The brain faces the difficult task of maintaining a stable representation of key features of the outside world in noisy sensory surroundings. How does the sensory representation change with noise, and how does the brain make sense of it? We investigated the effect of background white noise (WN) on tuning properties of neurons in mouse A1 and its impact on discrimination performance in a go/no-go task. We find that WN suppresses the activity of A1 neurons, which surprisingly increases the discriminability of tones spectrally close to each other. To confirm the involvement of A1, we optogenetically excited parvalbumin-positive (PV + ) neurons in A1, which have similar effects as WN on both tuning properties and frequency discrimination. A population model suggests that the suppression of A1 tuning curves increases frequency selectivity and thereby improves discrimination. Our findings demonstrate that the cortical representation of pure tones adapts during noise to improve sensory acuity.

In the present study, we investigated the effect of WN on cortical tuning curves in awake mice and the behavioral relevance of the resulting tuning curve perturbations. During WN, we found a general suppression of tone-evoked activity in A1, in line with previous studies in anesthetized animals (). Associated with this suppression, we surprisingly found that WN improves discrimination performance for two tones with small frequency differences. We then asked whether the shift in cortical neuronal tuning properties is the underlying mechanism for this improved discriminability. Using optogenetics, we manipulated the neuronal response properties directly in A1 to mimic the tuning changes observed during our WN experiments and found that discriminability was improved for the same frequency range. Measurements in the auditory thalamus only weakly reflected the cortical tuning changes following the two perturbations. Finally, we used a simple model to illustrate how general suppression in tuned activity—as observed in A1 but not in thalamus—can lead to behavioral improvement in discrimination. Together, our results demonstrate that the tuning properties of A1 neurons are used by the brain to perform auditory discrimination and judgments relevant for stimulus-driven behavior.

A previous study confirms that the primary auditory cortex is directly involved in driving auditory perceptions and judgments during sensorimotor integration and behavior in a frequency-discrimination task (). Another study indicates that changes in cortical neural population responses can alter behavioral performance (). Also, the decoding of cortical activity confirms that the stimulus information needed for the categorization of sounds is present in A1 cortical neurons (). Despite this knowledge, we still have a poor understanding of how tuning curves are used by the brain to construct representations of auditory stimuli and how perturbations of these representations modify sensory-driven perceptions and related behaviors.

Embedding relevant sound features in background noise may also change tuning features. Previous studies in anesthetized animals have shown that background white noise (WN) introduces a threshold effect that lowers the response of cortical neurons to pure tone stimuli (). At the perceptual level, previous human psychophysics studies mostly reported a decrease in auditory saliency with noise (), but some showed a positive effect of noise on signal discrimination () or speech perception (). In animal models, where a direct correlation between neuronal activity and behavior would be possible, psychophysical experiments seeking to measure the limit of perception are more challenging. A recent study shows that sound location discrimination of a pure tone decreases gradually with signal to noise ratio (). It is unclear, however, how the threshold effect on tuning curves reported at the neuronal cortical level extends to animals in the awake state and how this suppression of activity influences the behavior of the animal.

Cortical neurons are typically responsive to only a subset of sensory features, implying a distributed sensory representation. A clear example of this is the auditory cortex, where specific neurons are selective to distinct sound frequencies. When presented with an auditory stimulus, the cortex receives spike trains resulting from activity in the cochlea. The cochlea deconstructs the external sound environment into frequency components (), which are passed further along the auditory pathway up to the primary auditory cortex in a segregated manner (). This spatial separation of frequency components, conserved from the cochlea to the primary auditory cortex, is referred to as tonotopy (). At the cellular level, tonotopy translates into spatially organized neurons with frequency-selective receptive fields. In many cases, these receptive fields are well characterized by a bell-shaped response to a varying stimulus, also referred to as a tuning curve. Auditory tuning curves are not, however, static; they have been shown to adapt during changes in stimulus context () or attentional state (), and it is believed that this flexibility is relevant for adjusting the dynamic range of sensory representation ().

Sensory processing is the basis of our interaction with the world and an essential part of brain function. At the cortical level, we know that neurons are informative about sensory inputs, as activity from cortical neurons can be decoded to reveal the stimulus inputs (). However, despite the successful decoding of sensory mapping, it is still unclear what sensory-related activity the brain uses for generating perceptions and goal-directed behavior.

Taken together, these results for our model illustrate a possible link between suppression in A1 activity and improvement in behavioral performance.

Next, we investigated if the small changes observed in the MGB during WN or PVperturbations would lead to a better discrimination in our model. Instead of using A1 activity as input to the readout layer, we used fits of the experimentally recorded MGB activity ( Figure S7 ). We observed that the small changes during WN did give a better discrimination, though smaller than for A1 activity. However, the PVperturbation did not give an increased discriminatory performance ( Figure 7 G), which confirms our conclusion that A1 activity seems to have a stronger weight than MGB activity in determining the discriminatory behavior.

A key aspect of the model is the thresholding occurring in the layer of readout neurons, which causes a more frequency-selective activation in the perturbed conditions. To test the generality of this threshold assumption and the stability of our model results, we varied the threshold, expressed as spike count, over a wide range of values ( Figures 7 D and 7F). We computed the difference in decoding errors and the difference in d′ for WN and light perturbations separately. This analysis showed that the improvements in decoding were not specific to the threshold values we used above, but occurred in a range of thresholds corresponding to approximately 50%–100% of the control baseline firing or to a spike count range between 0.30 and 0.60 spikes per trial. The experimentally recorded mean spontaneous spike count was 0.33 ± 0.46 SD across cortical neurons. If, however, no threshold was applied (corresponding to decoding the A1 activity directly), only a small improvement could be seen for the WN perturbation and no effect for light activation (as shown in Figures 7 D and 7F with threshold, expressed in spike counts, set to zero).

To compare the performance of the decoder with the behavioral performance measured experimentally, we calculated the d′ of the decoder by setting p(hit) = 1 − (error rate) and p(FA) = error rate and computed the difference in d′ due to WN and light perturbations using experimentally derived tuning curves for control, WN, and light-induced PVperturbations ( Figures 7 F and 7G; see STAR Methods for details). This revealed a clear improvement in d′ for both WN and light perturbations for frequency intervals between go and no-go tones smaller than 0.5 octaves. We noted, however, that the decoder performed almost perfectly for larger frequency intervals, leaving little room for improvement at these intervals.

We simulated a model consisting of 800 readout neurons with preferred frequencies spanning eight octaves and computed the fraction of errors (error rate), as the frequency interval between go and no-go tones was varied ( Figures 7 C and 7E). The error rates generally increased as the frequency interval decreased, as expected, due to an increased similarity between the responses for go and no-go tones. While using the experimentally derived mean tuning curves for the control condition as a starting point, we then tried inducing a negative offset to the A1 activity (and hence in the input to the readout layer). An overall suppression in the A1 tuning curves led to a reduction in error rates, and this effect was most pronounced for small frequency intervals ( Figure 7 C). For values of negative offsets comparable to perturbed conditions (WN, PVmanipulation), we saw a clear improvement for frequency intervals smaller than 0.5 octaves ( Figure 7 C; colors indicate offset values for control, WN, and light-induced perturbations of PVneurons).

The model assumes that frequency-tuned activity in A1 feeds into a layer of readout neurons in which a threshold is applied. Only activity that reaches the threshold evokes activity in the readout layer ( Figure 7 B). We assumed the activity in the readout neurons to follow Poisson variability, and we set up a decoder to classify between the go and no-go tones based on the single-trial activity in the readout layer (see STAR Methods ). As a starting point, we used a threshold corresponding to 90% of the baseline spike rate in the control condition.

Our results so far indicate a direct relationship between perturbations of cortical activity and behavioral outcome. But how can a suppression of cortical activity lead to an improvement in sensory discriminability? One possible explanation is that downstream circuits decode the sound-evoked activity in the primary auditory cortex in comparison to spontaneous activity levels. If only activity that exceeds a specific threshold is used for discrimination, a suppression of the tuning curves would mean that a narrower range of frequencies evokes activity that exceeds the threshold ( Figure 7 B). To explore this possible mechanism, we implemented a simplified model of a decoding circuit ( Figure 7 A) and used our experimentally measured mean tuning curves as input to the model.

(F) Difference in d′ compared to control for WN (left) and PV + activation (right) as a function of the threshold level in the readout layer (y scale). Solid black lines indicate control baseline, and dashed white lines indicate default threshold.

(D) Difference in error rate compared to control for WN (left) and PV + activation (right) as a function of the threshold level in the readout layer (y scale). Solid black lines indicate control baseline, and dashed white line indicates default threshold.

(C) Error rate for control tuning curve shifted to different baseline level (y scale). Solid lines indicate baseline levels for control (black), WN (green), and PV + activation (red). Dashed white line indicates the default threshold set at 90% of control baseline.

(B) Bottom: Expected response in A1 for a go tone (solid lines) and a no-go tone (dashed lines) for the three different conditions indicated by color. The activity level that corresponds to the threshold in the readout layer is indicated by the dotted line. Top: Corresponding activity in the readout layer.

We next developed a theoretical framework to investigate if thalamic or cortical changes in activity could explain the behavioral effects of WN and cortical light-induced PV + manipulation.

These recordings reveal that WN and cortical PV + activation also modify tuning features in the thalamus. Since the thalamus is a relay station on the feedforward pathway to the cortex, it is not surprising that WN effects are observed here. In the case of cortical PV + perturbations, corticothalamic projections probably echoed the cortical changes in thalamic circuits. PV + perturbations, however, did not change the frequency selectivity of MGB neurons. In general, the effects of WN and light-driven activation of PV + neurons were less pronounced in the thalamus as compared to the cortex.

Next, we tested if cortical light-driven activation of PVneurons would similarly affect tone responses in the thalamus. We recorded activity in the MGB while performing cortical light-driven activation of PVneurons using similar light intensity levels described for the cortical recordings. We found a slightly significant decrease of peak and baseline spike rates ( Figure 6 J; peak= 21.7 ± 2.5 Hz; peak= 19.3 ± 2.2 Hz; N= 7; n= 75; p = 0.0283; df = 74; LMM) ( Figure 5 K; baseline= 9.7 ± 1.4 Hz; baseline= 8.3 ± 1.1 Hz; N= 7; n= 74; p = 0.0125; df = 74; LMM). The mean of peak aligned tuning curves exhibited a decrease in responses ( Figures 6 L and 6M; regression slope = 0.88; intersection = −0.19 Hz; r= 0.897), corresponding to 12.7% suppression at peak and 6.1% suppression at baseline. We also quantified the percentage of frequencies represented as a function of a threshold normalized to peak spike rates. PVactivation did not change the selectivity at a threshold of 0.5 of peak ( Figure 6 N;= 43.7% ± 3.3%;= 41.6% ± 3.1%; N= 7; n= 75; p = 0.2043; df = 74; LMM), as confirmed by the measurement of sigma of the Gaussian fits ( Figure S3 D; n= 10; p = 0.105; df = 9; LMM). As for the cortical data, we verified that the preferred frequencies were not significantly shifted in either of the two perturbations ( Figure S2 B).

We asked how changes in cortical tuning properties imposed by WN or cortical PVactivation would reflect in the auditory thalamus. We used a linear multi-electrode array to reach the medial geniculate body (MGB) and recorded activity in response to pure frequency tones as described earlier, with or without background WN ( Figures 6 A and 6B ) or while performing light-induced activation of PVneurons in the auditory cortex ( Figures 6 H and 6I). In the MGB, background WN induced a slightly significant reduction in peak and baseline spike rates of responses to pure frequency tones ( Figure 6 C; peak= 23.8 ± 2.6 Hz; peak= 21.5 ± 2.5 Hz; N= 11; n= 122; p = 0.034; df = 121; LMM) ( Figure 5 D; baseline= 9.8 ± 1.4 Hz; baseline= 8.5 ± 1.6 Hz; N= 11; n= 122; p = 0.0094; df = 121; LMM). Plotting the mean of the peak aligned tuning curves showed a decrease in responses ( Figures 6 E and 6F; regression slope = 0.915; intersection = −1.05 Hz; r= 0.936), corresponding to 13.0% suppression at peak and 7.9% suppression at baseline. We then estimated the frequency selectivity by computing the percentage of frequencies represented at a threshold of 0.5 of the peak spike rates. This showed a significant reduction in WN compared to no-WN trials ( Figure 6 G;= 42.9% ± 3.3%;= 35.3% ± 3.5%; N= 11; n= 122; p = 0.0013; df = 121; LMM). However, this increase in frequency selectivity could not be confirmed by a significant decrease of sigma in the small subset of cells whose tuning curves could be approximated by a Gaussian distribution ( Figure S3 C; n= 7; p = 0.578; df = 6; LMM). In summary, the MGB activity was suppressed by WN both at peak and baseline. However, the overall effect of WN was less pronounced than in the cortex.

(N) Percentage of frequencies represented as a function of a normalized spike rate threshold. The dotted line is the subtraction of light-off (blakc) and light on (red) lines. Boxplots with percentage of frequencies represented at a threshold of 0.5 of peak spike rate for light-off and light-on tuning curves (p = 0.2043; LMM). Inset shows p values comparing light off and light on for frequencies represented at all thresholds of peak-normalized tuning curves using Wilcoxon test. The vertical dotted line shows p = 0.05. See also Figures S2 and S3

(J and K) Scatterplot of peak (J) and baseline (K) spike rate in light off (black) or light on (red) conditions (n su = 75; N mice = 7). Boxplots with peak spike rate (J, ∗ p = 0.0283; LMM) or baseline spike rates (K, ∗ p = 0.0125; LMM).

(I) Expample of a SU's spike rates in response to pure tones in light-off (black) or light-on (red) conditions. All lines and symbols as indicated in Figure 1

(G) Percentage of frequencies represented as a function of a normalized spike rate threshold. The dotted line is the subtraction of no-WN (black) and WN (green) lines. Boxplots with percentage of frequency represented at a threshold of 0.5 of peak spike rate for no-WN and WN tuning curves ( ∗∗ p = 0.0013; df = 121; LMM). Inset shows p values comparing no WN and WN for frequencies represented at all thresholds of peak-normalized tuning curves using Wilcoxon test. The vertical dotted line shows p = 0.05.

(C and D) Scatterplot of peak (C) and baseline (D) spike rate in no-WN (black) versus WN (green) conditions (n su = 122; N mice = 11). Boxplots with peak spike rates (C, ∗ p = 0.034, LMM) or baseline spike rates (D, ∗∗ p = 0.0094, LMM). The dotted lines represent the unity lines.

(B) Example of a SU’s spike rates in response to pure tones in no-WN (black) or WN (green) background. All lines and symbols are as indicated in Figure 1

Our results prove that activating PVneurons suppresses tuned responses of A1 neurons similar to WN and enhances the discrimination performance for tones close in frequency in a similar range to WN. Together, the WN and optogenetic perturbations suggest that mice use the response features of A1 neurons to form perceptions and discriminate between pure tones. Although PVneurons were targeted at the cortical level, thalamic neurons could be influenced by these cortical perturbations via cortico-thalamic projections (), and behavioral performance could hence be explained by thalamic changes in activity. To address this possibility, we recorded in the auditory thalamus and tested the effect of the two perturbations.

Using optogenetics, we tested the effect of light-driven activation of PVneurons in the same go/no-go discrimination task described previously ( Figure 5 A). In these experiments, a craniotomy was performed to insert electrodes and set the light intensity to induce a suppression of roughly 50% in pure tone-evoked responses, similar to the passive recordings described above ( Figures S6 A and S6B). We found no difference in discrimination performance for pure tone intervals of 0.8, 0.65, and 0.5 octaves when comparing light-off versus light-on trials, while a significant improvement in performance was noted for the smaller intervals of 0.35 and 0.2 octaves ( Figures 5 B-5D; d′-d′= 0.42 ± 0.14; n= 8; p = 0.021; d′-d′= 0.50 ± 0.12; n= 6; p = 0.008, respectively; paired t test). As for the WN perturbations ( Figure S4 F), this improvement in discrimination performance was due to a decrease in FA rates ( Figure S6 E). We found no change in reaction time, suggesting that light-driven activation of PVneurons does not impose changes in motor-related areas driving the behavior ( Figures S6 C and S6D). The light effect was not caused by retinotopic activation, as light did not alter the discrimination performance in control mice ( Figure 5 D).

(D) The d′ changes between light-off and light-on trials as a function of frequency interval between go and no-go tones for PV-ChR2 ( ∗∗ p = 0.008, ∗ p = 0.021, paired t test) and control mice (Δ: wild-type control, n = 3, 4, 4, 4, 4 mice; □, PV-ChR2 before craniotomy control, n = 3, 3, 3, 3, 3 mice). Data show mean ± SEM.

(C) Discriminability index d′ without and with light for all frequency differences between go and no-go tones (n = 13, 9, 8, 8, 6 mice; color coded as in B).

(B) Hit rate versus FA rate in light-off (filled circles) and light-on (empty squares) conditions for each frequency difference between go and no-go trials in PV-ChR2 mice. Each pair of filled circle and empty square represents a single mouse tested at different frequency intervals.

Given the large similarities in the representations of pure tones in A1 between background WN and light-induced changes, we asked whether PV + neuron activation improves discrimination performance in the same interval range as the background WN does.

To look at a possible mechanistic relationship between the WN suppressive effect and PVneuron activity, we performed experiments combining a continuous WN background with light-induced activation of PVneurons with the same conditions described above. We found that combining both perturbations resulted in a response suppression that was largely similar to the sum of the suppressive effects of WN and PVneuron activation separately ( Figure 4 ). These results indicate that the activation of cortical PVneurons might not be the driving mechanism behind the suppressive effect induced by WN.

(D) Boxplots comparing controls (no WN or light off), WN, or light on and WN + light on together (peak spike rate of WN versus WN + light on, ∗∗∗ p < 0.0001; light on versus WN + light on, ∗∗ p = 0.0051; baseline spike rate WN versus WN + light on, ∗∗∗ p < 0.0003; light on versus WN + light on, ∗∗ p = 0.0035; % frequency tested at 0.5 threshold WN versus WN + light on, ∗∗∗ p < 0.0001; light on versus WN + light on, ∗∗∗ p < 0.0001; LMM). Circles indicate means. Red lines mark the medians showing a near linear effect of combining continuous WN and PV + neuron perturbation.

(C) Means of cell-by-cell percentage of frequencies represented as a function of a normalized spike rate threshold. The dotted line is the subtraction of control (black) and WN + light-on (pink) lines. Inset shows p values comparing control and WN + light on for frequencies represented at all thresholds of peak-normalized tuning curves using Wilcoxon test. The vertical dotted line shows p = 0.05.

(B) Mean tuning curves from (A) plotted against each other for all stimulus frequencies. The solid line indicates the regression lines. The dotted line represents the additive effects of isolated WN and PV + neuron perturbation effects by subtracting them from the control curve.

(A) Mean of peak-aligned tuning curves (n su = 159; N mice = 10). Shaded areas indicate 5%–95% confidence intervals. The dotted line represents the additive effects of isolated WN and PV + neuron perturbation effects by subtracting them from the control curve.

As previously described, we used extracellular electrophysiological recordings to measure tuned responses to different pure frequency tones. The preferred frequencies, peaks, and baseline spike rates were measured as described previously. As expected from the light titration approach, we observed that light activation of PVinterneurons significantly decreased the peak spike rates ( Figures 3 A and 3B ; peak= 29.3 ± 2.6 Hz; peak= 19.3 ± 1.9 Hz; N= 10; n= 159; p < 0.0005; df = 158; LMM), as well as baseline spike rates ( Figures 3 A and 3C; baseline= 12.8 ± 1.5 Hz; baseline= 7.8 ± 1.2 Hz; N= 10; n= 159; p < 0.0005; df = 158; LMM), independently of the preferred frequencies ( Figure S1 B). This was not related to a change in preferred frequency between both conditions ( Figure S2 A). As for the WN experiments, we plotted the mean of the tuning curves aligned to the peak ( Figure 3 D). PVperturbation produced a combined additive and multiplicative shift (regression slope = 0.701; intersection = −1.68 Hz; r= 0.988) corresponding to a 35.6% suppression at peak and 18.8% suppression at baseline ( Figure 3 E). We also found an increase in frequency selectivity, as estimated by a reduction in the percentage of frequencies represented as a function of a threshold normalized to peak responses in each condition. Comparing the percentage of frequencies represented at a threshold of 0.5 of peak showed a significant reduction in light on trials ( Figure 3 F; percentage of freq. represented:= 41.0% ± 3.3%;= 34.0% ± 3.5%; N= 10; n= 159; p = 0.0011; df = 158; LMM). As for the WN perturbation, this increase in frequency selectivity could be confirmed by a significant decrease of sigma in the subset of cells whose tuning curves could be approximated by a Gaussian distribution ( Figure S3 B; n= 27; p = 0.0014; df = 26; LMM). Light-driven activation of PVneurons did not affect noise correlation ( Figure 3 G; n= 452; p = 0.429; df = 451; LMM). The mean fano factor was also unchanged ( Figures 3 H and 3I; fano, = 1.31 ± 0.06; fano, = 1.33 ± 0.01; p = 0.382; df = 157; LMM), while mean variance decreased due to the overall decrease in spike rates ( Figure 3 J; variance, = 1.31 ± 0.11; variance, = 0.95 ± 0.08; p = 0.0265; df = 158; LMM).

(I and J) Mean fano factor (I) and trial-to-trial variance (J) across all frequencies (fano factor: p = 0.382, LMM; variance: ∗ p = 0.0265, LMM). All boxplots show medians and 25th–75th and 10th–90th percentiles; circles indicate means.

(F) Means of cell-by-cell percentage of frequencies represented as a function of a normalized spike rate threshold. The dotted line is the subtraction of light-off (black) and light-on (red) lines. Boxplots with percentage of frequency represented at a threshold set at 0.5 fraction of peak spike rate for light-off and light-on tuning curves ( ∗∗ p = 0.0011; df = 158; LMM). Inset shows p values comparing light off and on for frequencies represented at all thresholds of peak-normalized tuning curves using Wilcoxon test. The vertical dotted line shows p = 0.05.

(B and C) Scatterplot of peak (B) and baseline (C) spike rates in light-off versus light-on conditions for all SUs (n su = 159; N mice = 10). Boxplots with mean spike rates ( ∗∗∗ p < 0.0005, p < 0.0005 for peak and baseline respectively; LMM).

(A) Example of frequency-dependent spike rate responses in light-off (black) and light-on (red) conditions in a PV-ChR2 mouse, as recorded with extracellular electrophysiology. All lines and symbols are as indicated in Figure 1

It has been reported that specific activation of PVcells can alter the tuning properties of sensory neurons. For example, PV photoactivation in the auditory cortex has been shown to decrease the magnitude of tone-evoked responses () and to produce a mixture of divisive and subtractive suppression on both spontaneous and tone-evoked neuronal activities (). In order to specifically manipulate auditory cortical neuron responses, we expressed the light-sensitive cation channel, channelrhodopsin-2 (ChR2), in PVinterneurons using a breeding cre-lox approach (see STAR Methods ). ChR2 was successfully expressed in the majority of PVneurons in the auditory cortex, and all ChR2-positive neurons were positive for PV Figure S5 A). We tested the light effect on auditory cortical spiking with cell-attached and intracellular recordings ( Figure S5 C). We found that the suppression in spiking activity of excitatory neurons caused by light-driven activation of PVinterneurons varied from mouse to mouse and was independent of cortical depth up to 800 μm below the pia surface ( Figure S5 E). We titrated the light power in each mouse so that the sound-evoked peak response was inhibited to roughly 50% of its control amplitude ( Figure S5 D). This ensured a comparable level of inhibition across mice. The light pulse started at the tone onset and was maintained for the full duration of the tone (50 ms) to target the immediate tone representation in cortical circuits.

These experiments lead to the counterintuitive observation that noise can improve auditory discrimination. In humans, it is clear that auditory acuity, like frequency discrimination () or speech recognition (), can be impaired by noise. However, some studies have also shown that noise allows for smaller-frequency difference detection (), increases auditory signals (), or promotes acuity for certain signal-to-noise ratios (). To test whether the behavioral changes we observed are related to the changes in cortical tuning curves, and to address the possibility that WN may affect discrimination performance by mechanisms residing outside of A1 or by mechanisms that we did not consider to analyze, we next used optogenetics to selectively control the tuning curves of A1 neurons.

Including background WN significantly improved discrimination performance for frequency intervals of 0.35 (d′-d′= 0.24 ± 0.07; n= 8; p = 0.001; paired t test) and 0.2 octave (d′-d′= 0.39 ± 0.05; n= 5; p = 0.002; paired t test) but had no effect for larger frequency intervals ( Figures 2 C and 2D). Given that the reaction time from the tone onset to the lick was not affected by background WN ( Figures S4 H and S4I), this manipulation did not seem to affect the effectuation of motor-related programs.

How does such a mean suppression of spiking activity in auditory cortical neurons affect tone discrimination performance in mice? We tested the effect of background WN as mice performed a go/no-go discrimination task. The mice were trained to lick a drop of liquid in response to a pure frequency tone (go tone) and to restrain from licking when hearing another tone frequency (no-go tone). An incorrect lick was punished with a mild air puff and time out ( Figure 2 A). The mice learned the task in 3–6 days ( Figures S4 A and S4C). Once the task was learned, the difficulty of the task was increased by bringing the no-go tone closer in frequency to the go tone, until the animal was no longer able to discriminate between the tones ( Figures S4 A, S4B, and S4D). In this testing phase, the go tone was kept constant at 7 kHz, and the no-go tone was progressively shifted from 12 to 8 kHz in steps of 1 kHz (corresponding to frequency intervals of 0.8, 0.65, 0.5, 0.35, and 0.2 octaves between the go and no-go tones). Blocks of 100 trials with WN or no-WN backgrounds were tested in random order for each no-go tone ( Figure S4 B; see STAR Methods ). The increasing difficulty of the task was reflected in a gradual decrease in the discriminability index d′ ( Figure S4 D). Switching the no-go tone to 0.8 octaves below the go tone (4 kHz) produced the same d′ as the no-go tone at 0.8 octaves above the go tone ( Figure S4 E). This implies that the discrimination difficulty is related to the distance in octave and not to the absolute frequencies. The decay in d′ is a result of a decreasing hit rate as well as increasing false alarm (FA) rate ( Figures 2 B and S4 F), indicating that the increasing difficulty affects both go and no-go trials. A gradual decline in motivation due to increasing satiety during a session was not the cause of this decay, since d′ was back to its initial value at the end of the session ( Figure S4 G).

(D) Same data as in (C), but displayed as d′ changes with WN background (n = 5, 8, 9, 6, 9 mice; ∗∗ p = 0.002, p = 0.001; paired t test). Data show mean ± SEM.

(C) Discriminability index d′ for WN versus no-WN conditions. The color code represents the frequency interval between the go and no-go tones, as in (B).

(B) Hit rate versus FA rate for no-WN (filled circles) and WN background (empty squares) for each go/no-go frequency interval. Each pair of filled circle and empty square represents a single mouse tested at different frequency intervals.

In summary, our findings show that background WN significantly suppresses responses to pure tone stimulation, both as peak and baseline spike rates, resulting in a negative shift of tuned responses of A1 neurons. Noise correlation and fano factor remain unaffected.

As it is known that noise correlations may influence the ability of a population to code for sensory stimuli (), we next analyzed whether background WN influences inter-trial noise correlation. Noise correlation was only computed for trials with minimum spike rates above 1 Hz. Comparing no-WN to WN trials showed no significant difference ( Figure 1 I; n= 534; p = 0.187; df = 534; LMM). We also compared trial-by-trial variance, either as mean variance or variance normalized to mean spike rates (i.e., fano factor). The mean fano factor compared across all frequencies tested was unchanged ( Figures 1 J and 1K; fano= 1.29 ± 0.03; fano= 1.35 ± 0.04; p = 0.151; df = 157; LMM). The average variance decreased with WN, as expected, since variance scales with the number of spikes ( Figure 1 L; variance= 1.31 ± 0.17; variance= 0.90 ± 0.17; p = 0.0029; df = 158; LMM).

We then quantified frequency selectivity by measuring the percentage of stimulus frequencies tested giving a response with a spike rate at or above a threshold. The threshold was set at a fraction of each SU peak spike rate in each condition (no WN and WN). This percentage of frequencies represented at a threshold of 0.5 of the peak rate was significantly decreased ( Figure 1 H; percentage of freq. represented:= 41.0% ± 2.1%;= 34.4% ± 1.8%; N= 10; n= 159; p = 0.0004; df = 158; LMM), corresponding to an increased frequency selectivity with WN. The increased frequency selectivity was confirmed by a significant decrease in the sigma of the Gaussian fit on the subset of cells whose tuning curves could be approximated by a Gaussian distribution (see STAR Methods Figure S3 A; n= 27; p = 0.019; df = 26; LMM). Though more traditional, the method to estimate tuning width by measuring the sigma of the Gaussian fit does assume that tuning curves can be well fitted by a Gaussian function. This is the case for some SUs, but definitely not for all of them (27 out of 159 neurons in this case). The significant increase in frequency selectivity is, however, also observed in SUs that did not present a classical Gaussian tuning curve, as indicated by the less-conventional analysis method of frequency selectivity used here ( Figure 1 H).

Response characteristics of SUs were analyzed from the smoothed curve (see STAR Methods ), obtained by plotting the mean spike rates (10 trials per stimulus) during sound stimulation as a function of the stimulus frequency ( Figures 1 A–1C). Each SU’s preferred frequency was determined as the stimulus frequency evoking the peak spike rate of the smoothed curve. The peak spike rate was measured from the mean of the raw trial at the preferred frequency. The baseline spike rate was calculated as the median response spike rate for stimulus frequencies that elicited a spike rate lower than the mean spike rate of all stimulus frequencies. When comparing trials with no WN and those with WN, we found that peak spike rates were decreased (peak= 29.3 ± 2.7 Hz; peak= 23.0 ± 1.9 Hz; N= 10, n= 159; p = 0.0007; degrees of freedom [df] = 158; linear mixed model [LMM]) ( Figure 1 D). Baseline spike rates were also decreased (baseline= 12.5 ± 1.5 Hz; baseline= 8.4 ± 1.0 Hz; N= 10; n= 159; p = 0.0005; df = 158; LMM) ( Figure 1 E). These changes were observed across the whole tonotopic range ( Figure S1 A) and were not related to changes in preferred frequency ( Figure S2 A). We represented the overall change in tuning curves by aligning all responses to peak and calculating the grand mean ( Figure 1 F). WN produced a combined additive and multiplicative shift ( Figure 1 G; regression slope = 0.791; intersection = −1.53 Hz; r= 0.97) corresponding to 26.1% suppression at peak and 14.2% suppression at baseline.

(K and L) Mean fano factor (K) and trial-to-trial variance (L) across all frequencies (fano factor: p = 0.151, LMM; variance: ∗∗ p = 0.0029, LMM). All boxplots show medians and 25th–75th and 10th–90th percentiles; circles indicate means.

(H) Means of cell-by-cell percentage of frequencies represented as a function of a normalized spike rate threshold. The dotted line is the subtraction of no-WN (black) and WN (green) lines. Boxplots with percentage of frequency represented at 0.5 of spike rate peak for no-WN and WN tuning curves ( ∗∗∗ p = 0.0004; LMM). Inset shows p values comparing no WN and WN for frequencies represented at all thresholds of peak-normalized tuning curves using Wilcoxon test. The vertical dotted line shows p = 0.05.

(G) Mean tuning curves from (F) plotted against each other for all stimulus frequencies. The solid line indicates the regression line (slope = 0.791; intersection = −1.53 Hz; r 2 = 0.97).

(D and E) Scatterplot of peak spike rates (D) and baseline spike rates (E) in no-WN versus WN conditions for all SUs (n su = 159; N mice = 10). The dotted lines represent the unity lines, and the solid lines (gray) are the regression lines. Boxplots with mean spike rates ( ∗∗∗ p = 0.0007, ∗∗∗ p = 0.0005 for peak and baseline, respectively; LMM).

(C) Same SU as in (B), with smoothed tuning curves plotted overlaying vertical lines of trial means ± SEM for each frequency tested. The dotted line indicates mean spontaneous activity during no WN.

(A) Schematic of experimental setup. An extracellular electrode is inserted into A1 of awake mice while pure tones are played in a silent or with-WN background.

To investigate the stability of the representation of key features of an auditory stimulation in the presence of noise, we started by characterizing neural responses to pure frequency tones, perturbed it with a WN background, and determined whether this modification had any consequences at the behavioral level. Awake head-fixed mice were exposed to 37 randomly played pure frequency tones, spanning 3.7 octaves from 4 kHz to 48.7 kHz at 60 dB sound pressure level (SPL), lasting 50 ms each. Pure tones were presented in either a silent or continuous WN background at 50 dB SPL (referred to as no-WN or WN conditions, respectively). We recorded neuronal activity in the primary auditory cortex with multi-electrode arrays composed of four shafts with eight recording sites each. Voltage traces across the 32 channels were processed to extract single-unit (SU) activity (see STAR Methods ).

Discussion

Our results demonstrate that tone discrimination is influenced by the tuning properties of primary auditory cortical neurons. We show that background WN suppresses responses to pure tones in cortical neurons. In an auditory go/no-go task, we find that background WN improves discrimination for pure frequency tones that are close together but does not affect performance for tones farther apart. Since WN selectively modifies tuning curves without significantly changing the fano factor or noise correlation, our data suggest that the brain might use neuronal tuning features to solve the discrimination task. Optogenetic manipulation of neurons in A1 selectively led to a similar suppression of A1 responses and behavioral effects as WN, even though the optical and WN manipulation were suggested to rely on different mechanisms ( Figure 4 ). This supports the view that suppression of frequency tuning curves in the cortex led to the observed improvement in frequency discrimination. To gain insight into how activity in A1 neurons could relate to the discrimination of auditory stimuli and why background WN aids tone discrimination, we constructed a model of a simple decoding circuit and studied the discrimination performance in this model. The results of the model show that for neuronal parameters matching our in vivo recordings in A1, and not in the MGB, the uncertainty of the sensory representation of go and no-go signals is reduced with the suppression of neuronal tuning curves only when the go and no-go signals are close to each other, in line with our behavioral observations. Together, our results suggest that the general tuning properties of A1 neurons directly shape discriminative performance and guide sensory-driven behavior.

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Zador A.M. Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination. Panzeri et al., 2017 Panzeri S.

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Fellin T. Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior. Classical neural activity-to-behavior analysis () relies on a correlation between neurometric and psychometric functions to link neural activity and certain behaviors. However, to establish causality, the best available option is to perturb neurons while the animal engages in task-related behaviors (). Ideally, perturbations should be subtle enough not to annihilate the neural activity or the behavior. By preserving neuronal activity during perturbations, we can look for activity features relevant toward the behavior. These features can be further implemented in models that may predict behavioral outcome. In our study, we used two different methods of perturbation to confirm causality—one natural, experienced on a daily basis by the auditory system (background WN), and one artificial (optogenetics)—to point to a particular feature of cortical activity. The results of these manipulations, producing similar cortical changes and similar improved behavioral outcomes, allow us to conclude that the response profile of A1 neurons is not only correlated, but also directly involved in shaping the formation of tone percepts.

Liang et al., 2014 Liang F.

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Schnupp J.W. Constructing noise-invariant representations of sound in the auditory pathway. In our experiments, continuous WN did not show any increase in mean excitatory activity, as compared to silence ( Figure 1 B). This is counterintuitive—in our ears, continuous WN can be perceived—but not new () or surprising. Indeed, the mean A1 excitatory response to constant sounds, even as short as 100 ms, is mainly observed at the tone onset and sometimes at the tone offset, but not during the tone (). We cannot, however, rule out whether individual A1 neurons, or neurons outside of A1, respond strongly to continuous WN. Adaptation to continuous WN has been shown to increase as one ascends the auditory pathway ().

+ neurons might not be the main driver behind this suppression (+ neurons activated at different timing than the ones imposed by optogenetics, might emphasize the suppression already observed in the thalamus. It has indeed been shown that two tones presented simultaneously sharpen tuning curves via lateral inhibition throughout the auditory pathway ( Kato et al., 2017 Kato H.K.

Asinof S.K.

Isaacson J.S. Network-Level Control of Frequency Tuning in Auditory Cortex. Shamma and Symmes, 1985 Shamma S.A.

Symmes D. Patterns of inhibition in auditory cortical cells in awake squirrel monkeys. The mechanisms leading to the suppression of cortical tuning curves by background WN are also still unresolved. Our experiments indicate that background WN modifies auditory responses subcortically already ( Figures 6 A–6G) and that the activation of cortical PVneurons might not be the main driver behind this suppression ( Figure 4 ). We cannot rule out that other interneurons in the cortex, or PVneurons activated at different timing than the ones imposed by optogenetics, might emphasize the suppression already observed in the thalamus. It has indeed been shown that two tones presented simultaneously sharpen tuning curves via lateral inhibition throughout the auditory pathway (). Whether a similar mechanism might be involved when a tone is played simultaneously with WN is not known. Further work would be needed to disentangle the mechanisms behind the effects of background WN on tone responses.

+ neuron activation suppresses tuned responses and improves sensory acuity. A study in the auditory cortex finds that PV+ neuron activation causes improved perceptual discrimination between a phasic and a continuous pure frequency tone in a prepulse inhibition behavioral paradigm ( Aizenberg et al., 2015 Aizenberg M.

Mwilambwe-Tshilobo L.

Briguglio J.J.

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Geffen M.N. Bidirectional Regulation of Innate and Learned Behaviors That Rely on Frequency Discrimination by Cortical Inhibitory Neurons. + manipulation improves or impairs behavioral performance due to modifications in frequency tuning ( Briguglio et al., 2018 Briguglio J.J.

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Geffen M.N. Cortical Neural Activity Predicts Sensory Acuity Under Optogenetic Manipulation. We used the optogenetic approach targeted at inhibitory neurons to ask whether the cortical effects of the background WN were enough to explain the discrimination performance, and we hence affected neurons in A1 directly. Previous work has already shown that PVneuron activation suppresses tuned responses and improves sensory acuity. A study in the auditory cortex finds that PVneuron activation causes improved perceptual discrimination between a phasic and a continuous pure frequency tone in a prepulse inhibition behavioral paradigm (). With the same paradigm, it has also been shown that PVmanipulation improves or impairs behavioral performance due to modifications in frequency tuning (). Here, it is argued that an increased signal-to-noise ratio in A1 responses is the underlying mechanism for the improved performance. In our study, we rather see a decrease in the signal-to-noise ratio, measured as the peak-to-baseline ratio. Since the behavioral paradigms are different in both studies—the prepulse inhibition test relying on a startle response and the go/no-go test based on a decision of the mouse to react to a sound—it is difficult to compare both studies.

+ perturbation might lead to changes in other features of the cortical activity than those analyzed here. Interneurons, for example, have been shown to play an important role in increasing temporal precision and reducing the randomness of the cortical operation ( Wehr and Zador, 2003 Wehr M.

Zador A.M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Moore and Wehr, 2013 Moore A.K.

Wehr M. Parvalbumin-expressing inhibitory interneurons in auditory cortex are well-tuned for frequency. Kuchibhotla et al., 2017 Kuchibhotla K.V.

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Froemke R.C. Parallel processing by cortical inhibition enables context-dependent behavior. + interneurons could also decrease intracortical activity and promote activity locked to a strong thalamic drive ( Krause et al., 2019 Krause B.M.

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Banks M.I. PV+ Cells Enhance Temporal Population Codes but not Stimulus-Related Timing in Auditory Cortex. + activation has been shown to enhance functional connectivity in columnar cortical circuits ( Hamilton et al., 2013 Hamilton L.S.

Sohl-Dickstein J.

Huth A.G.

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Deisseroth K.

Bao S. Optogenetic activation of an inhibitory network enhances feedforward functional connectivity in auditory cortex. PVperturbation might lead to changes in other features of the cortical activity than those analyzed here. Interneurons, for example, have been shown to play an important role in increasing temporal precision and reducing the randomness of the cortical operation () or in enabling context-dependent behavior (). Activating PVinterneurons could also decrease intracortical activity and promote activity locked to a strong thalamic drive (). In addition, PVactivation has been shown to enhance functional connectivity in columnar cortical circuits (). Our data cannot tell whether such mechanisms are at play with the perturbations explored here.

+ activation show that discrimination performance improves only for small frequency intervals, while large intervals remain unaffected. This suggests that the perturbation effects are specific in manipulating particular constraints determined by the encoding of pure tones across a population of A1 neurons and not because of changes in a global parameter such as attention or motivation. However, our analysis cannot rule out whether specific changes in individual or subgroups of cells could be responsible for the increase in discriminability. Both perturbations induce heterogeneous changes to individual cells ( The results from both WN and light-driven PVactivation show that discrimination performance improves only for small frequency intervals, while large intervals remain unaffected. This suggests that the perturbation effects are specific in manipulating particular constraints determined by the encoding of pure tones across a population of A1 neurons and not because of changes in a global parameter such as attention or motivation. However, our analysis cannot rule out whether specific changes in individual or subgroups of cells could be responsible for the increase in discriminability. Both perturbations induce heterogeneous changes to individual cells ( Figures 1 D, 1E, 3 B, and 3C). The fact that the mean activity of the neuronal population correlates with behavioral performance suggests that the brain integrates activity across a larger population.

One could speculate whether the fact that the learning phase of the behavioral paradigm was done without background WN and without light stimulation could have any perceptual learning consequences that could influence the results of the testing phase. However, if this were the case, we would expect a bigger effect at the no-go frequency used during the learning phase (i.e., 0.8 oct) and not at frequencies the mice had not been exposed to during the learning phase (>0.8 oct), or at least the same effect on all frequencies tested. This was not reflected in our results.

Finally, although the two perturbations allow us to identify cortical tuning curves to shape tone discrimination performance, A1 would not be expected to be the sole contributor to the integrated auditory information relevant for solving the behavioral task.