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State-dependent encoding of sound and behavioral meaning in a tertiary region of the ferret auditory cortex

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Abstract

In higher sensory cortices, there is a gradual transformation from sensation to perception and action. In the auditory system, this transformation is revealed by responses in the rostral ventral posterior auditory field (VPr), a tertiary area in the ferret auditory cortex, which shows long-term learning in trained compared to naïve animals, arising from selectively enhanced responses to behaviorally relevant target stimuli. This enhanced representation is further amplified during active performance of spectral or temporal auditory discrimination tasks. VPr also shows sustained short-term memory activity after target stimulus offset, correlated with task response timing and action. These task-related changes in auditory filter properties enable VPr neurons to quickly and nimbly switch between different responses to the same acoustic stimuli, reflecting either spectrotemporal properties, timing, or behavioral meaning of the sound. Furthermore, they demonstrate an interaction between the dynamics of short-term attention and long-term learning, as incoming sound is selectively attended, recognized, and translated into action.

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The custom scripts written in MATLAB and Python used in this study are available from the corresponding author upon reasonable request.

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The data supporting the study findings are available from the corresponding author upon reasonable request.

References

  1. 1.

    Afraz, A., Yamins, D. L. K. & DiCarlo, J. J. Neural mechanisms underlying visual object recognition. Cold Spring Harb. Symp. Quant. Biol. 79, 99–107 (2014).

  2. 2.

    Yau, J. M., Kim, S. S., Thakur, P. H. & Bensmaia, S. J. Feeling form: the neural basis of haptic shape perception. J. Neurophysiol. 115, 631–642 (2016).

  3. 3.

    Kornblith, S. & Tsao, D. Y. How thoughts arise from sights: inferotemporal and prefrontal contributions to vision. Curr. Opin. Neurobiol. 46, 208–218 (2017).

  4. 4.

    Arcaro, M. J., Schade, P. F., Vincent, J. L., Ponce, C. R. & Livingstone, M. S. Seeing faces is necessary for face-domain formation. Nat. Neurosci. 20, 1404–1412 (2017).

  5. 5.

    Hernández-Pérez, R. et al. Tactile object categories can be decoded from the parietal and lateral-occipital cortices. Neuroscience 352, 226–235 (2017).

  6. 6.

    Rossi-Pool, R. et al. Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli. Proc. Natl Acad. Sci. USA 113, E7966–E7975 (2016).

  7. 7.

    Romo, R., Lemus, L. & de Lafuente, V. Sense, memory, and decision-making in the somatosensory cortical network. Curr. Opin. Neurobiol. 22, 914–919 (2012).

  8. 8.

    Freedman, D. J. & Assad, J. A. Neuronal mechanisms of visual categorization: an abstract view on decision making. Annu. Rev. Neurosci. 39, 129–147 (2016).

  9. 9.

    Rojas-Hortelano, E., Concha, L. & de Lafuente, V. The parietal cortices participate in encoding, short-term memory, and decision-making related to tactile shape. J. Neurophysiol. 112, 1894–1902 (2014).

  10. 10.

    Niwa, M., Johnson, J. S., O’Connor, K. N. & Sutter, M. L. Differences between primary auditory cortex and auditory belt related to encoding and choice for AM sounds. J. Neurosci. 33, 8378–8395 (2013).

  11. 11.

    Atiani, S. et al. Emergent selectivity for task-relevant stimuli in higher-order auditory cortex. Neuron 82, 486–499 (2014).

  12. 12.

    Tsunada, J., Liu, A. S. K., Gold, J. I. & Cohen, Y. E. Causal contribution of primate auditory cortex to auditory perceptual decision-making. Nat. Neurosci. 19, 135–142 (2016).

  13. 13.

    Dong, C., Qin, L., Zhao, Z., Zhong, R. & Sato, Y. Behavioral modulation of neural encoding of click-trains in the primary and nonprimary auditory cortex of cats. J. Neurosci. 33, 13126–13137 (2013).

  14. 14.

    Nodal, F. R. & King, A. J. Biology and Diseases of the Ferret. (Wiley-Blackwell, Hoboken, 2014).

  15. 15.

    Fritz, J., Shamma, S., Elhilali, M. & Klein, D. Rapid task-related plasticity of spectrotemporal receptive fields in primary auditory cortex. Nat. Neurosci. 6, 1216–1223 (2003).

  16. 16.

    Fritz, J. B., Elhilali, M. & Shamma, S. A. Differential dynamic plasticity of A1 receptive fields during multiple spectral tasks. J. Neurosci. 25, 7623–7635 (2005).

  17. 17.

    David, S. V., Fritz, J. B. & Shamma, S. A. Task reward structure shapes rapid receptive field plasticity in auditory cortex. Proc. Natl Acad. Sci. USA 109, 2144–2149 (2012).

  18. 18.

    Fritz, J. B., David, S. V., Radtke-Schuller, S., Yin, P. & Shamma, S. A. Adaptive, behaviorally gated, persistent encoding of task-relevant auditory information in ferret frontal cortex. Nat. Neurosci. 13, 1011–1019 (2010).

  19. 19.

    Bizley, J. K., Nodal, F. R., Nelken, I. & King, A. J. Functional organization of ferret auditory cortex. Cereb. Cortex 15, 1637–1653 (2005).

  20. 20.

    Bizley, J. K., Bajo, V. M., Nodal, F. R. & King, A. J. Cortico-cortical connectivity within ferret auditory cortex. J. Comp. Neurol. 523, 2187–2210 (2015).

  21. 21.

    Radtke-Schuller, S. Cyto- and Myeloarchitectural Brain Atlas of the Ferret (Springer International, Cham, 2018).

  22. 22.

    Pallas, S. L. & Sur, M. Visual projections induced into the auditory pathway of ferrets: II. Corticocortical connections of primary auditory cortex. J. Comp. Neurol. 337, 317–333 (1993).

  23. 23.

    Bajo, V. M., Nodal, F. R., Bizley, J. K., Moore, D. R. & King, A. J. The ferret auditory cortex: descending projections to the inferior colliculus. Cereb. Cortex 17, 475–491 (2007).

  24. 24.

    Recanzone, G. H., Schreiner, C. E. & Merzenich, M. M. Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J. Neurosci. 13, 87–103 (1993).

  25. 25.

    Galván, V. V. & Weinberger, N. M. Long-term consolidation and retention of learning-induced tuning plasticity in the auditory cortex of the guinea pig. Neurobiol. Learn. Mem. 77, 78–108 (2002).

  26. 26.

    Reed, A. et al. Cortical map plasticity improves learning but is not necessary for improved performance. Neuron 70, 121–131 (2011).

  27. 27.

    Froemke, R. C. et al. Long-term modification of cortical synapses improves sensory perception. Nat. Neurosci. 16, 79–88 (2013).

  28. 28.

    Bieszczad, K. M., Miasnikov, A. A. & Weinberger, N. M. Remodeling sensory cortical maps implants specific behavioral memory. Neuroscience 246, 40–51 (2013).

  29. 29.

    Wang, X., Lu, T., Snider, R. K. & Liang, L. Sustained firing in auditory cortex evoked by preferred stimuli. Nature 435, 341–346 (2005).

  30. 30.

    Gilbert, C. D. & Li, W. Top-down influences on visual processing. Nat. Rev. Neurosci. 14, 350–363 (2013).

  31. 31.

    Caras, M. L. & Sanes, D. H. Top-down modulation of sensory cortex gates perceptual learning. Proc. Natl Acad. Sci. USA 114, 9972–9977 (2017).

  32. 32.

    Slee, S. J. & David, S. V. Rapid task-related plasticity of spectrotemporal receptive fields in the auditory midbrain. J. Neurosci. 35, 13090–13102 (2015).

  33. 33.

    Bagur, S. et al. Go/no-go task engagement enhances population representation of target stimuli in primary auditory cortex. Nat. Commun. 9, 2529 (2018).

  34. 34.

    Bizley, J. K., Walker, K. M. M., Nodal, F. R., King, A. J. & Schnupp, J. W. H. Auditory cortex represents both pitch judgments and the corresponding acoustic cues. Curr. Biol. 23, 620–625 (2013).

  35. 35.

    Brosch, M., Selezneva, E. & Scheich, H. Nonauditory events of a behavioral procedure activate auditory cortex of highly trained monkeys. J. Neurosci. 25, 6797–6806 (2005).

  36. 36.

    Yin, P., Mishkin, M., Sutter, M. & Fritz, J. B. Early stages of melody processing: stimulus-sequence and task-dependent neuronal activity in monkey auditory cortical fields A1 and R. J. Neurophysiol. 100, 3009–3029 (2008).

  37. 37.

    Scheich, H. et al. Behavioral semantics of learning and crossmodal processing in auditory cortex: the semantic processor concept. Hear. Res. 271, 3–15 (2011).

  38. 38.

    Kelly, J. B., Judge, P. W. & Phillips, D. P. Representation of the cochlea in primary auditory cortex of the ferret (Mustela putorius). Hear. Res. 24, 111–115 (1986).

  39. 39.

    Kaas, J. H. & Hackett, T. A. Subdivisions of auditory cortex and processing streams in primates. Proc. Natl Acad. Sci. USA 97, 11793–11799 (2000).

  40. 40.

    Hackett, T. A. Information flow in the auditory cortical network. Hear. Res. 271, 133–146 (2011).

  41. 41.

    Hackett, T. A. et al. Feedforward and feedback projections of caudal belt and parabelt areas of auditory cortex: refining the hierarchical model. Front. Neurosci. 8, 72 (2014).

  42. 42.

    Plakke, B. & Romanski, L. M. Auditory connections and functions of prefrontal cortex. Front. Neurosci. 8, 199 (2014).

  43. 43.

    Camalier, C. R., D’Angelo, W. R., Sterbing-D’Angelo, S. J., de la Mothe, L. A. & Hackett, T. A. Neural latencies across auditory cortex of macaque support a dorsal stream supramodal timing advantage in primates. Proc. Natl Acad. Sci. USA 109, 18168–18173 (2012).

  44. 44.

    Kajikawa, Y. et al. Auditory properties in the parabelt regions of the superior temporal gyrus in the awake macaque monkey: an initial survey. J. Neurosci. 35, 4140–4150 (2015).

  45. 45.

    Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661–1663 (2014).

  46. 46.

    Kaas, J. H. The future of mapping sensory cortex in primates: three of many remaining issues. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 653–664 (2005).

  47. 47.

    Winer, J. A. & Lee, C. C. The distributed auditory cortex. Hear. Res. 229, 3–13 (2007).

  48. 48.

    Smith, E. et al. Seeing is believing: neural representations of visual stimuli in human auditory cortex correlate with illusory auditory perceptions. PLoS One 8, e73148 (2013).

  49. 49.

    Brincat, S. L., Siegel, M., von Nicolai, C. & Miller, E. K. Gradual progression from sensory to task-related processing in cerebral cortex. Proc. Natl Acad. Sci. USA 115, E7202–E7211 (2018).

  50. 50.

    Bizley, J. K. & Cohen, Y. E. The what, where and how of auditory-object perception. Nat. Rev. Neurosci. 14, 693–707 (2013).

  51. 51.

    Heffner, H. E. & Heffner, R. S. in Methods in Comparative Psychoacoustics (eds Klump, G. M. et al.) 79–93 (Birkhäuser, Basel, 1995).

  52. 52.

    Klein, D. J., Depireux, D. A., Simon, J. Z. & Shamma, S. A. Robust spectrotemporal reverse correlation for the auditory system: optimizing stimulus design. J. Comput. Neurosci. 9, 85–111 (2000).

  53. 53.

    Englitz, B., David, S. V., Sorenson, M. D. & Shamma, S. A. MANTA—an open-source, high density electrophysiology recording suite for MATLAB. Front. Neural Circuits 7, 69 (2013).

  54. 54.

    Rossant, C. et al. Spike sorting for large, dense electrode arrays. Nat. Neurosci. 19, 634–641 (2016).

  55. 55.

    Depireux, D. A., Simon, J. Z., Klein, D. J. & Shamma, S. A. Spectro-temporal response field characterization with dynamic ripples in ferret primary auditory cortex. J. Neurophysiol. 85, 1220–1234 (2001).

  56. 56.

    Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29, 1165–1188 (2001).

  57. 57.

    Efron, B. & Tibshirani, R. An Introduction to the Bootstrap (Chapman & Hall, Boca Raton, 1994).

  58. 58.

    Niwa, M., Johnson, J. S., O’Connor, K. N. & Sutter, M. L. Activity related to perceptual judgment and action in primary auditory cortex. J. Neurosci. 32, 3193–3210 (2012).

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Acknowledgements

We thank C. Bimbard, K. Dutta, and N. Joshi for their assistance with the neurophysiological recordings, A. Meredith and K. Dutta for their assistance with the neuroanatomical experiments, and L. Artemisia for support. This research was funded by grants from the US National Institutes of Health (grants R01-DC-005779 to S.A.S. and J.B.F. and R01-DC-014950 to S.V.D.) and by a DARPA grant (N660011724009) to J.B.F. D.D. held an AGAUR fellowship (Generalitat de Catalunya) in the frame of the EU COFUND Marie-Curie program (no. 2014BP-A00226) and an Erasmus Mundus ACN fellowship. D.E. held scholarships from CONICYT-PCHA/BECAS CHILE, Doctorado Convocatoria 2009-folio 72100839, Postdoctorado/Convocatoria 2016-folio 74170109, and Fulbright-Institute of International Education.

Author information

J.B.F., D.E., and S.A.S. designed the experiments. D.E., P.Y., D.D., and J.B.F. performed the experiments. D.E., S.V.D., and P.Y. analyzed the physiological results. S.R-S. analyzed the neuroanatomical results. D.E. prepared the physiological figures. S.R-S. and D.E. prepared the neuroanatomical figures. J.B.F., D.E., S.V.D., and S.A.S. wrote the manuscript.

Correspondence to Jonathan B. Fritz.

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Integrated supplementary information

Supplementary Figure 1 Depth of recordings in VPr.

Neurophysiological recordings in VPr were from four independently moveable tungsten electrodes that were slowly lowered through the dura and into the brain. As can be seen from the histogram plot above, most neuronal recordings in VPr were obtained from the first neurons encountered during each electrode penetration. After completion of the recording protocol at a given electrode depth, we lowered the electrodes >200 microns to ensure that we were recording from a new set of neurons. Hence, over 90% of our recordings were either from the first cortical neurons we encountered in our electrode penetrations or from neurons at a depth <500 below these first cells, thus likely in layers II, III or IV. INSET: This coronal section of the ferret brain (from a new Ferret Atlas21 by S. Radtke-Schuller) shows the 30° recording angle of our electrode penetrations into VPr (using an Alpha-Omega 4 electrode 2x2 array with ~500 microns lateral distance between adjacent electrodes which were independently moveable in depth).

Supplementary Figure 2 Examples of rapid task-adaptive changes in neuronal response timing, amplitude and selectivity.

Each row in this figure shows responses of separate, individual VPr neurons in three behavioral conditions: (i) pre-task passive or quiet listening, (ii) during behavioral performance of either the PT-D or the CLR-D discrimination tasks, (iii) post-task quiet listening. Blue shaded areas indicate the duration of Safe sounds; Red shaded areas show the duration of Warning sounds. Red vertical dashed lines indicate the duration of the behavioral response window. The responses of the two VPr neurons in A and B (in the PT-D task) change from relatively short onset (latency of 25–30 ms) and peak latency of 75 ms in the passive condition to a very different response pattern during behavior. As shown in the two middle panels, the initial onset response observed in the passive condition is nearly completely suppressed during behavior in both cells, and much longer latency responses emerge (the neuron in the top row has a new longer latency peak at 375 ms and the neuron in the second row has two new longer latency peaks at 175 and 525 ms in the active task condition). In both cells, the longer latency responses vanish again in the passive condition and the cells return to having one peak response at 75 ms in the post-passive condition. The responses of the two VPr neurons in C and D (in the CLR-D task) also illustrate behavioral state changes in timing (onset and duration), amplitude, and stimulus selectivity. During passive listening the cell in C responds to TORC onset and to both the Warning and Safe click trains. However, during behavior, the cell selectively responds only to the Warning click trains and stops responding to the TORC stimuli or to the Safe click trains. The pattern of responses to the Warning stimuli (fast click trains) also changes, now showing a buildup response during behavior. Similarly, the neuron in D responds to both click train rates during quiescent listening, but then, as in the cell above (C), it responds selectively to the Warning stimulus during behavior. What makes the neuron in D even more interesting, is that the response timing to the Warning stimulus, shifts from Warning stimulus to sound offset – marking the timing of the shock window. In both cells (C and D), responses return to the original pre-passive form in the post-passive condition.

Supplementary Figure 3 Examples of behaviorally gated responses in VPr.

Raster plots and PSTHs (mean firing rate ± SEM) of three VPr neurons. Blue shaded areas indicate the duration of Safe sounds; Red shaded areas show the duration of Warning sounds. Green vertical dashed lines indicate sound onset and offset. Gray shaded areas between red vertical dashed lines indicate the duration of the behavioral response window. In the three VPr cells above, there were no responses to task stimuli during quiescent listening conditions. However, when the animal engaged in behavior (tone detection (PT-D) in all three examples), the single units showed clear and striking responses to the task-relevant Warning stimulus (tone) in a manner similar to behaviorally gated responses previously described in frontal cortex18. (A) Task-gated sustained response, with a latency of about 150 ms, that lasted throughout and well beyond the post-stimulus shock period. No persistent post-passive effects. (B) Very long latency (1.8 sec) sustained response that slightly anticipated tone offset and continued throughout and beyond the post-stimulus shock period. Very short latency responses to Safe and Warning stimuli in the post-passive. (C) Task-gated rapid onset and sustained inhibitory response that lasted throughout and well beyond stimulus duration period into the post-stimulus period and up into the response (shock) window 400–800 ms after stimulus offset.

Supplementary Figure 4 Rates of click trains used in click rate discrimination task.

Four ferrets were trained in the click rate discrimination task (CLR-D). Two ferrets were trained to discriminate high rate Warning click trains (16–48 Hz) from low Safe click trains (4–24 Hz), and two ferrets in the opposite rate discrimination direction (low rate Warning and high rate Safe click trains). The slightly overlapping distribution of click rates for the two classes of high and low rates is shown in Panel A. The minimum rate difference between Safe and Warning click trains was 7 Hz, while the maximum was 32 Hz. The distribution of high-to-low click rate ratios used in every behavioral block is shown in Panel B. The mean (± SD) ratio used was 3.2 ± 1.09. Panel C: In the passive condition, we measured best click rates for neurons in A1 (N=92), dorsal PEG (N=182), and VPr (N=478) that were presented with click trains with rates of 4–60 Hz. These data include responses from neurons in both naïve and trained animals, and clearly indicate that neurons at multiple levels of auditory cortex (A1, dorsal PEG and VPr) respond to a wide range of click rates.

Supplementary Figure 5 Comparison of task-related responses across tasks.

(A) Venn diagram of 367 VPr neurons that had significant behavior-dependent changes in their responses in the PT-D task (n=251 neurons, green), CLR-D task (n=266 neurons, purple) and in both (150 neurons comprising 150/367 = ~41% of cells). Significance of behavioral effects within cells was measured by performing a two-sided Wilcoxon sign rank test, corrected for multiple comparisons using false discovery rate, between passive and active PSTHs of each sound class (See Methods). (B,C) Example VPr raster plots and PSTHs (mean firing rate ± SEM) are shown from neurons that exhibited similar responses in both tasks, suggesting that they were encoding behavioral meaning of the stimuli, rather than their particular spectral-temporal acoustic properties. Blue shaded areas indicate the duration of Safe sounds; Red shaded areas show the duration of Warning sounds. Green vertical dashed lines indicate the onset and offset of task sounds, bottom panels in B,C also show a dashed line indicating the TORC offset/click train onset. Gray shaded areas between red vertical dashed lines indicate the duration of the behavioral response window.

Supplementary Figure 6 Effects of regressing out lick-predicted spikes from VPr population response.

Normalized PSTH responses are shown as mean firing rate ± SEM. Gray vertical lines indicate sound duration, red vertical lines show the duration of the behavioral response window. In order to test for an effect of lick-related spiking activity, we reverse-correlated licks with spiking activity and found that 37% of VPr neurons (N=93/251 neurons tested in the PT-D task) had a significant motor component in their activity. However, removing these lick-predicted spikes did not cause a significant change in the firing rate of the population PSTH (one-way ANOVA, F=0.43, p=0.5122).

Supplementary Figure 7 Differences between responses in naive and trained animals.

We resampled the distribution of responses in the naïve and trained animals one thousand times and computed the likelihood of observing differences between responses of naïve and trained animals, if the units are randomly assigned. The P values represent the likelihood of observing the naïve mean response in the resampled distribution of trained ΔnFR(W-S) (n= 1000 resamples corresponding to the number of naïve neurons in each area, see Figure 6). The results and the p-values clearly indicate that the two distributions are statistically distinct.

Supplementary Figure 8 VPr neurons in trained animals display differential responses to sounds depending upon their behavioral context.

Responses are shown as mean normalized firing rate (mean ± SEM). Vertical gray dashed lines indicate sound onset. The set of 30 different TORCs were presented in three different behavioral contexts: (1) Task-irrelevant TORCs (left column, gray), were presented in series of 150 sound presentations (5 complete sets of TORCs) to assess STRF tuning and were not associated with any reward or punishment (A1 n=48, dorsal PEG n=189, VPr n=237, dlFC n=37 neurons), (2) Neutral TORCs (middle column, purple) preceded Safe and Warning click trains in the CLR-D task (A1 n=57, dorsal PEG n=60, VPr n=266, dlFC n=38 neurons). These neutral TORC stimuli did not provide information about the identity (rate) of the upcoming click train and hence did not cue a correct behavioral response. However, since they were presented with a fixed duration, the TORCs did provide timing information about the imminent time of onset of the upcoming highly relevant Safe and Warning click trains, (3) Safe TORCs (right column, blue) were presented in the PT-D task and had to be actively discriminated from Warning tones (A1 n=71, dorsal PEG n=199, VPr n=251, dlFC n=138 neurons). In the middle and right columns, responses recorded in the passive and active (behavior) conditions are shown as dashed and continuous lines, respectively. There is an onset response to TORCs in all three auditory areas in the task-irrelevant context (left column). The rest of the TORC response is different in the three cortical areas –the onset response is followed by a sustained response in A1, a rapid return to spontaneous baseline in dorsal PEG and distinctively, a deep inhibitory response in VPr. This inhibitory response to TORCs in the behaviorally irrelevant context is characteristic for VPr and is seen also in the passive presentations of the task stimuli. A similar response profile for the TORCs for all cortical areas is observed in the behavioral context of the PT-D task where the TORCs are the Safe stimuli (right column). In the PT-D task, VPr neurons became less suppressed during the presentation of Safe TORCs in the active compared to the passive state (solid blue). However, in the “anticipatory neutral” context of the CLR-D task, there is a striking qualitative difference in responses between cortical areas. In the CLR-D task, while TORCs carry no information about the identity of the upcoming click train, they do convey timing information since these are presented for a fixed duration before the click train (see also Figure 4b). As seen (middle column), in dorsal PEG, in VPr, and also in dlFC, the response to the TORCs in the active CLR-D task is a sustained increase in firing rate and an anticipatory “build-up” type response in VPr and dlFC.

Supplementary Figure 9 Box plots of behavior-dependent changes in normalized postwarning responses.

in PT-D (green) and CLR-D (purple) tasks. In the abscissa, data is grouped by cortical fields A1 (PT-D n=71, CLR-D n=57 neurons), dorsal PEG (PT-D n=199, CLR-D n=60 neurons), VPr (PT-D n=251, CLR-D n=266 neurons) and dlFC (PT-D n=138, CLR-D n=38 neurons). The boxes show the inter-quartile range between the 25th and 75th percentiles, each box middle line is the median and the notch indicates the 95% confidence interval of the median. The ‘whiskers’ extending above and below the boxes show the limits of the remaining percentiles and dots show outliers. Both VPr and dlFC display a significant increase in their responses after Warning sound offset during this silent 650 ms time window. (PT-D: chi-square=13.4, p=0.0052, df=3; CLR-D: chi-square=40.947, p=6.7x10−9, df=3; Kruskal-Wallis test). Asterisks and lines above show Tukey’s HSD post-hoc pair-wise difference significance between higher-order areas VPr and dlFC with A1 and dorsal PEG (PT-D: A1/VPr p=0.0072, dorsal PEG/VPr p=0, A1/DLFC p=0.008, dorsal PEG/dlFC p=0; CLR-D: A1/VPr p=0.0072, dorsal PEG/VPr p=0.3336, A1/dlFC p=0.0433, dorsal PEG/dlFC p=0.4292).

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Fig. 1: Behavioral task design and location of neurophysiological recordings.
Fig. 2: Comparison of the tuning properties of the A1, dorsal PEG, and VPr areas.
Fig. 3: VPr neurons enhance the contrast between responses to safe and warning stimuli during behavior.
Fig. 4: Average responses to warning and safe stimuli from the A1, dorsal PEG, VPr, and dlFC areas.
Fig. 5: Comparison of contrast between safe and warning sounds in naïve and trained animals in three different auditory cortical areas.
Fig. 6: Distributions of warning–safe response contrast (ΔnFR(W-S)) data recorded from A1, dorsal PEG, VPr, and dlFC neurons.
Fig. 7: VPr and dlFC neurons display a substantial and sustained response during the silent period following warning sound presentation during task performance.
Fig. 8: Summary of progression of task-related population responses to the warning stimulus along the auditory processing hierarchy in the PT-D task.
Supplementary Figure 1: Depth of recordings in VPr.
Supplementary Figure 2: Examples of rapid task-adaptive changes in neuronal response timing, amplitude and selectivity.
Supplementary Figure 3: Examples of behaviorally gated responses in VPr.
Supplementary Figure 4: Rates of click trains used in click rate discrimination task.
Supplementary Figure 5: Comparison of task-related responses across tasks.
Supplementary Figure 6: Effects of regressing out lick-predicted spikes from VPr population response.
Supplementary Figure 7: Differences between responses in naive and trained animals.
Supplementary Figure 8: VPr neurons in trained animals display differential responses to sounds depending upon their behavioral context.
Supplementary Figure 9: Box plots of behavior-dependent changes in normalized postwarning responses.