The hearing cortex influences sound processing in the midbrain listening center

Our first goal was to characterize the effects of activating the direct cortico-collicular projections on tone-evoked responses in IC. A combination of cortical anterograde and collicular retrograde viral transfections was used in order to achieve specific viral transfection of cortico-collicular neurons. We delivered either an excitatory opsin, ChannelRhodopsin2 (ChR2) or an inhibitory opsin, ArchaerhodopsinT (ArchT), bilaterally, specifically to the neurons in the auditory cortex which project to the inferior colliculus (Figure 1A). To achieve such specificity, we injected a retrograde virus that encoded Cre recombinase (Retro2 AAV.Cre) in IC (Figure 1B). This retrograde viral construct ensured that neurons projecting to IC expressed Cre recombinase a few weeks later. At the same time, we injected a virus that encoded ChR2 or ArchT in reversed fashion under the FLEX cassette in AC (AAV.Flex.ChR2, AAV.Flex.ArchT) (Figure 1C). This strategy ensured that only neurons expressing Cre recombinase in the auditory cortex would express ChR2 or ArchT in AC. Therefore, opsin was expressed exclusively in AC-IC projecting neurons, which terminate across all regions of IC, including central nucleus (CNIC) and dorsal cortex of IC (DCIC), from which we recorded (Figure 1D). Shining light over AC of these mice would therefore directly activate or suppress only these cortico-collicular feedback projections to IC.

First, we measured and quantified neuronal spiking in IC in response to stimulation or suppression of cortico-collicular projections using ChR2 or ArchT. To activate this projection, we shone blue laser over AC, recorded neuronal activity in IC, and quantified the effects of manipulating feedback in the absence of sound (Figure 2A,C,D). We measured the spiking activity in IC as we varied the duration of laser manipulation (1 ms, 5 ms, 25 ms, 250 ms). As exected, activation of cortico-collicular neurons resulted, on average, in an increase in firing rate of IC neurons. This effect persisted at the 5 ms, 25 ms, and 250 ms laser duration (Figure 2C; 1 ms: p=5.3e-4, ON = 5.6 ± 0.6 Hz, OFF = 4.9 ± 0.5 Hz; 5 ms: p=0.0027, ON = 6.9 ± 0.7 Hz, OFF = 4.8 ± 0.5 Hz; 250 ms: p=0.005, ON = 5.6 ± 0.6 Hz, OFF = 4.8 ± 0.5 Hz). Whereas the direction of the effect was consistent with our prior expectations, the magnitude of the effect was unexpectedly small. This suggests that AC targets a small subpopulation of IC neurons and that the effect of activation does not spread far within IC.

We tested the effect of suppressing AC-IC neurons on firing responses in IC and, surprisingly, we detected little difference in firing in IC neurons (Figure 2D; 25 ms: p=0.039, ON = 9.5 ± 0.7 Hz, OFF = 9.5 ± 0.7 Hz; 250 ms: p=0.02, ON = 9.5 ± 0.8 Hz, OFF = 9.5 ± 0.8 Hz). This result suggests that AC-IC inputs at rest do not contribute to IC responses, but rather modulate collicular activity only when activated.

Next, our goal was to characterize modulation of sound-evoked responses in IC by cortical feedback. We first tested the effects of feedback modulation on acoustic click responses. We chose clicks as the initial stimulus because they drive fast responses in both AC and IC. Laser stimulation began 250 ms prior to click train onset to allow for the response to the laser to come to a steady state, and continued activation throughout the clicks (Figure 2B). Activating the feedback using ChR2 had a weak suppressive effect on the firing rate of IC neurons in response to clicks. Consistent with previous experiments, we observed an overall increase in spontaneous activity (Figure 2E, bottom; p=0.0031, spont ON = 5 ± 0.87 Hz, spont OFF = 4.3 ± 0.92 Hz). Activating feedback caused a small decrease in click-evoked response (Figure 2E, left; p=0.001, click ON = 10.4 ± 1.2 Hz, click OFF = 10.7 ± 1.1 Hz).

By contrast, suppressing cortico-collicular feedback using ArchT had no effect on IC click responses (Figure 2F). Because attenuating the input does not affect the activity in IC during clicks, this result further suggests that the strength of the baseline signal from AC to IC, even in the presence of cortical activity, is not sufficient to modulate IC responses.

We also tested whether the effects of cortico-collicular modulation differed across tone frequency range. We presented a stimulus that consisted of tones of 50 frequencies ranging from 3 to 70 kHz. To modulate the feedback from AC, we tested three different laser onsets (−100 ms, −20 ms, +8 ms) relative to tone onset to isolate the effect of timing on affecting IC responses. These delays were chosen for the following reasons: −100 ms delay would allow for the laser effect on cortical activity to come to a steady state, allowing to quantify the effect throughout the tone pip; +8 ms is set up to mimic the time scale of cortical response to a tone, effectively amplifying the onset of the cortical response; −20 ms delay is an intermediate value.

We found that activating cortico-collicular feedback using ChR2 increased spontaneous activity of IC neurons (Figure 3A, left; −100 ms: p=0.022, ON = 4.3 ± 0.47 Hz; OFF = 3.6 ± 0.38; −20 ms: p=8.6e-6, ON = 5.6 ± 0.63, OFF = 3.7 ± 0.47). However, overall the feedback decreased tone-evoked response magnitude in IC, which we defined as the difference between spontaneous and tone-evoked response, at all laser onsets (Figure 3A, right; −100 ms: p=3.2e-5, ON = 14.2 ± 0.98 Hz; OFF = 15.5 ± 1.04 Hz; −20 ms: p=1.4e-6, ON = 11.8 ± 1.08 Hz, OFF = 13.9 ± 1 Hz; +8 ms: p=0.0034, ON = 13.03 ± 1.03, OFF = 13.7 ± 1.03). This suggests that the broad activation of feedback upregulates the baseline activity of IC neurons, but decreases tone-evoked response (Figure 6C, −100 ms: p=0.018, ON = 19.01 ± 1.2 Hz, OFF = 18.5 ± 1.2 Hz; 8 ms: p=0.007, ON = 17.3 ± 1.2 Hz; OFF = 16.9 ± 1.2 Hz). By contrast, suppressing the feedback using ArchT had no effect on either spontaneous activity or tone-evoked response magnitude (Figure 3B), suggesting that at baseline AC does not provide strong modulation of IC activity, as removing it does not affect sound-evoked effects in IC.

We then examined the effect of feedback on frequency tuning of IC units. Activation of feedback using ChR2 decreased frequency selectivity in the subsets of IC units that exhibited a decrease in tone-evoked response magnitude or increase in spontaneous activity (Figure 4A), but not in units that exhibited an increase in tone-evoked response magnitude or decrease in spontaneous activity (Figure 4B, mag decrease: −20 ms, p=0.00031, sparse ON = 0.49 ± 0.027, sparse OFF = 0.55 ± 0.025; +8 ms, p=0.00029, sparse ON = 0.46 ± 0.027, sparse OFF = 0.55 ± 0.024; spont increase: −100 ms, p=0.011, sparse ON = 0.49 ± 0.032, sparse OFF = 0.56 ± 0.031; −20 ms, p=0.00018, sparse ON = 0.46 ± 0.034, sparse OFF = 0.56 ± 0.029; +8 ms, p=2.2e-6, sparse ON = 0.41 ± 0.032, sparse OFF = 0.55 ± 0.029).

Interestingly, these changes were observed almost exclusively in cells located in DCIC rather than CNIC (Figure 5). Specifically, activation of feedback increased spontaneous activity (Figure 5A, left; −100 ms: p=0.0204, ON = 3.5 ± 0.5 Hz, OFF = 2.5 ± 0.3 Hz; −20 ms: p=2.8e-7, ON = 5.2 ± 0.7 Hz, OFF = 2.3 ± 0.3 Hz) and decreased tone-evoked response magnitude (Figure 5A, right, −100 ms: p=3.3e-6, ON = 14.8 ± 1.4 Hz, OFF = 16.5 ± 1.5 Hz; −20 ms: p=6.3e-7, ON = 11.8 ± 1.6 Hz, OFF = 14.7 ± 1.4 Hz) in cells located in DCIC, but not in CNIC (Figure 5B). Similarly, activation of feedback decreased frequency selectivity (Figure 5C; mag decrease: −20 ms, p=6.3e-4, sparse ON = 0.46 ± 0.03, sparse OFF = 0.53 ± 0.03; +8 ms, p=4.7e-5, sparse ON = 0.41 ± 0.03, sparse OFF = 0.53 ± 0.03; spont increase: −100 ms, p=0.0044, sparse ON = 0.43 ± 0.04, sparse OFF = 0.52 ± 0.04; −20 ms, p=0.00013, sparse ON = 0.39 ± 0.04, sparse OFF = 0.53 ± 0.04; +8 ms, p=5.2e-6, sparse ON = 0.33 ± 0.03, OFF = 0.53 ± 0.04) in cells located in DCIC, but not in CNIC (Figure 5D). This is consistent with the enhanced density of AC projections in DCIC as compared to CNIC (Figure 1D). To verify that these results were not due to a select few high firing rates of units in DCIC, we repeated the analysis comparing only units with spontaneous activity in the laser OFF condition less than 40 Hz. With these matched firing rates we still observed the same statistically significant effects (DCIC – mag decrease −100 ms: p=3.3e-6, ON = 14.8 Hz, OFF = 16.6 Hz; −20 ms: p=6.3e-7, ON = 12.1 Hz, OFF = 12.6 Hz; spont increase −100 ms: p=0.02 ON=3.5 Hz, OFF = 2.5 Hz; −20 ms: p=2.8e-7, ON = 5.1 Hz, OFF = 4.9 Hz; CNIC – mag decrease +8 ms: p=0.0039, ON = 11.7 Hz, OFF = 12.6 Hz).

To elucidate changes in the shape of the frequency response functions in IC with activation of cortico-cortical feedback beyond the general changes in selectivity we observed with changes in sparseness, we fitted linear regressions to light off versus light on firing rates. This analysis can reveal whether changes in frequency selectivity are linear/subtractive, multiplicative/divisive, or some combination of these transformations. For example, a slope of 1 and a positive y-intercept would represent a linear increase in frequency response (equal increase in response across all frequencies). Over the population of IC neurons with decreased tone-evoked response magnitude and/or increased spontaneous activity, the median slopes and y-intercepts of ranked linear fits to frequency responses are less than 1 and above zero, respectively (Figure 6A). These results, in combination with the decreased tone-evoked activity observed (Figure 6A; Figure 6B; Figure 6C, −100 ms: p=0.018, ON = 19.01 ± 1.2 Hz, OFF = 18.5 ± 1.2 Hz; 8 ms: p=0.007, ON = 17.3 ± 1.2 Hz; OFF = 16.9 ± 1.2 Hz), indicate that the decrease in frequency selectivity was due to a decrease in response to tones at preferred frequencies, not non-preferred frequencies. This result suggests that the suppressive effect of the cortico-collicular feedback is preferential for higher firing responses. Suppressing feedback resulted in very small (<0.04%) change in sparseness, therefore it did not affect frequency selectivity in IC (Figure 4B).

Correlations in trial-to-trial responsiveness of neurons can affect the information encoded by a neuronal population (Abbott and Dayan, 1999; Averbeck et al., 2006; Nirenberg and Latham, 2003; Shadlen and Newsome, 1998; Zohary et al., 1994). To test whether activation of cortico-collicular feedback affected correlations in trial-to-trial variability of response strength between units recorded simultaneously we measured noise correlations. We found that activating cortico-collicular feedback using ChR2 had no effect on noise correlations during spontaneous activity or tone response (Figure 3—figure supplement 1).

To better understand the effect of modulation of cortical activity on spectro-temporal receptive field properties of IC neurons, we presented a continuous signal comprised of dynamic random chords (DRCs) sampled from a uniform distribution of loudness values per frequency bin. The unbiased nature of this stimulus allowed us to estimate the spectro-temporal receptive field of neurons (STRF), which quantifies the dynamics of sound waveform in time and frequency that lead to a neuronal response. During the DRC stimulus, we turned on the laser every other second for 250 ms to activate cortico-collicular projections with ChR2. We found that a subset of IC units reduced their mean DRC-evoked firing rates (N = 56), whereas another subset of IC units increased their mean DRC-evoked firing rate when we activated cortico-collicular feedback (N = 47). We next separately computed the receptive fields for each neuron when laser was off and when laser was on and compared the STRFs for activation with ChR2 (Figure 7A). To quantify those changes, we identified the positive (activation) and negative (suppressive) regions in the STRFs and compared them for laser ON and laser OFF conditions. In the subset of neurons whose firing rate increased with laser STRFs changed: only 42% of positive lobes, and 63% of negative lobes persisted with the laser (Figure 7B, left). Of those lobes that persisted, for positive lobes, there was on average a decrease in temporal width (p=0.00098, ON = 0.0303 ± 0.0018 s, OFF = 0.037 ± 0.0023 s), frequency selectivity (p=0.00042, ON = 6.7 ± 1.1 Hz, OFF = 9.6 ± 1.8 Hz) and STRF size (p=0.00036, ON = 46.05 ± 6.9 pixels, OFF = 77.5 ± 12.04 pixels), whereas for negative lobes, we did not detect any changes (Figure 7C, left). For IC units whose firing rate was decreased, there was a much smaller change in the lobes with cortico-collicular activation, with 78% and 79% of positive and negative lobes persisting, respectively (Figure 7B, right). For both positive lobes and negative lobes, the only difference was an increase in the temporal width when laser activated cortico-collicular feedback (Figure 7C, right; Positive lobes: p=0.026, ON = 0.034 ± 0.0025 s, OFF = 0.032 ± 0.0025 s; Negative lobes: p=0.02, ON = 0.037 ± 0.0028 s, OFF = 0.032 ± 0.0024 s). The decrease in STRF size in units with increased DRC-evoked response and decrease in sparseness across the population is consistent with the interpretation that the effect of the cortico-collicular feedback leads to a decrease in responsiveness to tones that evoke the greatest responses (in the center of the receptive field) and an increase to stimuli that evoke weaker activity. In other words, neuronal firing increases overall, but selective responses to specific frequency bands decrease. Because the majority of the units were recorded as multi-units, it is possible that this change in selectivity is due to the change in firing rate of differentially tuned single units.

Auditory responses in AC are shaped by interactions between excitatory and inhibitory neurons (Wood et al., 2017). To determine how modulating frequency selectivity in AC might affect tone-evoked responses in IC in a frequency-selective fashion, we perturbed the excitatory-inhibitory interactions by modulating two different classes of inhibitory interneuron known to contribute to sound responses in AC: PV and SST inhibitory interneurons (Figure 8A). We found that modulating PV interneuron activity in AC had no significant effects on spontaneous and tone-evoked activity or frequency selectivity in IC (Figure 8B–E) despite modulating frequency selectivity, spontaneous activity, and tone-evoked response magnitude in AC. Specifically, in AC, activating PVs decreased spontaneous activity and tone-evoked response magnitude (Figure 8F; spontaneous activity: p=2.2e-9, ON = 0.88 ± 0.16 Hz, OFF = 2.7 ± 0.34 Hz; tone-evoked response magnitude: p=8.02e-7, ON = 6.9 ± 1.06 Hz, OFF = 11.5 ± 1.3 Hz) and increased frequency selectivity (Figure 8G; p=3.3e-12, ON = 0.59 ± 0.022, OFF = 0.42 ± 0.02), while suppressing PVs increased spontaneous activity (Figure 8I; p=5.2e-5, ON = 4.08 ± 0.54 Hz, OFF = 3.06 ± 0.55 Hz) and decreased frequency selectivity (Figure 8J; p=3.8e-5, ON = 0.34 ± 0.027, OFF = 0.41 ± 0.031). This suggests that the changes in cortical activity driven by manipulation of PV activity does not propagate to the inferior colliculus.

Different interneuron classes may function in distinct networks, so we also tested the effects of modulating SST interneurons in AC. Modulating SST interneurons had no effect on tone-evoked activity (Figure 9B,D, right) or frequency selectivity (Figure 9C,E), but suppressing SST interneurons increased spontaneous activity in IC (Figure 9D, left; p=0.029, ON = 6.9 ± 0.66 Hz, OFF = 6.7 ± 0.63 Hz), a change that we also observed with activation of the direct feedback projections (Figure 2A, left). Similar to PV interneurons, activating SST interneurons decreased spontaneous activity and tone-evoked response magnitude in AC (Figure 9F; spontaneous activity: p=1.4e-12, ON = 0.97 ± 0.25 Hz, OFF = 2.9 ± 0.37 Hz; tone-evoked response magnitude: p=1.3e-15, ON = 3.3 ± 0.59 Hz, OFF = 11.4 ± 1.2 Hz) and increased frequency selectivity (Figure 9G; p=1.6e-13, ON = 0.69 ± 0.024, OFF = 0.47 ± 0.019). In AC, suppressing SSTs reduced spontaneous activity, but had no significant effect on tone-evoked response magnitude or frequency selectivity (Figure 9I,J; p=8.1e-4, ON = 3.6 ± 0.41 Hz, OFF = 2.2 ± 0.38 Hz). This lack of effect was not due to the relatively small effect of light penetrating to the deep layers. In fact, to confirm that modulating PV and SST activity in AC affected activity of units in L5/6 where the feedback projections we computed changes in spontaneous activity at each tetrode which spanned the entire auditory cortex. We found that activity was modulated across the layers (Figures 8–9 H,K). Whereas modulating PVs did not have an effect on IC activity, SST suppression resulted in an increase in spontaneous, but not tone-evoked activity in IC, which suggests that inhibitory modulation of sound responses in AC does not propagate to IC.

All experiments were performed in adult male and female mice (supplier: Jackson Laboratories; age, 12–15 wk; weight, 22–32 g; wild-type C57BL/6J RRID:IMSR_JAX:000664↗; Pvalb-Cre mice, strain: B6;129P2-Pvalbtm1(cre)Arbr/J RRID:IMSR_JAX:008069↗; Sst-Cre mice, strain: Ssttm2.1(cre)Zjh/J RRID:IMSR_JAX:013044↗; Cdh23 mice, strain: Cdh23tm2.1Kjn/J RRID:IMSR_JAX:018399↗, or Pvalb-Cre x Cdh23 or Sst-Cre x Cdh23 crosses). Mice were housed at 28°C on a 12 hr light–dark cycle with water and food provided ad libitum, less than five animals per cage. In Pvalb-Cre mice Cre recombinase (Cre) was expressed in parvalbumin-positive interneurons, and in Sst-Cre, Cre was expressed in somatostatin-positive interneurons. All animal work was conducted according to the guidelines of University of Pennsylvanian IACUC (protocol number 803266) and the AALAC Guide on Animal Research. Anesthesia by isoflurane and ketamine and euthanasia by CO2 were used. All means were taken to minimize the pain or discomfort of the animals during and following the experiments. All experiments were performed during the animals' dark cycle. Original spike data and code are available on Dryad (http://doi.org/10.5061/dryad.1t61c80↗).

Modified AAVs encoding ArchT (AAV9-CAG-FLEX-ArchT-GFP or AAV9-CAG-FLEX-ArchT-tdTomato; UNC Vector Core) or ChR2 (AAV9-CAG-FLEX-ChR2-tdTomato; Penn Vector Core) were used for selective suppression or excitation, respectively. Retrograde AAV virus encoding Cre (retro AAV-hSyn-Cre-GFP) was custom made in our laboratory. Briefly, RetroAAV2 hSyn Cre-GFP was packaged using the Helper-Free system (Agilent) and the retrograde trafficking plasmid, Retro2, which bears capsid mutations in serotype 2.

At least 21 days prior to electrophysiological recordings, mice were anesthetized with isoflurane to a surgical plane. The head was secured in a stereotactic holder. The mouse was subjected to a small craniotomy (2 × 2 mm) over AC under aseptic conditions. Viral particles were injected (750 nl) bilaterally using a syringe pump (Pump 11 Elite, Harvard Apparatus) targeted to AC (coordinates relative to bregma: −2.6 mm anterior,±4.3 mm lateral, +1 mm ventral). Fiber-optic cannulas (Thorlabs, Ø200 μm Core, 0.22 NA) were implanted bilaterally over the injection site at depth of 0.5 mm from the skull surface. For a subset of mice, to target direct feedback, the mouse was also subjected to a craniotomy over IC (1 × 4 mm). Retro AAV viral construct was injected (3 × 200 nl) via glass syringe (30–50 um diameter) using a syringe pump (Pump 11 Elite, Harvard Apparatus) bilaterally in IC. Craniotomies were covered with a removable silicon plug. A small headpost was secured to the skull with dental cement (C and B Metabond) and acrylic (Lang Dental). For postoperative analgesia, slow release Buprenex (0.1 mg/kg) and Bupivicane (2 mg/kg) were injected subcutaneously. An antibiotic (5 mg/kg Baytril) was injected subcutaneously daily (for 4 days) at the surgical site during recovery. Virus spread was confirmed in all mice postmortem by visualization of the fluorescent protein expression in fixed brain tissue, and its colocalization with PV or SST or, for feedback cohort, expression in AC layer 5/6, following immuno-histochemical processing with the appropriate antibody.

To confirm the location of viral injection sites, tissue sections containing the IC or AC were cut at 40 μm using a cryostat. Sections were mounted onto glass slides and imaged using Zeiss LSM 800 confocal microscope. 20X images were taken throughout the entire IC or AC, and tiles were stitched together to form a composite image using Zen software. Masks were drawn around the edge of the tissue to hide the embedding medium using Photoshop.

Stimuli were delivered via a magnetic speaker (Tucker-Davis Technologies) directed toward the mouse’s head. Speakers were calibrated prior to the experiments to ± 3 dB over frequencies between 3 and 70 kHz by placing a microphone (Brüel and Kjaer) in the location of the ear contralateral to the recorded AC hemisphere, recording speaker output and filtering stimuli to compensate for acoustic aberrations (Carruthers et al., 2013). Free-standing speaker was used to approximate the conditions under which the mouse typically experiences sounds in awake state.

To obtain a quick assessment of auditory onset responses we used click trains. Click trains were composed of six 50 ms clicks at 70 dB sound pressure level relative to 20 microPascals (SPL) with a 50 ms ISI. Click trains were repeated 120 times with a 450 ms ISI between trains. Alternating click trains were also paired with 1 s laser stimulation beginning 250 ms prior to click train onset.

To measure tuning for direct feedback cohorts, a train of 50 pure tones of frequencies spaced logarithmically between 3 and 70 kHz, at 70 dB SPL in pseudo-random order was presented 20 times. Each tone was 50 ms duration (5 ms cosine squared ramp up and down) with an inter-stimulus interval (ISI) of 450 ms. Alternating tones were paired with continuous 250 ms laser pulse at either −100 ms, −20 ms, or +8 ms onset relative to tone onset.

For Sst-Cre and Pvalb-Cre cohorts, a train of pure tones of 35 frequencies spaced logarithmically between 3 and 70 kHz and 8 uniformly spaced intensities from 0 to 70 dB SPL were presented 10 times in a pseudo-random order. Alternating tones were paired with continuous 250 ms laser pulse at −100 ms relative to tone onset.

To measure spectro-temporal receptive fields (STRFs) we constructed DRCs from 20 ms chords (with 1 ms ramp) of 50 frequencies spaced logarithmically between 5 and 40 kHz with average intensity of 50 dB SPL and 20 dB SPL standard deviation selected from a uniform distribution for each chord. Total duration was 40 min with a 250 ms continuous laser pulse presented every 1 s.

All recordings were carried out inside a double-walled acoustic isolation booth (Industrial Acoustics). Mice were placed in the recording chamber, and a headpost was secured to a custom base, immobilizing the head. Activity of neurons in AC were recorded via a custom silicon multi-channel probe (Neuronexus), lowered in the area targeting AC via a stereotactic instrument following a craniotomy at a 35-degree angle. The electrode tips were arranged in a vertical fashion that permits recording the activity of neurons across the depth of the auditory cortex and the inferior colliculus. Activity of neurons in IC were recorded via the same custom probes, lowered in the area targeting IC via a stereotactic instrument following either a craniotomy (Sst-Cre and Pvalb-Cre cohorts) or removal of the silicon plug, vertically. Recording sites in IC were identified based on anatomical markers and stereotaxic coordinates as dorsal cortex (<0.5 mm of midline) or central nucleus of IC (center of central nucleus of IC ~1 mm lateral of midline). Recordings were made in multiple locations across IC. Electro-physiological data from 32 channels were filtered between 600 and 6000 Hz (spike responses), digitized at 32 kHz and stored for offline analysis (Neuralynx). Spikes belonging to single neurons and multi-units were detected using commercial software (Plexon). Original spike data and code are available on Dryad (http://doi.org/10.5061/dryad.1t61c80↗). We examined the experimental groups described in Table 1 (note: we do not distinguish here between Sst-Cre/Pvalb Cre and Sst-Cre x Cdh23 and Pvalb-Cre x Cdh23). Initial experiments were performed under anesthesia to control for stability or recordings. For cohorts with awake and anesthetized recordings data were analyzed separately, but we observed no difference in our results so data were combined (Figure 8—figure supplement 1). We used power analysis for effect size of 25% and expected variance in firing rate (50%) to determine the minimum total number of units for each experimental group at N = 44. All groups included a greater number of units (single- and multi-units were combined in analysis). The number of units in measuring tone-evoked responses are specified in Table 1. Mice that did not show effect of laser activation or suppression in auditory cortex were excluded. We recorded from AC in Cdh23+ChR2 mice and Cdh23+ArchT mice to verify an effect in AC (Figure 3—figure supplement 2) in all mice used in our analysis.

Neurons were stimulated by application of continuous light pulse delivered from either blue (473 nm, BL473T3-150, used for ChR2 stimulation) or green DPSS laser (532 nm, GL532T3-300, Slocs lasers, used for ArchT stimulation) through implanted cannulas. Laser power measured through cannulas was 3 mW. Timing of the light pulse was controlled with microsecond precision via a custom control shutter system, synchronized to the acoustic stimulus delivery.

Noise correlations were calculated as the pairwise Pearson correlation coefficient between the spike counts of units recorded simultaneously. For spontaneous activity spikes were counted in a window 20 ms before tone onset and for tone response spikes were counted in a window 75 ms after tone onset.

Significant differences and P values were calculated using paired Wilcoxon sign-rank test (unless noted otherwise) with standard MATLAB routine. For the laser alone data, to compare distributions to standard normal distribution data were normalized by mean and standard deviation and then significant differences and P values were calculated by Kolmogorov-Smirnoff test with standard MATLAB routine. Mean ± standard error of the mean was reported unless stated otherwise. * indicates p<0.05, ** indicates p<0.01, *** indicates p<0.001.