Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Secondary auditory cortex mediates a sensorimotor mechanism for action timing

Abstract

The ability to accurately determine when to perform an action is a fundamental brain function and vital to adaptive behavior. The behavioral mechanism and neural circuit for action timing, however, remain largely unknown. Using a new, self-paced action timing task in mice, we found that deprivation of auditory, but not somatosensory or visual input, disrupts learned action timing. The hearing effect was dependent on the auditory feedback derived from the animal’s own actions, rather than passive environmental cues. Neuronal activity in the secondary auditory cortex was found to be both correlated with and necessary for the proper execution of learned action timing. Closed-loop, action-dependent optogenetic stimulation of the specific task-related neuronal population within the secondary auditory cortex rescued the key features of learned action timing under auditory deprivation. These results unveil a previously underappreciated sensorimotor mechanism in which the secondary auditory cortex transduces self-generated audiomotor feedback to control action timing.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Self-paced fixed-interval timing task in mice.
Fig. 2: Auditory deprivation acutely disrupts self-paced fixed-interval performance.
Fig. 3: Learning- and performance-dependent dorsal secondary auditory cortex cFos activation during self-paced fixed-interval task.
Fig. 4: Lever-pressing-related neuronal activity in dorsal secondary auditory cortex during the performance of self-paced fixed-interval task in trained mice.
Fig. 5: Dorsal secondary auditory cortex activation is necessary in providing the sensorimotor feedback for action timing.
Fig. 6: Press-dependent dorsal secondary auditory cortex activation is sufficient in providing the sensorimotor feedback for action timing.
Fig. 7: A computational model of action timing based on the integration of actions regulated by sensorimotor feedback.
Fig. 8: Dorsal secondary auditory cortex provides sensorimotor feedback during action timing via layer V active populations.

Data availability

All data are available upon reasonable request from the corresponding author.

Code availability

All code is available upon request.

References

  1. Gallistel, C. R. The organization of action: a new synthesis. Behav. Brain Sci. 4, 609–619 (1981).

    Google Scholar 

  2. Jin, X. & Costa, R. M. Start/stop signals emerge in nigrostriatal circuits during sequence learning. Nature 466, 457–462 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Prochazka, A. & Ellaway, P. Sensory systems in the control of movement. Compr. Physiol. 2, 2615–2627 (2012).

    PubMed  Google Scholar 

  4. Gossard, J. P., Cabelguen, J. M. & Rossignol, S. An intracellular study of muscle primary afferents during fictive locomotion in the cat. J. Neurophys. 65, 914–926 (1991).

    CAS  Google Scholar 

  5. Rossignol, S., Dubuc, R. & Gossard, J. P. Dynamic sensorimotor interactions in locomotion. Physiol. Rev. 86, 89–154 (2006).

    PubMed  Google Scholar 

  6. Zehr, E. P. & Stein, R. B. What functions do reflexes serve during human locomotion? Prog. Neurobiol. 58, 185–205 (1999).

    CAS  PubMed  Google Scholar 

  7. Guenthner, C. J., Miyamichi, K., Yang, H. H., Heller, H. C. & Luo, L. Permanent genetic access to transiently active neurons via TRAP: targeted recombination in active populations. Neuron 78, 773–784 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Abner, R. T., Edwards, T. & Douglas, A. Pharmacology of temporal cognition in two mouse strains. Int. J. Comp. Psychol. 14, 189–210 (2001).

    Google Scholar 

  9. Cheng, K. & Westwood, R. Analysis of single trials in pigeons’ timing performance. J. Exp. Psychol. Anim. Behav. Process. 19, 56–67 (1993).

    Google Scholar 

  10. Church, R. M., Meck, W. H. & Gibbon, J. Application of scalar timing theory to individual trials. J. Exp. Psychol. Anim. Behav. Process. 20, 135–155 (1994).

    CAS  PubMed  Google Scholar 

  11. Gallistel, C. R., King, A. & McDonald, R. Sources of variability and systematic error in mouse timing behavior. J. Exp. Psychol. Anim. Behav. Process. 30, 3–16 (2004).

    CAS  PubMed  Google Scholar 

  12. Koch, S. C. et al. RORβ spinal interneurons gate sensory transmission during locomotion to secure a fluid walking gait. Neuron 96, 1419–1431 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Schmucker, C., Seeliger, M., Humphries, P., Biel, M. & Schaeffel, F. Grating acuity at different luminances in wild-type mice and in mice lacking rod or cone function. Invest. Ophthalmol. Vis. Sci. 46, 398–407 (2005).

    PubMed  Google Scholar 

  14. Crawley, J. N. et al. Behavioral phenotypes of inbred mouse strains: implications and recommendations for molecular studies. Psychopharmacology 132, 107–124 (1997).

    CAS  PubMed  Google Scholar 

  15. Eliades, S. J. & Wang, X. Neural substrates of vocalization feedback monitoring in primate auditory cortex. Nature 453, 1102–1106 (2008).

    CAS  PubMed  Google Scholar 

  16. Schneider, D. M., Sundararajan, J. & Mooney, R. A cortical filter that learns to suppress the acoustic consequences of movement. Nature 561, 391–395 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Geddes, C. E., Li, H. & Jin, X. Optogenetic editing reveals the hierarchical organization of learned action sequences. Cell 174, 32–43 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Howard, C. D., Li, H., Geddes, C. E. & Jin, X. Dynamic nigrostriatal dopamine biases action selection. Neuron 93, 1436–1450 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Buhusi, C. V. & Meck, W. H. What makes us tick? functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6, 755–765 (2005).

    CAS  PubMed  Google Scholar 

  20. Gibbon, J., Church, R. M. & Meck, W. H. Scalar timing in memory. Ann. N. Y. Acad. Sci. 423, 52–77 (1984).

    CAS  PubMed  Google Scholar 

  21. Kawashima, T. et al. Functional labeling of neurons and their projections using the synthetic activity–dependent promoter E-SARE. Nat. Meth. 10, 889–895 (2013).

    CAS  Google Scholar 

  22. Znamenskiy, P. & Zador, A. M. Corticostriatal neurons in auditory cortex drive decisions during auditory discrimination. Nature 497, 482–485 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Tervo, D. G. R. et al. A designer AAV variant permits efficient retrograde access to projection neurons. Neuron 92, 372–382 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Skinner, B. F. ‘Superstition’ in the pigeon. J. Exp. Psychol. 38, 168–172 (1948).

    CAS  PubMed  Google Scholar 

  25. Mello, G. B. M., Soares, S. & Paton, J. J. A scalable population code for time in the striatum. Curr. Biol. 25, 1113–1122 (2015).

    CAS  PubMed  Google Scholar 

  26. Auerbach, B. D., Rodrigues, P. V. & Salvi, R. J. Central gain control in tinnitus and hyperacusis. Front. Neurol. 5, 206 (2014).

    PubMed  PubMed Central  Google Scholar 

  27. Caspary, D. M., Ling, L., Turner, J. G. & Hughes, L. F. Inhibitory neurotransmission, plasticity and aging in the mammalian central auditory system. J. Exp. Biol. 211, 1781–1791 (2008).

    CAS  PubMed  Google Scholar 

  28. Tollin, D. J. The lateral superior olive: a functional role in sound source localization. Neuroscientist 9, 127–143 (2003).

    PubMed  Google Scholar 

  29. Malmierca, M. S., Merchán, M. A., Henkel, C. K. & Oliver, D. L. Direct projections from cochlear nuclear complex to auditory thalamus in the rat. J. Neurosci. 22, 10891–10897 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Schofield, B. R., Motts, S. D., Mellott, J. G. & Foster, N. L. Projections from the dorsal and ventral cochlear nuclei to the medial geniculate body. Front. Neuroanat. 8, 10 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. Huang, C. L. & Winer, J. A. Auditory thalamocortical projections in the cat: laminar and areal patterns of input. J. Comp. Neurol. 427, 302–331 (2000).

    CAS  PubMed  Google Scholar 

  32. Lee, C. C. Exploring functions for the non-lemniscal auditory thalamus. Front. Neural Circuits 9, 69 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. Smith, P. H., Uhlrich, D. J., Manning, K. A. & Banks, M. I. Thalamocortical projections to rat auditory cortex from the ventral and dorsal divisions of the medial geniculate nucleus. J. Comp. Neurol. 520, 34–51 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Otazu, G. H., Tai, L.-H., Yang, Y. & Zador, A. M. Engaging in an auditory task suppresses responses in auditory cortex. Nat. Neurosci. 12, 646–654 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Buonomano, D. V. Temporal information transformed into a spatial code by a neural network with realistic properties. Science 267, 1028–1030 (1995).

    CAS  PubMed  Google Scholar 

  36. Buonomano, D. V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009).

    CAS  PubMed  Google Scholar 

  37. Goldman, M. S. Memory without feedback in a neural network. Neuron 61, 621–634 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Laje, R. Robust timing and motor patterns by taming chaos in recurrent neural networks. Nat. Neurosci. 16, 925–933 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Liu, J. K. & Buonomano, D. V. Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner. J. Neurosci. 29, 13172–13181 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Miller, A. & Jin, D. Z. Potentiation decay of synapses and length distributions of synfire chains self-organized in recurrent neural networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 88, 062716 (2013).

    PubMed  Google Scholar 

  41. Namboodiri, V. M. K., Huertas, M. A., Monk, K. J., Shouval, H. Z. & Shuler, M. G. H. Visually cued action timing in the primary visual cortex. Neuron 86, 319–330 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Shuler, M. G. Reward timing in the primary visual cortex. Science 311, 1606–1609 (2006).

    CAS  PubMed  Google Scholar 

  43. Goodfellow, L. D. An empirical comparison of audition, vision, and touch in the discrimination of short intervals of time. Am. J. Psychol. 46, 243–258 (1934).

    Google Scholar 

  44. Kubovy, M. Should we resist the seductiveness of the space:time::vision:audition analogy? J. Exp. Psychol. Hum. Percept. Perform. 14, 318–320 (1988).

    Google Scholar 

  45. Repp, B. H. & Penel, A. Rhythmic movement is attracted more strongly to auditory than to visual rhythms. Psychol. Res. 68, 252–270 (2003).

    PubMed  Google Scholar 

  46. Brosch, M. & Schreiner, C. E. Time course of forward masking tuning curves in cat primary auditory cortex. J. Neurophys. 77, 923–943 (1997).

    CAS  Google Scholar 

  47. He, J., Hashikawa, T., Ojima, H. & Kinouchi, Y. Temporal integration and duration tuning in the dorsal zone of cat auditory cortex. J. Neurosci. 17, 2615–2625 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Welch, R. B., DuttonHurt, L. D. & Warren, D. H. Contributions of audition and vision to temporal rate perception. Percept. Psychophys. 39, 294–300 (1986).

    CAS  PubMed  Google Scholar 

  49. Zhou, X., de Villers-Sidani, E. & Panizzutti, R. Successive-signal biasing for a learned sound sequence. Proc. Natl Acad. Sci. USA 107, 14839–14844 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Nobre, A. C. & van Ede, F. Anticipated moments: temporal structure in attention. Nat. Rev. Neurosci. 19, 34–48 (2018).

    CAS  PubMed  Google Scholar 

  51. Pizzera, A. & Hohmann, T. Acoustic information during motor control and action perception: a review. Open Psychol. J. 8, 183–191 (2015).

    Google Scholar 

  52. Repp, B. H. & Penel, A. Auditory dominance in temporal processing: new evidence from synchronization with simultaneous visual and auditory sequences. J. Exp. Psychol. Hum. Percept. Proc. 28, 1085–1099 (2002).

    Google Scholar 

  53. Sacco, T. & Sacchetti, B. Role of secondary sensory cortices in emotional memory storage and retrieval in rats. Science 329, 649–656 (2010).

    CAS  PubMed  Google Scholar 

  54. Tsukano, H. et al. Reciprocal connectivity between secondary auditory cortical field and amygdala in mice. Sci. Rep. 9, 19610 (2019).

  55. Issa, J. B. et al. Multiscale optical Ca2+ imaging of tonal organization in mouse auditory cortex. Neuron 83, 944–959 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Duque, D., Ayala, Y. A. & Malmierca, M. S. Deviance detection in auditory subcortical structures: what can we learn from neurochemistry and neural connectivity? Cell Tissue Res. 361, 215–232 (2015).

    PubMed  Google Scholar 

  57. Kraus, N. et al. Discrimination of speech‐like contrasts in the auditory thalamus and cortex. J. Acoust. Soc. Am. 96, 2758–2768 (1998).

    Google Scholar 

  58. Malmierca, M. S., Cristaudo, S., Pérez-González, D. & Covey, E. Stimulus-specific adaptation in the inferior colliculus of the anesthetized rat. J. Neurosci. 29, 5483–5493 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kelly, J. B. The effects of insular and temporal lesions in cats on two types of auditory pattern discrimination. Brain Res. 62, 71–87 (1973).

    CAS  PubMed  Google Scholar 

  60. Layton, L. S., Toga, A. W., Horenstein, S. & Davenport, D. G. Temporal pattern discrimination survives simultaneous bilateral ablation of suprasylvian cortex but not sequential bilateral ablation of insular-temporal cortex in the cat. Brain Res. 173, 337–340 (1979).

    CAS  PubMed  Google Scholar 

  61. Meck, W. H. Neuroanatomical localization of an internal clock: a functional link between mesolimbic, nigrostriatal, and mesocortical dopaminergic systems. Brain Res. 1109, 93–107 (2006).

    CAS  PubMed  Google Scholar 

  62. Soares, S., Atallah, B. & Paton, J. J. Midbrain dopamine neurons control judgement of time. Science 354, 1273–1277 (2016).

    CAS  PubMed  Google Scholar 

  63. Drew, M. R. et al. Transient overexpression of striatal D2 receptors impairs operant motivation and interval timing. J. Neurosci. 27, 7731–7739 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Ward, D. et al. Impaired timing precision produced by striatal D2 receptor overexpression is mediated by cognitive and motivational deficits. Behav. Neurosci. 123, 720–730 (2009).

    PubMed  PubMed Central  Google Scholar 

  65. Murray, J. M. & Escola, G. S. Learning multiple variable-speed sequences in striatum via cortical tutoring. eLife 6, e26084 (2017).

    PubMed  PubMed Central  Google Scholar 

  66. Chaplan, S. R., Bach, F. W., Pogrel, J. W., Chung, J. M. & Yaksh, T. L. Quantitative assessment of tactile allodynia in the rat paw. J. Neurosci. Methods 53, 55–63 (1994).

    CAS  PubMed  Google Scholar 

  67. Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100–1114 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Lewis, M. C. & Gould, T. J. Reversible inactivation of the entorhinal cortex disrupts the establishment and expression of latent inhibition of cued fear conditioning in C57BL/6 mice. Hippocampus 17, 462–470 (2007).

    PubMed  Google Scholar 

  69. Du, J., Blanche, T. J., Harrison, R. R. & Lester, H. A. Multiplexed, high density electrophysiology with nanofabricated neural probes. PLoS ONE 6, e26204 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Stringer, C. et al. Inhibitory control of correlated intrinsic variability in cortical networks. eLife 5, e19695 (2016).

    PubMed  PubMed Central  Google Scholar 

  71. Dayan, P. & Abbott, L. F. Theoretical neuroscience: computational and mathematical modeling of neural systems (MIT Press, 2001).

  72. Aravanis, A. M. et al. An optical neural interface: in vivo control of rodent motor cortex with integrated fiberoptic and optogenetic technology. J. Neural Eng. 4, 143–156 (2007).

    Google Scholar 

  73. Keller, D., Erö, C. & Markram, H. Cell densities in the mouse brain: a systematic review. Front. Neuroanat. 12, 83 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Klug, J. R. et al. Differential inputs to striatal cholinergic and parvalbumin interneurons imply functional distinctions. eLife 7, e35657 (2018).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank H. Bito for the ESARE viral construct, B. Sabatini for the DO-ChR2 viral construct, and M. Goulding, C. Kintner and J. Thomas for helpful discussion. This study was supported by grants from the National Institutes of Health under award numbers R01NS083815 and R01AG047669 to X.J., and EY022577 to E.M.C. and the McKnight Memory and Cognitive Disorders Award to X.J.

Author information

Authors and Affiliations

Authors

Contributions

X.J. conceived the behavior task. X.J. and J.R.C. conceived the sensorimotor hypothesis and designed the experiments. J.R.C., B.N. and P.M. conducted the behavioral experiments. J.R.C. and B.N. performed surgeries, histology and immunohistochemistry. J.R.C., P.M. and M.E. performed cell counting. J.R.C. and P.S. analyzed the behavioral data. H.L. and H.-H.H. performed the in vivo electrophysiological experiments and analyses in freely behaving mice during the performance of the SFI task. M.A.K. and E.M.C. performed the electrophysiological experiments and analyses in head-fixed FosTRAP mice. H.-H.H. performed the optogenetic experiments in Vgat-Ai32 mice. X.J. built the computational model and wrote the scripts. J.R.C. and X.J. wrote the manuscript.

Corresponding author

Correspondence to Xin Jin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Henry Yin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Self-initiated learning and performance of SFI peak timing is characterized notably by half peak PETH fall time, which reflects individual step-like lever press responding, related to Figs. 1 and 2.

a, Correlation analysis of average response rate (n = 10) at 30 s versus 70 s based on PETH for omission trials across training. b, The Pearson correlation coefficient can be calculated for the average response rate at each second over the omission trial window versus the average response rate at 30 s across learning showing autocorrelation around 30 s and a cross-temporal period of correlation around 70 s. The arrow denotes the Pearson correlation coefficient for the correlation in (a). c-d, Trial-by-trial analysis for the number of presses from 20–30 s and 60–70 s confirms the relationship between response rate changes at 30 s and 70 s from PETHs in (a). c, The response raster of an exemplar for the 5th trial of training days 1, 4, 7 and 21 shows that as the number of responses increase around 30 s, the number of responses around 70 s decreases. d, The numbers of responses over 20–30 s for the same exemplar in (c) negatively correlates with the number of responses over 60–70 s across learning for the 5th trial (top). The average number of presses for all animals (n = 10) for the 5th trial over 20–30 s negatively correlates with the average number of presses over 60–70 s (bottom). e-g, Response rasters (top), response rate PETH (middle), and percent maximum response rate PETH (bottom) of exemplars for rewarded (left) and omission trials (right) performing regular SFI (70% rewarded trials) on day 21 after being trained 20 days on either SFI with 70% (e) or 100% (f) rewarded trials. g-i, The latency to initiate post-lever extension does not change across training days (g), and does not show a relationship to half peak fall time (h) or peak time (i) at day 21 of training (n = 10). j, Alignment to the start and stop of pressing bouts reveals that peak timing is an artifact of averaging many trials. Bouts of pressing for an individual animal can be defined based on the trial-by-trial press rate to establish the start and stop (left, top, red hash marks) of pressing sequences that give rise to the overall PETH, which can be used to define the PETH peak time and half peak rise and fall time (left, bottom). Alignment of pressing bouts to the start (middle, top) and stop (right, top) reveals that PETHs resemble step functions (middle and right, bottom), indicating that trial-by-trial behavior is in fact characterized by low rates of responding early on, followed by an abrupt switch to a constant high rate, and then an abrupt return to no responding. k, Correlating PETH half peak fall time versus mean trial-by-trial stop time at day 21 of training reveals that the half peak fall time metric can serve as an accurate measure of overall individual trial stop times. l, Same as (k) but for PETH half peak rise time versus trial-by-trial start time. m-n, The PETH half peak rise time changes minimally across 21 days of training (m, main effect of treatment F(4, 36) = 11.54, P < 0.0001; day 1 vs. day 21, P< 0.0001), compared to half peak fall time (see Fig. 1), and shows nonsignificant changes with ear sealing (n). o-p, Analysis of interpress interval times between individual trial start and stop pressing times. The histogram of all interpress interval times between the start and stop times for all trials for the exemplar shown in (j) follows a Poisson-like distribution. (p) Autocorrelation function coefficients calculated for the sequence of interpress intervals across the second (top) and 10th (bottom) trials for increasing lag times, again for the exemplar shown in (j). Grey, non-labeled points denote intermediate training days for learning correlation plots. Error bars denote s.e.m. For correlation plots analyzing latency to initiate post-lever extension, and trial-by-trial mean stop time, grey points denote individual animals. Grey shading for all correlation plots denote 95% confidence interval for regression and PCC denotes Pearson correlation coefficient. **** P < 0.0001; NS, not significant. Blue dotted lines for autocorrelation functions denote rejection region bands for testing individual autocorrelations.

Extended Data Fig. 2 SFI ear seal learning, related to main Fig. 2.

a, Behavior of an example mouse for probe trials at day 1, 4, 7 and 21 of SFI ear seal training. b, Average training PETHs for ears open (left, n = 10) and ear sealed (right, n = 8) for response rate (top) and percent maximum response rate (bottom). c-e, The presses per minute at 30 s (c, effect of interaction F(4, 64) = 4.55, P = 0.0027; ears open vs. sealed: day 7, day 14, and day 21, P = 0.0009, P = 0.0008, and P = 0.0004, respectively), peak time (d), and half peak fall time (e, effect of interaction F(4, 64) = 3.43, P = 0.0133; ears open vs. sealed: day 4 and day 7, P < 0.0001 and P = 0.0139, respectively) across training days for ears open (n = 10) and sealed (n = 8). Learning data were analyzed using two-way ANOVA followed by Sidak post hoc comparisons. Values for performance metrics are means. Shading for average PETHs and error bars denote s.e.m. **** P < 0.0001, *** P < 0.001, * P < 0.05; NS, not significant.

Extended Data Fig. 3 Auditory deprivation effects on SFI performance are independent of any cues related to reward delivery, related to main Fig. 2.

a-e, Sweetened, condensed milk (n = 5) can be used as a reward for SFI training. a, Response rasters (top), response rate PETH (middle) and percent maximum response rate PETH (bottom) of an exemplar for omission trials performing SFI under auditory deprivation (ears sealed) between flanking control sessions (pre-control and post-control) using sweetened, condensed milk as a reward. SFI performance with milk reward can be measured by the response rate at 10 s (pre-control middle) and the half peak fall time (pre-control bottom) for omission trials. Ear sealing has no effect on the rewarded head entry time (b), or how often animals check for the milk reward by making a head entry (c), indicating auditory deprivation does not have an effect on a sensory cue related to reward availability. d, Auditory deprivation experiments on animals trained using the milk reward showed similar effects on response rate (left, main effect of treatment F(2,8) = 9.209, P = 0.0084; ears sealed versus pre-/post-control, P = 0.0115 and P = 0.0191, respectively) and half peak fall time (right, main effect of treatment F(2,8) = 9.414, P = 0.0079; ears sealed versus pre-/post-control, P = 0.0107 and P = 0.0183, respectively) as with the pellet reward. e, Average PETHs for response rate (top) and percent maximum response rate (bottom) for performance with sweetened, condensed milk reward on pre-control session and ear seal session. f-h, Separate groups of animals underwent extinction through exposure to continuous omission trials with ears open (n = 8) or sealed (n = 10) also demonstrating that the auditory deprivation effects on SFI performance were independent of a reward-related cue. f, Under auditory deprivation during extinction, response rate at 30 s decreased (left, two-tailed, unpaired t-test, t = 5.076, P = 0.0001), half peak fall time increased (right, two-tailed, unpaired t-test, t = 3.347, P = 0.0041), and no significant change was observed in the peak time. g, Response rasters (top), response rate PETH (middle), and percent maximum response rate PETH (bottom) of exemplars for omission trials performing extinction post-21 days of training on SFI with ears open (left) and sealed (right). h, Average PETHs for response rate (top) and percent maximum response rate (bottom) for ears sealed and open groups. SFI milk data were analyzed using repeated-measures one-way ANOVA followed by Tukey post hoc comparisons. Extinction results were analyzed using unpaired t-tests. Values for performance metrics are means, and error bars and shading for average PETHs denote s.e.m. ***P < 0.001; ** P< 0.01; *P< 0.05; NS, not significant.

Extended Data Fig. 4 Effects of unilateral auditory deprivation on cFos expression in auditory structures and striatum, related to main Fig. 3.

a, cFos expression in AUDd (left), ipsilateral to the sealed ear (middle), and ipsilateral to the open ear (right) of a unilateral ear sealed animal. (b-d), same as (a), but for the medial geniculate (b), inferior colliculus (c), and striatum (d). Scale bars for immunohistochemical images denote 200 μm for auditory structures, and 1 mm for striatum. ‘D’ and ‘L’ denote dorsal and lateral, respectively.

Extended Data Fig. 5 Effects of unilateral auditory deprivation on the visual system activation, and validation of FosTRAP labeling system in AUDd, related to main Figs. 3 and 4.

a-b, Random unilateral ear sealing does not disrupt cFos expression across hemispheres in VISp. a, cFos expression across hemispheres in VISp was quantified in the same group of animals that underwent random unilateral ear sealing while performing SFI and then sacrificed. b, cFos immunohistochemistry for exemplar showing VISp cortical region for an animal sacrificed on day 21 of SFI training with one ear sealed (left) and one ear open (middle). Scale bars denote 50 μm. Comparison of percent activation according to cFos counts across hemispheres for VISp cortical region (right) ipsilateral versus contralateral to the sealed ear of animals sacrificed upon completion of SFI at day 21 (n = 5) of training. c-g, FosTRAP expression recapitulates cFos protein expression pattern in AUDd. c, FosTRAP animals were trained for 21 days on SFI and split into two groups. One group (top)(n = 4) was induced with 4-OHT after being injected with a Cre-dependent AAV expressing GFP in AUDd and eventually sacrificed immediately after performing SFI again with ears open. The other group (bottom)(n = 4) performed the no lever version of the task also with ears open and immediately sacrificed following session completion. d, Percent distribution across the cortical layers in AUDd of cFos protein and cFosCreERT2 (as visualized via Cre-dependent GFP expression) in FosTRAP animals that were induced (green) while performing SFI with their ears open and later in another SFI session immediately sacrificed upon completion (black), again with ears open. These distributions were compared to the cFos protein percent distribution of FosTRAP animals that were sacrificed upon session completion in the no lever context with their ears open (red)(effect of interaction F(8,45) = 2.335, P = 0.0343; no lever layer V cFos protein vs. SFI layer V cFosCreERT2/cFos protein, P = 0.0172 and P = 0.0496, respectively) with ears open (n = 4). e-f, AUDd cortical layer expression pattern of endogenous cFos protein for FosTRAP animals sacrificed immediately after performing SFI (e) and being in the no lever context (f) with their ears open. g, FosTRAP cFosCreERT2 expression as visualized via Cre-dependent GFP expression in AUDd of an animal induced with its ears open while performing SFI. Scale bars denote 100 μm. h, Fiber placement in AUDd of FosTRAP animals injected with an AAV expressing Cre-dependent ChR2-EYFP and induced with its ears open while performing SFI. Scale bar denotes 500 μm. For VISp cFos expression analysis, data were analyzed using a paired t-test. Bars denote mean percentage across hemispheres. For FosTRAP validation and AUDd laminar analysis, data were analyzed using two-way ANOVA followed by Sidak post hoc comparisons. Values are mean percentages across layers. Error bars denote s.e.m. For all immunohistochemistry images, ‘D’ and ‘L’ denote dorsal and lateral, respectively. *P < 0.05; NS, not significant.

Extended Data Fig. 6 Firing properties of AUDd neurons with ears open versus sealed and photoactivation of AUDd FosTRAPed populations, related to main Figs. 4 and 6.

a, Baseline firing rates of AUDd neurons with lever press responses exhibit no significant difference between ears open and sealed (n = 83). b, Raw firing rate (that is not normalized to baseline firing rate) between ears open and sealed for neurons with activated responses (left, n = 11) upon lever pressing significantly decreased their peak firing rate (open: 17.0 ± 4.7 Hz; sealed: 14.2 ± 5.0 Hz; two-tailed, paired t-test, t = 2.480, P = 0.0325), while neurons with inhibited responses (right, n = 5) showed no significant change with ear sealing. c, Latency of peak/dip responses upon lever pressing between ears open and sealed for activated (left, n = 11) and inhibited (right, n = 5) neurons showed no significant difference. d, Single-units were recorded extracellularly from AUDd in FosTRAP animals (n = 2, 2 recording sessions) in which ChR2-EYFP was expressed in a Cre-ON dependent manner following 4-OHT induction. e, Raster plot (left) and PSTH (right) of an example photo-tagged unit (significant response within 10 ms of light stimulus onset; P < 0.01, Stimulus-Associated spike Latency Test (SALT)). f, Light modulation index (difference in light-evoked and baseline firing rate divided by their sum) of all single-units recorded at different cortical depths. Filled dots indicate putatively photo-tagged units (P < 0.01, SALT). Bars indicate the mean light modulation index in 100 μm bins (unfilled: all units, filled: photo-tagged units). Pie chart: percent of single-units that are putatively photo-tagged (P < 0.01, SALT). g, Single-units were recorded extracellularly from AUDd in FosTRAP animals (n = 2, 3 recording sessions) in which ChR2-mcherry was expressed in a Cre-OFF dependent manner following 4-OHT induction. h-i, Same as for (e-f) but for Cre-OFF population. j-l, Correlation analysis of change in the press rate at 30 s and change in half peak fall time between stimulation and no stimulation trials (Δ = stimulation - no stimulation) for the three experimental conditions tested with FosTRAP: ears open/Cre-ON (j), ears sealed/Cre-ON (k), and ears open/Cre-OFF (l). Firing indicies are means, and error is s.e.m. Differences in firing index were analyzed using paired t-tests. *P < 0.05; NS, not significant. Grey shading for correlation plots denotes 95% confidence interval for regression. PCC denotes Pearson correlation coefficient.

Extended Data Fig. 7 FosTRAPed populations, and assessment of muscimol spread, related to main Fig. 4.

a, Exemplar histology from a FosTRAP animal showing Cre-dependent expression of EYFP is primarily restricted to AUDd. Optrode placement is overlayed showing conical light spread from the fiber tip. b, Cell density of Cre-ON cells in the three auditory cortical fields (top), and percentage within each field (bottom). The highest density (main effect of treatment F(2, 9) = 4.305, P = 0.0488, AUDd vs. AUDv, P = 0.0439) and percentage (main effect of treatment F(2, 9) = 17.75, P = 0.0008, AUDd vs. AUDv, P = 0.0006) of cells are located within AUDd, with decreasing expression moving ventrally to AUDv (n = 4). c, Diagram depicting parameters used to calculate conical volume of illumination sufficient to induce spiking. Using a formula that takes into account scattering, absorption, and geometric loss of light through the brain, we calculated the total illuminated volume that is within the intensity threshold (≥1 mW/mm2) to activate ChR2 and elicit spikes. This volume, which is based on the 5 mW initial intensity at the fiber tip (~159 mW/mm2) makes up a cone with an angle of θ = 33° that stretches z = 0.87 mm downward. Using the trigonometric relationship between z and θ, the radius of the base of this cone, r = 0.061 mm3, can be calculated. Based on r and z, the volume of the cone can be determined. Using the neuronal density of the auditory cortex, and the percentage of Cre-ON and Cre-OFF cells from laminar recordings, we calculated ~1,100 cells/hemisphere were activated in the ears open/Cre-ON condition, and ~5,600 cells/hemisphere in the ears open/Cre-OFF condition. d, Exemplar histology of an animal implanted with a cannula in AUDd, and sacrificed following infusion of fast green, showing dye is largely restricted to AUDd. For FosTRAP cell density and regional percentage quantifications, data were analyzed using one-way ANOVA followed by Tukey post hoc comparisons, and bars are means. Scale bars for all immunohistochemical images denote 1 mm, and ‘D’ and ‘L’ denote dorsal and lateral, respectively. ***P < 0.001; *P < 0.05; NS, not significant.

Extended Data Fig. 8 Optogenetic, press-dependent perturbation of AUDd via stimulation of CAMKII + populations with ears open disrupts SFI timing performance, related to main Fig. 5.

a, Exemplar histology from a CAMKII-Cre animal showing Cre-dependent expression of ChR2-EYFP is primarily restricted to AUDd. b, Behavior of an example animal using closed-loop, press-triggered optical stimulation (5 mW, 100 ms per press) of CAMKII + populations in AUDd during SFI performance (blue line denotes stimulation and grey denotes no stimulation). c, Stimulation and no stimulation average PETHs (n = 8) for response rate (top) and percent maximum response rate (bottom) for press triggered optical stimulation of CAMKII + populations in AUDd with ears open. d, The optogenetic stimulation effects on response rates at 30 s (left, two-tailed, paired t-test, t = 4.295, P = 0.0036), peak time, and half peak fall times (right, two-tailed, paired t-test,t = 5.261, P = 0.0012) for press-dependent optical stimulation of CAMKII + populations in AUDd (n = 8) during SFI performance with ears open. Scale bars for immunohistochemical image denotes 400 μm, and ‘D’ and ‘L’ denote dorsal and lateral, respectively. Shading for average PETHs denotes SEM. **P < 0.01; NS, not significant.

Supplementary information

Reporting Summary

Supplementary Video 1

Behavioral example of a trained mouse during the probe trial of the SFI task.

Supplementary Video 2

Behavioral example of a FosTRAP mouse from the ears-open/Cre-ON group performing the SFI task under auditory deprivation during probe trials without (top) or with (bottom) optical stimulation.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cook, J.R., Li, H., Nguyen, B. et al. Secondary auditory cortex mediates a sensorimotor mechanism for action timing. Nat Neurosci 25, 330–344 (2022). https://doi.org/10.1038/s41593-022-01025-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01025-5

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing