, plastic changes due to training might be dormant in the passive condition and only activated when the animal engages in the task. This would allow for the cortical circuit in the passive condition to be in a state open to the analysis of many possible signals, but during task performance the circuit is optimized for AM detection.

Comparisons Between A1 and ML

Comparison to Recent Study Looking at Attention and Nonprimary AC

Recently Dong et al. (2013) compared neural responses in multiple cortical areas while cats discriminated click trains to responses in a passive condition. While some of their results are similar to ours, one apparent discrepancy is that they report increases and decreases in driven responses in lower AC (dorsal tonotopic AC) but only increases in higher ventral nontonotopic AC. Major differences in methods, species, and analysis prevent direct comparison. Their dorsal “lower” cortical area probably includes both A1 and ML in monkeys, and their ventral area is probably much higher than any belt area (perhaps similar to temporal pole or insular cortex in monkeys), so the hierarchical areas between the two studies are not comparable. Their task is relatively easy from a sensory perspective (discriminating 15 Hz from 50 Hz, which is highly suprathreshold) and is unlikely to place high demands on feature-selective attention, whereas our animals are performing around threshold level. For us, particularly in A1, decreases refer to the slope of firing rate vs. modulation depth functions. For them, decreases refer to decreases in activity to both stimuli, not on changes in the neurometric (response vs. frequency) function. Much of the complexity we see occurs between 400 and 800 ms after stimulus onset, while their stimuli are only 320 ms. Finally, comparisons are made difficult by numerous other differences in experimental design: performance level, neuronal inclusion criteria (they only include neurons that fire at least 2 standard deviations above spontaneous level to one of the two stimuli), and species. This highlights that experimental differences can have large impacts on physiological results, particularly attention-related effects that depend critically on the difficulty of discriminating or detecting stimuli.

Is There a Special Role for Nonsynchronized Responses?

AM encoding is hypothesized to transform from temporal to rate-based representations as information is processed from lower to higher auditory areas (Lu and Wang 2004; Nelson and Carney 2004). This is supported by the decrease in the upper limit of modulation frequency to which neurons phase-lock and by the increase in prominence of rate-based AM coding when ascending from lower to higher areas (Blackburn and Sachs 1989; Creutzfeldt et al. 1980; de Ribaupierre et al. 1980; Frisina et al. 1990; Langner and Schreiner 1988; Lu and Wang 2004; Nelson and Carney 2007; Preuss and Muller-Preuss 1990; Rees and Moller 1983; Rouiller et al. 1981) and the emergence of nonsynchronized firing rate encoding of temporal modulation in higher areas (Bendor and Wang 2007; Lu and Wang 2004).

Several lines of evidence support a special contribution for nonsynchronized responses in temporal discrimination. In A1, nonsynchronized (increasing) responses are more sensitive to AM than synchronized responses (Fig. 12) and the nonsynchronized responses increase sensitivity with engagement. Nonsynchronized increasing ML responses, although slightly less sensitive than those in A1, yield similar early-stimulus results. During the second half of the stimulus, ML nonsynchronized increasing responses show depth-dependent improvement in the behaving condition (Fig. 12, B and D), consistent with a more pronounced or longer-lasting attention effect near threshold.

ML nonsynchronized decreasing responses also show large improvements during behavior and are more AM sensitive than synchronizing responses, particularly later in the stimulus (Fig. 12, A–D). Finally, during the entire stimulus, both increasing and decreasing nonsynchronized ML responses improve during behavior (Fig. 12, A–D), whereas for synchronized responses improvements only occur in the first half for increasing and only during the second half for decreasing functions (Fig. 12, E–H). Together these findings suggest that increasing (A1 and ML) and decreasing (ML) nonsynchronized responses have properties that make them particularly well-suited for AM processing. This is further supported by recent studies in behaving cats showing that nonsynchronized responses' discrimination ability more closely match the animal's behavioral performance than synchronized responses (Dong et al. 2011) and nonsynchronized rate responses are modulated by attention but synchronizing ones are not (Dong et al. 2013).

Comparison of ML Activity in Our Task to Somatosensory Cortical Activity During Flutter Frequency Discrimination Tasks

Our study shares similarities with results from the primary and secondary somatosensory cortices (S1 and S2). Romo et al. conducted extensive studies examining the neural codes underlying perception, memory, and decision making during vibrotactile discrimination, where monkeys were trained to report whether the frequency of a first flutter stimulus is greater than the flutter frequency of a second stimulus (Hernandez et al. 2000, 2002, 2010; Luna et al. 2005; Romo et al. 1998, 2002, 2003; Salinas et al. 2000). First, S2 neurons' flutter frequency coding capacity was significantly lower than that of S1 neurons (Salinas et al. 2000); similarly, we find that ML neurons generally have lower AM discriminability than A1 cells. Second, transformation of encoding schemes appears to occur from a single mode of “increasing” rate response in S1 to a dual mode of “increasing” and “decreasing” responses in S2 (Hernandez et al. 2002; Salinas et al. 2000). Salinas et al. (2000) showed that 92% of S1 neurons encoded an increase in flutter frequency with monotonically increasing firing rate and only 8% used decreasing rate. In S2, 58% had increasing rate vs. flutter frequency functions and 42% decreasing. Our results also suggest that the primary sensory area, A1, employs a single-mode encoding with mainly increasing rate-depth functions while the secondary area, ML, employs a dual-polar mode with both increasing and decreasing rate-depth functions. Although our studies and those of the Romo group use different behavioral tasks in different sensory modalities, there is a general principle: a meaningful temporal feature (flutter frequency or modulation depth) in primary sensory cortex is encoded primarily by one type of response—an increase in activity. But in the secondary area two types of responses—activity increases or decreases—encode the feature. The similarities in our results suggest a common scheme in hierarchical processing of temporal information shared in sensory cortices of different modalities.

We acknowledge Drs. G. Recanzone, K. Britten, J. Ditterich, and C. Schreiner for useful comments on the manuscript. We also acknowledge Zachary Cline-Egri for his help in animal training and recording.

Present addresses: M. Niwa, Dept. of Otolaryngology, Stanford University School of Medicine, Stanford, CA 94305; E. Engall, School of Veterinary Medicine, University of California, Davis, CA 95616.