The primary sensory areas of the cerebral cortex are characterized by a systematic spatial arrangement of responses to the stimulus features represented by peripheral receptors. In A1, this takes the form of a logarithmic gradient of neural response preferences for a single ‘best’ frequency, known as tonotopy (Winer and Lee, 2007). Early electrophysiological investigations revealed a tonotopic arrangement of neuronal frequency preferences in A1 in all species studied, including monkeys (Merzenich and Brugge, 1973), cats (Hind, 1953), ferrets (Kelly et al., 1986a), gerbils (Steffen et al., 1988), and rats (Sally and Kelly, 1988). More recent multielectrode recordings (Imaizumi et al., 2004; Bizley et al., 2005; Recanzone et al., 1999) and large-scale imaging experiments (Nelken et al., 2008) have confirmed the presence of a tonotopic gradient over large areas of A1 in carnivores and primates, while single-neuron recordings in awake animals have reported clear frequency gradients in marmosets (Bendor and Wang, 2008) and weaker tonotopy in cats (Goldstein et al., 1970).

In vivo two-photon calcium imaging can measure the stimulus preferences of a much larger number of individual neurons within a cortical region than the above techniques, improving the spatial sampling of mapping studies. The results of such experiments in mouse A1 over the past decade have challenged our understanding of the cortical tonotopic map. While previous electrophysiological and large-scale imaging studies confirmed an overall tonotopic organization in mouse A1 (Hackett et al., 2011; Guo et al., 2012; Stiebler et al., 1997), more recent two-photon imaging studies have reported different degrees of frequency tuning variability among neighboring neurons, from well-ordered local maps (Issa et al., 2014) to moderately heterogeneous tuning (Bandyopadhyay et al., 2010; Rothschild et al., 2010; Panniello et al., 2018; Romero et al., 2020). Similarly, two-photon imaging has revealed local heterogeneity in whisker selectivity in mouse primary somatosensory cortex (Kerr et al., 2007; Sato et al., 2007), and in orientation tuning in mouse primary visual cortex (V1) (Bonin et al., 2011).

There are several possible explanations for the local variation of A1 frequency preferences revealed by two-photon imaging studies compared to the smoother tonotopy described in electrophysiological studies. First, the heterogeneity may be due to the higher spatial sampling rate of two-photon imaging. Microelectrode studies are often based on responses that are summed across several neurons, and this spatial averaging may lead to smoother tonotopic gradients (Guo et al., 2012). Even in single neuron recordings, the spacing between cells is at least 50 μm (South and Weinberger, 1995), and typically ~100 μm or greater, so variations in stimulus preferences between neighboring cells are not usually examined (Kanold et al., 2014). Secondly, multielectrode recordings may be biased more towards the most robustly responding neurons in thalamorecipient layers, whereas most two-photon imaging studies have been restricted to the superficial layers of the cortex. Studies investigating layers 2/3 and 4 have usually found smoother tonotopy in the deeper layers (Guo et al., 2012; Winkowski and Kanold, 2013) (but see Tischbirek et al., 2019, reporting similar tonotopic organization across all layers of mouse auditory cortex). We tested these two explanations by comparing the frequency tuning of neurons in superficial auditory cortex measured using in vivo two-photon calcium imaging and high-density multielectrode (Neuropixels; Jun et al., 2017) recordings.

The variations in tonotopy across studies may also partly reflect a species difference in the cortical organization of rodents and higher mammals, particularly as local thalamic inputs to A1 in mice are also heterogeneous in their frequency tuning (Vasquez-Lopez et al., 2017). Two-photon imaging studies of primary visual cortex have revealed poorer spatial organization of orientation tuning in rodents than in cats (Bonin et al., 2011; Ohki et al., 2005), tree shrews (Lee et al., 2016), and ferrets (Wilson et al., 2017). The same may be true of tonotopy in A1. This view is supported by a recent study in marmosets, in which A1 was reported to be more tonotopically organized than in rats (Zeng et al., 2019). The present experiments further examined whether local heterogeneity in tonotopy is a general feature of mammalian A1, or a peculiarity of rodents. We conducted two-photon imaging experiments in mice and ferrets, and compared their local tonotopic organization.

It is not clear how spatial organization of tuning to a single sound frequency coincides with the known functions and complex frequency receptive fields of auditory cortex. Throughout the ascending auditory pathway, neurons progressively integrate spectral and temporal features of sound (Linden and Schreiner, 2003), and the receptive fields of many A1 neurons are poorly predicted by a model of linear tuning to a single sound frequency (Ahrens et al., 2008; Sadagopan and Wang, 2009). Recent studies have shown that the preferred frequencies of A1 neurons with irregular tuning curves are poorly mapped (Romero et al., 2020; Tischbirek et al., 2019). Our present results further demonstrate how A1 neurons with multipeaked frequency tuning curves impact on tonotopic organization.

This is the first application of 2-photon calcium imaging and Neuropixels recordings to study auditory processing in ferrets. Ferrets are a popular animal model in auditory neuroscience because they have a gyrencephalic cortex, their A1 is easily accessible, they are readily trained on behavioural tasks, and (unlike mice) their hearing range overlaps well with the human audiogram (Nodal and King, 2014).

We found that mice and ferrets show comparable local variation in frequency preferences among well-tuned neurons. Furthermore, neurons with more complex frequency receptive fields have poorer tonotopic arrangement in both species. Our findings suggest that ferrets and mice may use the same general principles for mapping frequency in A1, and explain past discrepancies in cortical maps described in studies using different species and experimental techniques.

We investigated how the frequency preferences of neurons in the superficial layers of ferret A1 are organized spatially, both locally (within ~0.25 mm²) and along the entire tonotopic axis (~3.5 mm), and how this organization compares to the local tonotopy in mice (Bandyopadhyay et al., 2010; Rothschild et al., 2010; Panniello et al., 2018). We used an AAV vector to express either GCaMP6m (four subjects) or GCaMP6f (four subjects) in the right A1 of ferrets (Figure 1A; Figure 1—figure supplement 1A,B) and recorded neuronal calcium transients in 3604 neurons across 32 imaging fields (e.g. Figure 1A,B), while presenting pure tones (0.3–41 kHz; 30–90 dB SPL) to the contralateral ear. To ensure that our recordings were carried out in layers 2/3, we imaged neuronal activity at 176 ± 26.83 μm (median ± SD) below the cortical surface (Figure 1—figure supplement 2).

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Frequency response organization at the local scale

Across all fields imaged in ferret A1, 693 of 3604 (19.23%) neurons were frequency-sensitive (two-way ANOVA; p<0.05). Neurons often showed a V-shaped Frequency Response Area (FRA; Figure 1B), with a clearly defined Best Frequency (BF). In the example field shown in Figure 1 (300 × 300 µm), 26.5% of neurons were significantly sensitive to tone frequency or frequency/level combinations, and the average BF across all frequency-sensitive neurons (Figure 1C) was 14.7 kHz (±0.75 kHz; mean ± SEM). As previously described for A1 of mice (Bandyopadhyay et al., 2010; Rothschild et al., 2010; Panniello et al., 2018), most neurons within this local region of ferret A1 were tuned to a similar BF, but there were some outliers preferring higher or lower BFs (Figure 1D). The BF of individual neurons in this field ranged from 2.2 kHz to 19.9 kHz, with 9% of BFs being more than one octave away from the mean.

The practice of assigning a BF is based on the assumption that neurons are tuned to a single preferred frequency, but some neurons can respond strongly to multiple frequencies (Bizley et al., 2005; Hackett et al., 2011; Sutter and Schreiner, 1991). We observed that the FRAs of frequency-sensitive neurons in our ferret A1 dataset could be classified into three broad types: 1) V-, I- or O-shaped FRAs, which all have a clear single BF (‘single-peaked neurons’); 2) FRAs with response peaks at two distinct frequencies (‘double-peaked neurons’), where one peak is usually substantially stronger than the other; and 3) more complex FRAs, often containing three response peaks and a poorly defined BF. Little is known about the functions of neurons with these different frequency-response profiles, and their local spatial distribution has not been directly investigated, although neurons with multi-peaked FRAs have been reported in the A1 of marmosets (Kadia and Wang, 2003), cats (Sutter and Schreiner, 1991), rats (Turner et al., 2005) and mice (Winkowski and Kanold, 2013). We hypothesized that complexity in the shape of FRAs could help explain some of the reported local variability in cortical tonotopy, as it is less clear where double-peaked and complex FRAs should be located on a tonotopic map.

Each frequency-sensitive neuron was classified using an automated algorithm as having a single-peaked, double-peaked or complex FRA (see Materials and methods; Figure 2A). In most imaging fields, we observed all three FRA classes, (as illustrated in Figure 2B), and the following analyses were restricted to imaging fields containing at least three neurons of each class. Best frequency was derived in the same manner for neurons across all three classes, as the peak of the level-averaged tuning curve (as shown in Figure 1C; see Methods and materials).

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Visual inspection of tonotopic maps (Figure 2B; Figure 2—figure supplement 1) suggested that the BFs of double-peaked (BF_d) and complex neurons (BF_c) may be more varied within a local field than those of single-peaked neurons (BF_s). To quantitatively compare the local tonotopic organization of neurons with single-peaked, double-peaked and complex FRAs, we computed a BF variance metric. This metric was defined as the octave difference per millimeter between the BF of each neuron and the median BF of all other neurons in the imaging field.

Best frequencies varied less within an imaging field among single-peaked neurons (1.34 ± 0.10 octaves; mean ± SEM) than among double-peaked neurons (2.16 ± 0.18 octaves; t-test: t = −4.15, p=4.1×10⁻⁵) and complex neurons (3.05 ± 0.24 octaves; t = 7.55, p=3.4×10⁻¹³) (Figure 2C). Within double-peaked neurons, the frequency variability of the second peak (peak 2) and BF_d were comparable (t = 1.91, p=0.057). Therefore, tonotopic organization within a local region of ferret A1 was less reliable among neurons with more complex frequency receptive fields.

We also investigated whether the three classes of neurons differed in their response strengths (Figure 2D). We found that the average deconvolved calcium response at the best frequency and level combination was stronger in single-peaked neurons compared to either double-peaked neurons (t-test: t = 2.22, p=0.027) or neurons with complex FRAs (t = 4.02, p=6.9×10⁻⁵). There was no significant difference in response strength between neurons with double-peaked and complex FRAs (t = 1.58, p=0.11). The Fano Factor calculated at the best frequency and level did not significantly differ between neurons in the three FRA classes (single- and double-peaked: t = 0.60, p=0.55; single-peaked and complex: t = 0.638, p=0.52), indicating that responses at BF were equally reliable for neurons with single- and multi-peaked FRAs (Figure 2E). As expected, when data from GCaMP6m and GCaMP6f injections were analyzed separately, we found a higher percentage of frequency-sensitive neurons in the GCaMP6m (32.29%), compared to the GCaMP6f dataset (10.81%). Importantly, both indicators showed similar effects of FRA class on local BF variance, response strength and response reliability (Figure 2—figure supplement 2), so our main findings are consistent across the two indicators, and data are pooled across all ferrets for our remaining analyses.

Extracellular recordings are known to be biased towards more active neurons, and Figure 2D suggests this may bias them towards single-peaked neurons. In addition, complex neurons may be expected to have more widespread dendritic branches, making them more prone to damage during electrode insertion. If either or both of these effects cause microelectrodes to oversample single-peaked neurons, this could explain the smoother tonotopic maps typically described using this technique. To investigate this possibility, we used high channel count microelectrodes to isolate the tone responses of single neurons in layers 2/3 of ferret A1 (Figure 2—figure supplement 3; see Materials and methods), and the recorded FRAs were classified in the same way. The anesthetic regime, surgery, and stimuli were similar to our imaging experiments. Although all three FRA classes were observed, the microelectrode recordings yielded a higher proportion of neurons with single-peaked FRAs (Likelihood Ratio Test: χ² = 68.45, p=1.1×10⁻¹⁶) than the imaging experiments, and fewer with double-peaked (χ² = 22.16, p=2.5×10⁻⁶) and complex (χ² = 30.06, p=4.2×10⁻⁸) FRAs (Figure 2F).

Contrary to our two-photon imaging results, we did not find a significant difference between the spike rates of neurons with single- and double-peaked FRAs (t-test: t = 1.29, p=0.20) or between those with single-peaked and complex FRAs (t = 0.05, p=0.96) in our microelectrode recordings (Figure 2—figure supplement 4). This may result from a bias towards the most strongly responsive multi-peaked neurons, given the small number of double-peaked and complex neurons measured using microelectrodes. Together, our results suggest that biases towards sampling single-peaked neurons in microelectrode recording studies could lead to estimates of more ordered tonotopy than two-photon calcium imaging in the same cortical region.

The FRAs of single-peaked neurons can be well approximated by their BF. However, this is not the case for neurons with double-peaked and, in particular, complex FRAs. Consequently, an analysis based on BF alone may be blind to an underlying organization of neurons with complex frequency receptive fields. To take into account a more complete representation of FRAs, we computed pairwise signal correlations. Correlations were calculated between pairs of frequency-sensitive neurons imaged simultaneously and belonging to the same FRA category: single-peaked, double-peaked and complex (Figure 3A). Signal correlations were found to differ across the three classes (one-way ANOVA: F = 389.70, p=1.6×10⁻¹⁶³). In post hoc pairwise tests (Tukey’s HSD), signal correlations within complex neurons (0.048 ± 0.003; mean ± SEM) were significantly lower than those for single-peaked (0.207 ± 0.002; p=9.6×10⁻¹⁰) and double-peaked neurons (0.193 ± 0.002; p=9.6×10⁻¹⁰). Similarly, signal correlations were significantly higher within single-peaked neurons than double-peaked neurons (p=4.4×10⁻⁵). Furthermore, signal correlations decreased with cortical distance between neurons for single-peaked (Pearson’s correlation: r = 0.21, p=5.3×10⁻²⁹; Figure 3E) and double-peaked cells (r = 0.17, p=1.5×10⁻⁹; Figure 3F), but not for complex neurons (r = 0.06; p=0.12; Figure 3G). These results further confirm that there is a greater degree of tonotopy for cells with simpler FRAs within a local cortical region.

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To investigate whether single- and double-peaked neurons share different local networks from those with complex FRAs, we calculated pairwise noise correlations, which are thought to reflect connectivity and common inputs between neurons (Hofer et al., 2011; Figure 3B). Indeed, the strength of noise correlations differed across the three FRA classes (one-way ANOVA: F = 109.50, p=9.4×10⁻⁴⁸). Noise correlations were significantly higher for single-peaked (0.138 ± 0.016; mean ± SEM) and double-peaked (0.136 ± 0.004) neurons than those with complex FRAs (0.059 ± 0.004) (Tukey’s HSD tests; single vs complex: p=9.6×10⁻¹⁰; double vs complex: p=9.6×10⁻¹⁰). Noise correlations did not differ between single-peaked and double-peaked neurons (p=0.86). For all FRA types, noise correlations significantly decreased for pairs of neurons that were located further apart within the imaging field (Figure 3—figure supplement 1A–C).

When we examined the correlation structure between neurons with different FRA classifications, we found that single- and double-peaked neurons had higher signal and noise correlations with one another than with complex neurons (Figure 3C,D). This suggests that neurons with complex FRAs may differ in their connectivity within the local cortical network from those with simpler frequency tuning.

Frequency response organization at the global scale

To investigate tonotopic organization along the entire extent of ferret primary auditory cortex, all neurons imaged in our eight ferrets were projected onto a common template of A1. These projections were aligned based on both an anatomical criterion (i.e. the gross anatomy of the ectosylvian gyrus), and a functional one (i.e. A1 boundaries derived from neuronal responses to tones) (Figure 4—figure supplement 1A,B; see Materials and methods).

When single-peaked neurons were projected onto this global A1 template, the expected tonotopic gradient was clearly evident (Figure 4A1), with high frequencies mapped onto the dorsal tip of the ectosylvian gyrus (Figure 4B1) and lower frequencies toward the ventral border with secondary posterior fields (Kelly et al., 1986a; Bizley et al., 2005). The large-scale spatial organization of frequency preferences along the tonotopic gradient was quantified as a correlation between BF_s and the neuron’s position along the tonotopic axis (Pearson’s correlation: r = −0.40, p=1.1×10⁻¹²; Figure 4C1).

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When examining only neurons with double-peaked FRAs (BF_d), global tonotopy was clearly visible (Figure 4A2, B2), and this tonotopic gradient was again statistically significant (r = 0.46, p=5.7×10⁻¹³; Figure 4C2). However, the BFs of double-peaked neurons varied more around their fitted tonotopic gradient (red line, Figure 4C2) than those of single-peaked neurons (t-test: t = 2.43, p=0.02), suggesting that neurons with double-peaked FRAs have a less smooth tonotopic organization.

The BFs of neurons with complex FRAs were also organized tonotopically on the global A1 template (r = 0.32, p=1.8×10⁻⁵; Figure 4A4, B4), but were more variable around the tonotopic gradient (Figure 4C4) than the BFs of either single-peaked (t = 6.83, p=2.7×10⁻¹¹) or double-peaked (t = 4.52, p=8.2×10⁻⁶) neurons.

In contrast to BF, other aspects of the neurons’ frequency responses were not found to be systematically ordered along the tonotopic gradient. The frequency of the second-strongest peak of double-peaked FRAs (peak 2) did not change systematically along this axis (r = −0.096, p=0.15; Figure 4A3, B3, C3), nor did the differences between the two peaks of double-peaked neurons (r = 0.13, p=0.046; Figure 4—figure supplement 2A–C).

These results indicate that, in ferrets, the BFs of A1 neurons are tonotopically organized at the global scale, but neurons with increasingly complex FRAs show more variance along the global tonotopic axis. In order to validate the method used for deconvolving calcium traces, we repeated our analyses on non-deconvolved ΔF/F₀ traces (Figure 4—figure supplement 3). The local BF variability was similar when computed from non-deconvolved (Figure 4—figure supplement 3A) and deconvolved traces (Figure 2C). In keeping with the data from the deconvolved signals, we found that the average amplitude of the calcium transient at BF was significantly higher in single-peaked neurons compared to double-peaked (t-test: t = 3.59, p=3.5×10⁻⁴) and complex neurons (t = 5.10, p=5.0×10⁻⁷) (Figure 4—figure supplement 3B; compare to Figure 2D). In addition, the Fano Factor calculated at BF from the non-deconvolved traces did not significantly differ between the three FRA classes (single- and double-peaked: t = 0.25, p=0.80; single-peaked and complex: t = 1.030, p=0.30; Figure 4—figure supplement 3C; compare to Figure 2E). At the global scale, the tonotopic organization was evident when BF was calculated from non-deconvolved traces (Figure 4—figure supplement 3D; compare to Figure 4B), and BF variability along the tonotopic axis was similar in the deconvolved and non-deconvolved datasets (two-way ANOVA: F = 1.24, p=0.27; Figure 4—figure supplement 3E).

Our analysis of both the local and global organization of frequency responses was carried out after removing the neuropil signal from each individual neuronal body. This procedure was necessary, as neuropil contamination can change the BF of individual neurons, particularly for those with complex frequency responses (see Materials and methods; Figure 4—figure supplement 4A,B), as also highlighted in a recent study of mouse tonotopy (Romero et al., 2020). For example, we found that neuropil signal introduced the appearance of a tonotopic order to an otherwise spatially disorganized signal – namely, the second peaks of double-peaked neurons (Figure 4—figure supplement 4D3).

Comparison of tonotopic organization in ferrets and mice

The contrast between local heterogeneity and global tonotopic organization of BFs, sometimes referred to as ‘fractured’ or ‘salt and pepper’ organization, has been previously reported in the primary auditory cortex of the mouse (Bandyopadhyay et al., 2010; Rothschild et al., 2010; Panniello et al., 2018). The results above show that a qualitatively similar organization exists in ferret A1, but that much of the heterogeneity at both the local and global scales arises from neurons with complex frequency receptive fields. To directly compare the local organization of frequency preferences between mice and ferrets, we imaged neuronal responses in the primary auditory cortex of 11 mice under the same experimental conditions.

The BFs of neurons and their spatial organization within an example imaging field in the mouse are shown in Figure 5A and B, respectively. In keeping with previous studies (Bandyopadhyay et al., 2010; Rothschild et al., 2010), these plots suggest that the spatial organization of BFs in the mouse primary auditory cortex is also locally heterogeneous. Across all imaging fields, 854 of 1964 (43.75%) neurons were frequency-sensitive in mice (two-way ANOVA; p<0.05), while only 693 of 3604 (19.23%) neurons were frequency-sensitive using the same statistical criterion in ferrets.

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Classification of neurons based on their FRAs confirmed that single-peaked, double-peaked, and complex neurons also exist in the mouse (Figure 5B; Figure 5—figure supplement 1A–B), as they do in the ferret (Figure 2B). However, the relative proportions of these three FRA classes differ between the two species (Figure 5C). Specifically, a higher percentage of frequency-sensitive neurons in the mouse (60.54%) showed ‘simpler’ frequency receptive fields with a single peak at BF than in the ferret (42.71%; Likelihood Ratio Test: χ² = 48.96, p=2.6×10⁻¹²). Conversely, ferrets had more double-peaked (31.75% vs 26.23%; χ² = 5.67, p=0.017) and complex FRAs (24.96% vs. 13.23%; χ² = 34.83, p=3.6×10⁻⁹) than mice. Single-peaked and double-peaked neurons in mice responded more strongly at BF than complex neurons (single- and double-peaked: t = −0.73, p=0.46; single-peaked and complex: t = 3.20, p=0.0010; double-peaked and complex: t = 3.43, p=1.9×10⁻⁴; Figure 5—figure supplement 1C), and trial-to-trial reliability in complex neurons was better than that of single-peaked neurons (single- and double-peaked: t = −0.99, p=0.32; single-peaked and complex: t = 2.37, p=0.018; Figure 5—figure supplement 1D).

Neurons with single-peaked FRAs showed broader frequency tuning in mice (0.91 ± 0.03 octaves) compared to ferrets (0.74 ± 0.03 octaves; t-test: t = 3.66, p=2.7×10⁻⁴). This could partially account for the larger proportion of single-peaked neurons in mice, if the dominant peak occupies a greater proportion of the maximum bandwidth. However, there were no significant species differences in tuning bandwidth for double-peaked (t = 0.76, p=0.44) or complex neurons (t = 0.72, p=0.47; Figure 5—figure supplement 2A). Furthermore, larger bandwidths of single-peaked neurons were not associated with larger deviations in local BF in ferrets (Pearson’s correlation: r = 0.020, p=0.73; Figure 5—figure supplement 2B), and were weakly correlated with smaller BF deviations in mice (r = −0.11, p=0.030; Figure 5—figure supplement 2C). In both species, bandwidth and BF variability were not significantly correlated in either double-peaked or complex neurons (p>0.05). Therefore, the sharpness of frequency tuning does not explain local BF variability.

Could the presence of double-peaked and complex FRAs help explain the local variance in cortical tonotopy in mice, as we observed in ferrets? To answer this question, the BF variance within an imaging field was calculated separately for single-peaked, double-peaked and complex neurons (Figure 5D), in every imaging field containing at least three neurons of each FRA class. Unlike in ferrets, the BF variability of double-peaked neurons (2.73 ± 0.33 oct/mm; mean ± SEM) was not significantly higher than that of single-peaked neurons (2.57 ± 0.16 oct/mm; t-test: t = 0.46, p=0.64) in mice, and the BF variability of the two peaks of double-peaked neurons did not differ significantly (t = 1.15, p=0.25). In line with our ferret data, however, the BFs of neurons with complex FRAs (4.14 ± 0.38 oct/mm) were more variable than those of single-peaked (t = 4.33, p=2.0×10⁻⁵) and double-peaked (t = 2.78, p=0.0060) neurons, suggesting that the frequency preferences of neurons with more complex FRAs are less tonotopically organized within the local microcircuit. To ensure that the smaller number of multi-peaked neurons in mice did not bias our estimates of BF variance, we repeated this analysis using the same number of neurons in each imaging field. Furthermore, we bootstrapped our results across different subgroups of neurons in each field. The same patterns of BF variance were found across single-peaked, double-peaked and complex cells in these control analyses (Figure 5—figure supplement 3B,C,E and F).

As many neurons in sensory cortex can be unresponsive or poorly tuned to pure tone frequency, it is common practice to map only neurons that are significantly modulated by sound frequency (Rothschild et al., 2010; Tischbirek et al., 2019). However, this inclusion criterion may favor the emergence of smoother BF distributions in mapping studies. Therefore, we repeated our BF analyses on all imaged neurons. The inclusion of more noisy neurons in this analysis predictably resulted in a larger proportion of complex FRAs overall. Importantly, mice again showed substantially more single-peaked neurons than ferrets (Figure 5—figure supplement 4A), as in our analysis of frequency-sensitive neurons (Figure 5C). Furthermore, more scatter in the spatial distribution of BF was observed with increased FRA complexity at both local (Figure 5—figure supplement 4B and C) and global (Figure 5—figure supplement 4D–G) levels of tonotopic organization, as we found for frequency-sensitive neurons (Figure 2C; Figure 4C; Figure 5D).

A comparison of the tonotopic organization between mice and ferrets is complicated by the anatomical and hearing range differences between the two species. The A1 tonotopic gradient is shorter in the mouse (~1 mm) (Guo et al., 2012; Stiebler et al., 1997) than the ferret (~3.5 mm) (Kelly et al., 1986a; Bizley et al., 2005), and the audible hearing range is typically 2–60 kHz in C57BL/6 mice (Heffner and Heffner, 2007; Ison et al., 2007) and 0.3–44 kHz in ferrets (Kelly et al., 1986b). To illustrate the importance of these species differences, Figure 5E compares histograms of the BF_s variance in ferrets and mice, expressed in units of octaves per mm. The BFs of single-peaked neurons were more variable within a millimetre of A1 in mice (2.18 ± 0.08 octave/mm) than in ferrets (1.33 ± 0.09; t-test: t = 5.62, p=2.5×10⁻⁸). This may be taken to suggest that BF_s is more locally variable in mouse primary auditory cortex than in ferrets, but this metric does not account for the species differences in hearing range and tonotopic axis length.

To account for this difference, we computed the percentage of neurons that had a BF within the expected frequency range in each species, for a given imaging field. The expected BF range was calculated based on the diameter of the imaging field, the median BF across all neurons in the field, and an expected tonotopic gradient of 4.91 oct/mm in mice and 2.93 oct/mm in ferrets. To simplify the calculation, this measure assumes a constant log frequency gradient across the entire tonotopic axis. The percentage of neurons with a BF within the expected range provided a measure of the variance in tonotopic organization that could be directly compared between species, and was computed separately for neurons with: 1) single-peaked FRAs; 2) double-peaked FRAs; 3) complex FRAs; and 4) all three FRA types combined (Figure 5F). The resulting values varied across the three different FRA classes (two-way ANOVA: F = 8.41, p=4.3×10⁻⁴), but not between species (F = 3.72, p=0.057), and there was no significant interaction between FRA class and species (F = 1.66, p=0.19). Furthermore, post hoc pairwise Tukey’s HSD tests found no significant differences between BF variability in mice and ferrets for single-peaked (p=0.97), double-peaked (p=0.12), or complex neurons (p=1.0).

Taken together, these results suggest that mice and ferrets have equivalent local heterogeneity in frequency preferences within each FRA class of neurons in layers 2/3 of primary auditory cortex. More complex frequency tuning is associated with more variance in the frequency preferences of neighboring neurons in both species.

We used two-photon calcium imaging to study the representation of sound frequency in populations of neighboring neurons in mouse and ferret primary auditory cortex. Previous studies in mice have described global A1 tonotopy with local variability of frequency preferences (Bandyopadhyay et al., 2010; Rothschild et al., 2010; Panniello et al., 2018; Romero et al., 2020; Winkowski and Kanold, 2013; Tischbirek et al., 2019), and here we show that ferret A1 shares the same spatial organization. Furthermore, we demonstrate that the complexity of frequency receptive fields can account for much of the local heterogeneity in both mice and ferrets. Thus, the locally heterogeneous sensory maps described in primary cortical areas may be a consequence of complex stimulus feature extraction.

We have provided our data and Matlab scripts for generating our figures on Dryad: https://doi.org/10.5061/dryad.9ghx3ffd9.

1. 1. Gaucher Q
   2. Panniello M
   3. Ivanov AZ
   4. Dahmen JC
   5. King AJ
   6. Walker KMM

   (2019) Dryad Digital Repository

   Complexity of frequency receptive fields predicts tonotopic variability across species.

   https://doi.org/10.5061/dryad.9ghx3ffd9

1. 1. Ahrens MB
   2. Linden JF
   3. Sahani M

   (2008) Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods

   Journal of Neuroscience 28:1929–1942.

   https://doi.org/10.1523/JNEUROSCI.3377-07.2008

   • PubMed
   • Google Scholar
2. 1. Andermann ML
   2. Gilfoy NB
   3. Goldey GJ
   4. Sachdev RN
   5. Wölfel M
   6. McCormick DA
   7. Reid RC
   8. Levene MJ

   (2013) Chronic cellular imaging of entire cortical columns in awake mice using microprisms

   Neuron 80:900–913.

   https://doi.org/10.1016/j.neuron.2013.07.052

   • PubMed
   • Google Scholar
3. 1. Anderson LA
   2. Christianson GB
   3. Linden JF

   (2009) Mouse auditory cortex differs from visual and somatosensory cortices in the laminar distribution of cytochrome oxidase and acetylcholinesterase

   Brain Research 1252:130–142.

   https://doi.org/10.1016/j.brainres.2008.11.037

   • PubMed
   • Google Scholar
4. 1. Atencio CA
   2. Schreiner CE

   (2010) Columnar connectivity and laminar processing in cat primary auditory cortex

   PLOS ONE 5:e9521.

   https://doi.org/10.1371/journal.pone.0009521

   • PubMed
   • Google Scholar
5. 1. Bandyopadhyay S
   2. Shamma SA
   3. Kanold PO

   (2010) Dichotomy of functional organization in the mouse auditory cortex

   Nature Neuroscience 13:361–368.

   https://doi.org/10.1038/nn.2490

   • PubMed
   • Google Scholar
6. 1. Barnstedt O
   2. Keating P
   3. Weissenberger Y
   4. King AJ
   5. Dahmen JC

   (2015) Functional microarchitecture of the mouse dorsal inferior colliculus revealed through in vivo Two-Photon calcium imaging

   Journal of Neuroscience 35:10927–10939.

   https://doi.org/10.1523/JNEUROSCI.0103-15.2015

   • PubMed
   • Google Scholar
7. 1. Bendor D
   2. Wang X

   (2008) Neural response properties of primary, Rostral, and rostrotemporal core fields in the auditory cortex of marmoset monkeys

   Journal of Neurophysiology 100:888–906.

   https://doi.org/10.1152/jn.00884.2007

   • PubMed
   • Google Scholar
8. 1. Bizley JK
   2. Nodal FR
   3. Nelken I
   4. King AJ

   (2005) Functional organization of ferret auditory cortex

   Cerebral Cortex 15:1637–1653.

   https://doi.org/10.1093/cercor/bhi042

   • PubMed
   • Google Scholar
9. 1. Bizley JK
   2. Walker KM
   3. Silverman BW
   4. King AJ
   5. Schnupp JW

   (2009) Interdependent encoding of pitch, timbre, and spatial location in auditory cortex

   Journal of Neuroscience 29:2064–2075.

   https://doi.org/10.1523/JNEUROSCI.4755-08.2009

   • PubMed
   • Google Scholar
10. 1. Bonin V
    2. Histed MH
    3. Yurgenson S
    4. Reid RC

    (2011) Local diversity and fine-scale organization of receptive fields in mouse visual cortex

    Journal of Neuroscience 31:18506–18521.

    https://doi.org/10.1523/JNEUROSCI.2974-11.2011

    • PubMed
    • Google Scholar
11. 1. Chen TW
    2. Wardill TJ
    3. Sun Y
    4. Pulver SR
    5. Renninger SL
    6. Baohan A
    7. Schreiter ER
    8. Kerr RA
    9. Orger MB
    10. Jayaraman V
    11. Looger LL
    12. Svoboda K
    13. Kim DS

    (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity

    Nature 499:295–300.

    https://doi.org/10.1038/nature12354

    • PubMed
    • Google Scholar
12. 1. Christianson GB
    2. Sahani M
    3. Linden JF

    (2011) Depth-dependent temporal response properties in core auditory cortex

    Journal of Neuroscience 31:12837–12848.

    https://doi.org/10.1523/JNEUROSCI.2863-11.2011

    • PubMed
    • Google Scholar
13. 1. Cooke JE
    2. King AJ
    3. Willmore BDB
    4. Schnupp JWH

    (2018) Contrast gain control in mouse auditory cortex

    Journal of Neurophysiology 120:1872–1884.

    https://doi.org/10.1152/jn.00847.2017

    • PubMed
    • Google Scholar
14. 1. Cossell L
    2. Iacaruso MF
    3. Muir DR
    4. Houlton R
    5. Sader EN
    6. Ko H
    7. Hofer SB
    8. Mrsic-Flogel TD

    (2015) Functional organization of excitatory synaptic strength in primary visual cortex

    Nature 518:399–403.

    https://doi.org/10.1038/nature14182

    • PubMed
    • Google Scholar
15. 1. Dahmen JC
    2. Hartley DE
    3. King AJ

    (2008) Stimulus-timing-dependent plasticity of cortical frequency representation

    Journal of Neuroscience 28:13629–13639.

    https://doi.org/10.1523/JNEUROSCI.4429-08.2008

    • PubMed
    • Google Scholar
16. 1. Feng L
    2. Wang X

    (2017) Harmonic template neurons in primate auditory cortex underlying complex sound processing

    PNAS 114:E840–E848.

    https://doi.org/10.1073/pnas.1607519114

    • PubMed
    • Google Scholar
17. 1. Goldstein MH
    2. Abeles M
    3. Daly RL
    4. McIntosh J

    (1970) Functional architecture in cat primary auditory cortex: tonotopic organization

    Journal of Neurophysiology 33:188–197.

    https://doi.org/10.1152/jn.1970.33.1.188

    • PubMed
    • Google Scholar
18. 1. Guo W
    2. Chambers AR
    3. Darrow KN
    4. Hancock KE
    5. Shinn-Cunningham BG
    6. Polley DB

    (2012) Robustness of cortical topography across fields, Laminae, anesthetic states, and neurophysiological signal types

    Journal of Neuroscience 32:9159–9172.

    https://doi.org/10.1523/JNEUROSCI.0065-12.2012

    • PubMed
    • Google Scholar
19. 1. Hackett TA
    2. Barkat TR
    3. O'Brien BM
    4. Hensch TK
    5. Polley DB

    (2011) Linking topography to tonotopy in the mouse auditory thalamocortical circuit

    Journal of Neuroscience 31:2983–2995.

    https://doi.org/10.1523/JNEUROSCI.5333-10.2011

    • PubMed
    • Google Scholar
20. 1. Heffner HE
    2. Heffner RS

    (2007)

    Hearing ranges of laboratory animals

    Journal of the American Association for Laboratory Animal Science : JAALAS 46:20–22.

    • PubMed
    • Google Scholar
21. 1. Hind JE

    (1953) An electrophysiological determination of tonotopic organization in auditory cortex of cat

    Journal of Neurophysiology 16:475–489.

    https://doi.org/10.1152/jn.1953.16.5.475

    • PubMed
    • Google Scholar
22. 1. Hofer SB
    2. Ko H
    3. Pichler B
    4. Vogelstein J
    5. Ros H
    6. Zeng H
    7. Lein E
    8. Lesica NA
    9. Mrsic-Flogel TD

    (2011) Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex

    Nature Neuroscience 14:1045–1052.

    https://doi.org/10.1038/nn.2876

    • PubMed
    • Google Scholar
23. 1. Imaizumi K
    2. Priebe NJ
    3. Crum PA
    4. Bedenbaugh PH
    5. Cheung SW
    6. Schreiner CE

    (2004) Modular functional organization of cat anterior auditory field

    Journal of Neurophysiology 92:444–457.

    https://doi.org/10.1152/jn.01173.2003

    • PubMed
    • Google Scholar
24. 1. Ison JR
    2. Allen PD
    3. O'Neill WE

    (2007) Age-related hearing loss in C57BL/6J mice has both frequency-specific and non-frequency-specific components that produce a hyperacusis-like exaggeration of the acoustic startle reflex

    Journal of the Association for Research in Otolaryngology 8:539–550.

    https://doi.org/10.1007/s10162-007-0098-3

    • PubMed
    • Google Scholar
25. 1. Issa JB
    2. Haeffele BD
    3. Agarwal A
    4. Bergles DE
    5. Young ED
    6. Yue DT

    (2014) Multiscale optical Ca2+ imaging of tonal organization in mouse auditory cortex

    Neuron 83:944–959.

    https://doi.org/10.1016/j.neuron.2014.07.009

    • PubMed
    • Google Scholar
26. 1. Jun JJ
    2. Steinmetz NA
    3. Siegle JH
    4. Denman DJ
    5. Bauza M
    6. Barbarits B
    7. Lee AK
    8. Anastassiou CA
    9. Andrei A
    10. Aydın Ç
    11. Barbic M
    12. Blanche TJ
    13. Bonin V
    14. Couto J
    15. Dutta B
    16. Gratiy SL
    17. Gutnisky DA
    18. Häusser M
    19. Karsh B
    20. Ledochowitsch P
    21. Lopez CM
    22. Mitelut C
    23. Musa S
    24. Okun M
    25. Pachitariu M
    26. Putzeys J
    27. Rich PD
    28. Rossant C
    29. Sun WL
    30. Svoboda K
    31. Carandini M
    32. Harris KD
    33. Koch C
    34. O'Keefe J
    35. Harris TD

    (2017) Fully integrated silicon probes for high-density recording of neural activity

    Nature 551:232–236.

    https://doi.org/10.1038/nature24636

    • PubMed
    • Google Scholar
27. 1. Kadia SC
    2. Wang X

    (2003) Spectral integration in A1 of awake primates: neurons with single- and multipeaked tuning characteristics

    Journal of Neurophysiology 89:1603–1622.

    https://doi.org/10.1152/jn.00271.2001

    • PubMed
    • Google Scholar
28. 1. Kanold PO
    2. Nelken I
    3. Polley DB

    (2014) Local versus global scales of organization in auditory cortex

    Trends in Neurosciences 37:502–510.

    https://doi.org/10.1016/j.tins.2014.06.003

    • PubMed
    • Google Scholar
29. Software

    1. Karsh B
    2. Janelia Research Campus

    (2017) SpikeGLX, version Release_v20170901_phase3A

    GitHub.

    https://github.com/billkarsh/SpikeGLX
30. 1. Kelly JB
    2. Judge PW
    3. Phillips DP

    (1986a) Representation of the cochlea in primary auditory cortex of the ferret (Mustela putorius)

    Hearing Research 24:111–115.

    https://doi.org/10.1016/0378-5955(86)90054-7

    • Google Scholar
31. 1. Kelly JB
    2. Kavanagh GL
    3. Dalton JCH

    (1986b) Hearing in the ferret (Mustela putorius): Thresholds for pure tone detection

    Hearing Research 24:269–275.

    https://doi.org/10.1016/0378-5955(86)90025-0

    • Google Scholar
32. 1. Kerr JN
    2. de Kock CP
    3. Greenberg DS
    4. Bruno RM
    5. Sakmann B
    6. Helmchen F

    (2007) Spatial organization of neuronal population responses in layer 2/3 of rat barrel cortex

    Journal of Neuroscience 27:13316–13328.

    https://doi.org/10.1523/JNEUROSCI.2210-07.2007

    • PubMed
    • Google Scholar
33. 1. Lee KS
    2. Huang X
    3. Fitzpatrick D

    (2016) Topology of ON and OFF inputs in visual cortex enables an invariant columnar architecture

    Nature 533:90–94.

    https://doi.org/10.1038/nature17941

    • PubMed
    • Google Scholar
34. 1. Linden JF
    2. Schreiner CE

    (2003) Columnar transformations in auditory cortex? A comparison to visual and somatosensory cortices

    Cerebral Cortex 13:83–89.

    https://doi.org/10.1093/cercor/13.1.83

    • PubMed
    • Google Scholar
35. 1. Ludwig KA
    2. Miriani RM
    3. Langhals NB
    4. Joseph MD
    5. Anderson DJ
    6. Kipke DR

    (2009) Using a common average reference to improve cortical neuron recordings from microelectrode arrays

    Journal of Neurophysiology 101:1679–1689.

    https://doi.org/10.1152/jn.90989.2008

    • PubMed
    • Google Scholar
36. 1. Meng X
    2. Winkowski DE
    3. Kao JPY
    4. Kanold PO

    (2017) Sublaminar subdivision of mouse auditory cortex layer 2/3 based on functional translaminar connections

    The Journal of Neuroscience 37:10200–10214.

    https://doi.org/10.1523/JNEUROSCI.1361-17.2017

    • PubMed
    • Google Scholar
37. 1. Merzenich MM
    2. Brugge JF

    (1973) Representation of the cochlear partition of the superior temporal plane of the macaque monkey

    Brain Research 50:275–296.

    https://doi.org/10.1016/0006-8993(73)90731-2

    • PubMed
    • Google Scholar
38. 1. Nelken I
    2. Bizley JK
    3. Nodal FR
    4. Ahmed B
    5. King AJ
    6. Schnupp JW

    (2008) Responses of auditory cortex to complex stimuli: functional organization revealed using intrinsic optical signals

    Journal of Neurophysiology 99:1928–1941.

    https://doi.org/10.1152/jn.00469.2007

    • PubMed
    • Google Scholar
39. Book

    1. Nodal FR
    2. King AJ

    (2014) Biology and Diseases of the Ferret

    In: Fox JG, Marini RP, editors. Hearing and Auditory Function in Ferrets. Wiley-Blackwell. pp. 685–710.

    https://doi.org/10.1002/9781118782699.ch29

    • Google Scholar
40. 1. Ohki K
    2. Chung S
    3. Ch'ng YH
    4. Kara P
    5. Reid RC

    (2005) Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex

    Nature 433:597–603.

    https://doi.org/10.1038/nature03274

    • PubMed
    • Google Scholar
41. Software

    1. Olsen T

    (2020) Current source density (CSD)

    MATLAB Central File Exchange.

    https://uk.mathworks.com/matlabcentral/fileexchange/69399-current-source-density-csd
42. 1. Oviedo HV
    2. Bureau I
    3. Svoboda K
    4. Zador AM

    (2010) The functional asymmetry of auditory cortex is reflected in the organization of local cortical circuits

    Nature Neuroscience 13:1413–1420.

    https://doi.org/10.1038/nn.2659

    • PubMed
    • Google Scholar
43. Preprint

    1. Pachitariu M
    2. Steinmetz N
    3. Kadir S
    4. Carandini M
    5. Harris KD

    (2016) Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

    bioRxiv.

    https://doi.org/10.1101/061481

    • Google Scholar
44. 1. Pachitariu M
    2. Stringer C
    3. Harris KD

    (2018) Robustness of spike deconvolution for neuronal calcium imaging

    The Journal of Neuroscience 38:7976–7985.

    https://doi.org/10.1523/JNEUROSCI.3339-17.2018

    • PubMed
    • Google Scholar
45. Software

    1. Pachitariu M
    2. Steinmetz N

    (2019) Kilosort2: automated spike sorting with drift tracking and template matching on GPUs, version 2.0

    GitHub.

    https://github.com/MouseLand/Kilosort2
46. Software

    1. Pachitariu M
    2. Stringer C

    (2018) Suite2p, version 0.6

    GitHub.

    https://github.com/MouseLand/suite2p
47. 1. Panniello M
    2. King AJ
    3. Dahmen JC
    4. Walker KMM

    (2018) Local and global spatial organization of interaural level difference and frequency preferences in auditory cortex

    Cerebral Cortex 28:350–369.

    https://doi.org/10.1093/cercor/bhx295

    • PubMed
    • Google Scholar
48. 1. Pettersen KH
    2. Devor A
    3. Ulbert I
    4. Dale AM
    5. Einevoll GT

    (2006) Current-source density estimation based on inversion of electrostatic forward solution: effects of finite extent of neuronal activity and conductivity discontinuities

    Journal of Neuroscience Methods 154:116–133.

    https://doi.org/10.1016/j.jneumeth.2005.12.005

    • Google Scholar
49. 1. Recanzone GH
    2. Schreiner CE
    3. Sutter ML
    4. Beitel RE
    5. Merzenich MM

    (1999) Functional organization of spectral receptive fields in the primary auditory cortex of the owl monkey

    The Journal of Comparative Neurology 415:460–481.

    https://doi.org/10.1002/(SICI)1096-9861(19991227)415:4<460::AID-CNE4>3.0.CO;2-F

    • PubMed
    • Google Scholar
50. 1. Romani GL
    2. Williamson SJ
    3. Kaufman L

    (1982) Tonotopic organization of the human auditory cortex

    Science 216:1339–1340.

    https://doi.org/10.1126/science.7079770

    • PubMed
    • Google Scholar
51. 1. Romero S
    2. Hight AE
    3. Clayton KK
    4. Resnik J
    5. Williamson RS
    6. Hancock KE
    7. Polley DB

    (2020) Cellular and widefield imaging of sound frequency organization in primary and higher order fields of the mouse auditory cortex

    Cerebral Cortex 30:1603–1622.

    https://doi.org/10.1093/cercor/bhz190

    • Google Scholar
52. Software

    1. Rossant C
    2. Harris K
    3. Carandini M

    (2019) Phy, version 2.0

    GitHub.

    https://github.com/cortex-lab/phy
53. 1. Rothschild G
    2. Nelken I
    3. Mizrahi A

    (2010) Functional organization and population dynamics in the mouse primary auditory cortex

    Nature Neuroscience 13:353–360.

    https://doi.org/10.1038/nn.2484

    • PubMed
    • Google Scholar
54. 1. Sadagopan S
    2. Wang X

    (2009) Nonlinear spectrotemporal interactions underlying selectivity for complex sounds in auditory cortex

    Journal of Neuroscience 29:11192–11202.

    https://doi.org/10.1523/JNEUROSCI.1286-09.2009

    • PubMed
    • Google Scholar
55. 1. Sally SL
    2. Kelly JB

    (1988) Organization of auditory cortex in the albino rat: sound frequency

    Journal of Neurophysiology 59:1627–1638.

    https://doi.org/10.1152/jn.1988.59.5.1627

    • PubMed
    • Google Scholar
56. 1. Sato TR
    2. Gray NW
    3. Mainen ZF
    4. Svoboda K

    (2007) The functional microarchitecture of the mouse barrel cortex

    PLOS Biology 5:e189.

    https://doi.org/10.1371/journal.pbio.0050189

    • PubMed
    • Google Scholar
57. 1. Shoham S
    2. O’Connor DH
    3. Segev R

    (2006) How silent is the brain: is there a “dark matter” problem in neuroscience?

    Journal of Comparative Physiology A 192:777–784.

    https://doi.org/10.1007/s00359-006-0117-6

    • Google Scholar
58. 1. South DA
    2. Weinberger NM

    (1995) A comparison of tone-evoked response properties of 'cluster' recordings and their constituent single cells in the auditory cortex

    Brain Research 704:275–288.

    https://doi.org/10.1016/0006-8993(95)01134-x

    • PubMed
    • Google Scholar
59. Book

    1. Steffen H
    2. Simonis C
    3. Thomas H
    4. Tillein J
    5. Scheich H

    (1988) Auditory Pathway

    In: Syka J, Masterton RB, editors. Auditory Cortex: Multiple Fields, Their Architectonics and Connections in the Mongolian Gerbil. Springer. pp. 223–228.

    https://doi.org/10.1007/978-1-4684-1300-7_33

    • Google Scholar
60. 1. Stiebler I
    2. Neulist R
    3. Fichtel I
    4. Ehret G

    (1997) The auditory cortex of the house mouse: left-right differences, tonotopic organization and quantitative analysis of frequency representation

    Journal of Comparative Physiology A: Sensory, Neural, and Behavioral Physiology 181:559–571.

    https://doi.org/10.1007/s003590050140

    • Google Scholar
61. 1. Sutter ML
    2. Schreiner CE

    (1991) Physiology and topography of neurons with multipeaked tuning curves in cat primary auditory cortex

    Journal of Neurophysiology 65:1207–1226.

    https://doi.org/10.1152/jn.1991.65.5.1207

    • PubMed
    • Google Scholar
62. 1. Szymanski FD
    2. Garcia-Lazaro JA
    3. Schnupp JW

    (2009) Current source density profiles of stimulus-specific adaptation in rat auditory cortex

    Journal of Neurophysiology 102:1483–1490.

    https://doi.org/10.1152/jn.00240.2009

    • PubMed
    • Google Scholar
63. 1. Szymanski FD
    2. Rabinowitz NC
    3. Magri C
    4. Panzeri S
    5. Schnupp JW

    (2011) The laminar and temporal structure of stimulus information in the phase of field potentials of auditory cortex

    Journal of Neuroscience 31:15787–15801.

    https://doi.org/10.1523/JNEUROSCI.1416-11.2011

    • PubMed
    • Google Scholar
64. 1. Tischbirek CH
    2. Noda T
    3. Tohmi M
    4. Birkner A
    5. Nelken I
    6. Konnerth A

    (2019) In Vivo Functional Mapping of a Cortical Column at Single-Neuron Resolution

    Cell Reports 27:1319–1326.

    https://doi.org/10.1016/j.celrep.2019.04.007

    • PubMed
    • Google Scholar
65. 1. Turner JG
    2. Hughes LF
    3. Caspary DM

    (2005) Divergent response properties of layer-V neurons in rat primary auditory cortex

    Hearing Research 202:129–140.

    https://doi.org/10.1016/j.heares.2004.09.011

    • PubMed
    • Google Scholar
66. 1. Vasquez-Lopez SA
    2. Weissenberger Y
    3. Lohse M
    4. Keating P
    5. King AJ
    6. Dahmen JC

    (2017) Thalamic input to auditory cortex is locally heterogeneous but globally tonotopic

    eLife 6:e25141.

    https://doi.org/10.7554/eLife.25141

    • PubMed
    • Google Scholar
67. 1. Wilson DE
    2. Smith GB
    3. Jacob AL
    4. Walker T
    5. Dimidschstein J
    6. Fishell G
    7. Fitzpatrick D

    (2017) GABAergic neurons in ferret visual cortex participate in functionally specific networks

    Neuron 93:1058–1065.

    https://doi.org/10.1016/j.neuron.2017.02.035

    • PubMed
    • Google Scholar
68. 1. Winer JA
    2. Lee CC

    (2007) The distributed auditory cortex

    Hearing Research 229:3–13.

    https://doi.org/10.1016/j.heares.2007.01.017

    • PubMed
    • Google Scholar
69. 1. Winkowski DE
    2. Kanold PO

    (2013) Laminar transformation of frequency organization in auditory cortex

    Journal of Neuroscience 33:1498–1508.

    https://doi.org/10.1523/JNEUROSCI.3101-12.2013

    • PubMed
    • Google Scholar
70. 1. Zeng HH
    2. Huang JF
    3. Chen M
    4. Wen YQ
    5. Shen ZM
    6. Poo MM

    (2019) Local homogeneity of tonotopic organization in the primary auditory cortex of marmosets

    PNAS 116:3239–3244.

    https://doi.org/10.1073/pnas.1816653116

    • PubMed
    • Google Scholar

Author details

1. Quentin Gaucher

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology, Writing - review and editing

   Contributed equally with

   Mariangela Panniello

   Competing interests

   No competing interests declared

2. Mariangela Panniello

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Quentin Gaucher

   Competing interests

   No competing interests declared

3. Aleksandar Z Ivanov

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Johannes C Dahmen

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9889-8303

5. Andrew J King

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   Senior editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0001-5180-7179

6. Kerry MM Walker

   Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   kerry.walker@dpag.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1043-5302

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