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Mechanisms underlying gain modulation in the cortex

Nature Reviews Neuroscience volume 21, pages 80–92 (2020)Cite this article

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Abstract

Cortical gain regulation allows neurons to respond adaptively to changing inputs. Neural gain is modulated by internal and external influences, including attentional and arousal states, motor activity and neuromodulatory input. These influences converge to a common set of mechanisms for gain modulation, including GABAergic inhibition, synaptically driven fluctuations in membrane potential, changes in cellular conductance and changes in other biophysical neural properties. Recent work has identified GABAergic interneurons as targets of neuromodulatory input and mediators of state-dependent gain modulation. Here, we review the engagement and effects of gain modulation in the cortex. We highlight key recent findings that link phenomenological observations of gain modulation to underlying cellular and circuit-level mechanisms. Finally, we place these cellular and circuit interactions in the larger context of their impact on perception and cognition.

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Fig. 1: Cellular and network-level mechanisms of gain modulation.
Fig. 2: Multiple modes of state-dependent cortical gain modulation.

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References

  1. Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168–185 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lee, S. H. et al. Activation of specific interneurons improves V1 feature selectivity and visual perception. Nature 488, 379–383 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ohshiro, T., Angelaki, D. E. & DeAngelis, G. C. A normalization model of multisensory integration. Nat. Neurosci. 14, 775–782 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Carandini, M. & Heeger, D. J. Summation and division by neurons in primate visual cortex. Science 264, 1333–1336 (1994).

    Article  CAS  PubMed  Google Scholar 

  5. Carandini, M. & Heeger, D. J. Normalization as a canonical neural computation. Nat. Rev. Neurosci. 13, 51–62 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Salinas, E. & Thier, P. Gain modulation: a major computational principle of the central nervous system. Neuron 27, 15–21 (2000).

    Article  CAS  PubMed  Google Scholar 

  7. Andersen, R. A., Snyder, L. H., Bradley, D. C. & Xing, J. Multimodal representation of space in the posterior parietal cortex and its use in planning movements. Annu. Rev. Neurosci. 20, 303–330 (1997).

    Article  CAS  PubMed  Google Scholar 

  8. Pouget, A. & Snyder, L. H. Computational approaches to sensorimotor transformations. Nat. Neurosci. 3, 1192–1198 (2000).

    Article  CAS  PubMed  Google Scholar 

  9. Salinas, E. & Sejnowski, T. J. Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J. Neurosci. 20, 6193–6209 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Finn, I. M., Priebe, N. J. & Ferster, D. The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. Neuron 54, 137–152 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sclar, G. & Freeman, R. D. Orientation selectivity in the cat’s striate cortex is invariant with stimulus contrast. Exp. Brain Res. 46, 457–461 (1982).

    Article  CAS  PubMed  Google Scholar 

  12. Skottun, B. C., Bradley, A., Sclar, G., Ohzawa, I. & Freeman, R. D. The effects of contrast on visual orientation and spatial frequency discrimination: a comparison of single cells and behavior. J. Neurophysiol. 57, 773–786 (1987).

    Article  CAS  PubMed  Google Scholar 

  13. Anderson, J. S., Lampl, I., Gillespie, D. C. & Ferster, D. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290, 1968–1972 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. McAdams, C. J. & Maunsell, J. H. Effects of attention on the reliability of individual neurons in monkey visual cortex. Neuron 23, 765–773 (1999).

    Article  CAS  PubMed  Google Scholar 

  15. Somers, D. C., Nelson, S. B. & Sur, M. An emergent model of orientation selectivity in cat visual cortical simple cells. J. Neurosci. 15, 5448–5465 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Treue, S. & Martinez Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 (1999).

    Article  CAS  PubMed  Google Scholar 

  17. Baccus, S. A. & Meister, M. Fast and slow contrast adaptation in retinal circuitry. Neuron 36, 909–919 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Ruff, D. A., Ni, A. M. & Cohen, M. R. Cognition as a window into neuronal population space. Annu. Rev. Neurosci. 41, 77–97 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shadlen, M. N., Britten, K. H., Newsome, W. T. & Movshon, J. A. A computational analysis of the relationship between neuronal and behavioral responses to visual motion. J. Neurosci. 16, 1486–1510 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Barlow, H. B., Kaushal, T. P., Hawken, M. & Parker, A. J. Human contrast discrimination and the threshold of cortical neurons. J. Opt. Soc. Am. A 4, 2366–2371 (1987).

    Article  CAS  PubMed  Google Scholar 

  21. Boynton, G. M., Demb, J. B., Glover, G. H. & Heeger, D. J. Neuronal basis of contrast discrimination. Vis. Res. 39, 257–269 (1999).

    Article  CAS  PubMed  Google Scholar 

  22. Clatworthy, P. L., Chirimuuta, M., Lauritzen, J. S. & Tolhurst, D. J. Coding of the contrasts in natural images by populations of neurons in primary visual cortex (V1). Vis. Res. 43, 1983–2001 (2003).

    Article  CAS  PubMed  Google Scholar 

  23. Parker, A. & Hawken, M. Capabilities of monkey cortical cells in spatial-resolution tasks. J. Opt. Soc. Am. A 2, 1101–1114 (1985).

    Article  CAS  PubMed  Google Scholar 

  24. Watson, A. B. Gain, noise, and contrast sensitivity of linear visual neurons. Vis. Neurosci. 4, 147–157 (1990).

    Article  CAS  PubMed  Google Scholar 

  25. Eldar, E., Cohen, J. D. & Niv, Y. Amplified selectivity in cognitive processing implements the neural gain model of norepinephrine function. Behav. Brain Sci. 39, e206 (2016).

    Article  PubMed  Google Scholar 

  26. Natan, R. G., Carruthers, I. M., Mwilambwe-Tshilobo, L. & Geffen, M. N. Gain control in the auditory cortex evoked by changing temporal correlation of sounds. Cereb. Cortex 27, 2385–2402 (2017).

    PubMed  Google Scholar 

  27. Mineault, P. J., Tring, E., Trachtenberg, J. T. & Ringach, D. L. Enhanced spatial resolution during locomotion and heightened attention in mouse primary visual cortex. J. Neurosci. 36, 6382–6392 (2016). This paper demonstrates that the relative gain of visual responses between quiescence and locomotion is heterogeneous across cells and depends on the (spatial-frequency) tuning properties of the cell.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Reimer, J. et al. Pupil fluctuations track fast switching of cortical states during quiet wakefulness. Neuron 84, 355–362 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bennett, C., Arroyo, S. & Hestrin, S. Subthreshold mechanisms underlying state-dependent modulation of visual responses. Neuron 80, 350–357 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ohshiro, T., Angelaki, D. E. & DeAngelis, G. C. A neural signature of divisive normalization at the level of multisensory integration in primate cortex. Neuron 95, 399–411.e8 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ratan Murty, N. A. & Arun, S. P. Multiplicative mixing of object identity and image attributes in single inferior temporal neurons. Proc. Natl Acad. Sci. USA 115, E3276–E3285 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  32. Niell, C. M. & Stryker, M. P. Modulation of visual responses by behavioral state in mouse visual cortex. Neuron 65, 472–479 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Vinck, M., Batista-Brito, R., Knoblich, U. & Cardin, J. A. Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding. Neuron 86, 740–754 (2015). This paper shows that arousal and locomotion differentially regulate neural activity and sensory response gain in mouse V1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Destexhe, A., Contreras, D. & Steriade, M. Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neurosci. 19, 4595–4608 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Livingstone, M. S. & Hubel, D. H. Effects of sleep and arousal on the processing of visual information in the cat. Nature 291, 554–561 (1981).

    Article  CAS  PubMed  Google Scholar 

  36. Pakan, J. M. et al. Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex. eLife 5, e14985 (2016). This paper shows that locomotion-induced gain modulation of neuronal activity is context dependent, and varies across light and dark conditions and across distinct cell populations.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Dadarlat, M. C. & Stryker, M. P. Locomotion enhances neural encoding of visual stimuli in mouse V1 J. Neurosci. 37, 3764–3775 (2017). This paper demonstrates that locomotion enhances neural encoding of visual stimuli through increased firing rates and decreased noise correlations across the population.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Schneider, D. M., Nelson, A. & Mooney, R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 513, 189–194 (2014). This article shows that locomotion decreases the response gain in the mouse primary auditory cortex via inhibitory interneuron actions in the local cortical circuit.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Munoz, W., Tremblay, R., Levenstein, D. & Rudy, B. Layer-specific modulation of neocortical dendritic inhibition during active wakefulness. Science 355, 954–959 (2017). This paper shows that distinct SST + interneuron populations demonstrate lamina-dependent and state-dependent differences in gain modulation in S1.

    Article  CAS  PubMed  Google Scholar 

  41. Maunsell, J. H. R. Neuronal mechanisms of visual attention. Annu. Rev. Vis. Sci. 1, 373–391 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. McAdams, C. J. & Reid, R. C. Attention modulates the responses of simple cells in monkey primary visual cortex. J. Neurosci. 25, 11023–11033 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Reynolds, J. H., Pasternak, T. & Desimone, R. Attention increases sensitivity of V4 neurons. Neuron 26, 703–714 (2000).

    Article  CAS  PubMed  Google Scholar 

  44. Connor, C. E., Gallant, J. L., Preddie, D. C. & Van Essen, D. C. Responses in area V4 depend on the spatial relationship between stimulus and attention. J. Neurophysiol. 75, 1306–1308 (1996).

    Article  CAS  PubMed  Google Scholar 

  45. Connor, C. E., Preddie, D. C., Gallant, J. L. & Van Essen, D. C. Spatial attention effects in macaque area V4. J. Neurosci. 17, 3201–3214 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lee, J. & Maunsell, J. H. A normalization model of attentional modulation of single unit responses. PLOS ONE 4, e4651 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Martinez-Trujillo, J. C. & Treue, S. Feature-based attention increases the selectivity of population responses in primate visual cortex. Curr. Biol. 14, 744–751 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Williford, T. & Maunsell, J. H. Effects of spatial attention on contrast response functions in macaque area V4. J. Neurophysiol. 96, 40–54 (2006).

    Article  PubMed  Google Scholar 

  49. Reynolds, J. H., Chelazzi, L. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. J. Neurosci. 19, 1736–1753 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ecker, A. S., Denfield, G. H., Bethge, M. & Tolias, A. S. On the structure of neuronal population activity under fluctuations in attentional state. J. Neurosci. 36, 1775–1789 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Rabinowitz, N. C., Goris, R. L., Cohen, M. & Simoncelli, E. P. Attention stabilizes the shared gain of V4 populations. eLife 4, e08998 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Tiesinga, P. H. & Sejnowski, T. J. Rapid temporal modulation of synchrony by competition in cortical interneuron networks. Neural Comput. 16, 251–275 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Reynolds, J. H. & Chelazzi, L. Attentional modulation of visual processing. Annu. Rev. Neurosci. 27, 611–647 (2004).

    Article  CAS  PubMed  Google Scholar 

  54. Schoups, A., Vogels, R., Qian, N. & Orban, G. Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549–553 (2001).

    Article  CAS  PubMed  Google Scholar 

  55. Jurjut, O., Georgieva, P., Busse, L. & Katzner, S. Learning enhances sensory processing in mouse V1 before improving behavior. J. Neurosci. 37, 6460–6474 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kaneko, M. & Stryker, M. P. Sensory experience during locomotion promotes recovery of function in adult visual cortex. eLife 3, e02798 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Chance, F. S., Abbott, L. F. & Reyes, A. D. Gain modulation from background synaptic input. Neuron 35, 773–782 (2002).

    Article  CAS  PubMed  Google Scholar 

  58. Ho, N. & Destexhe, A. Synaptic background activity enhances the responsiveness of neocortical pyramidal neurons. J. Neurophysiol. 84, 1488–1496 (2000).

    Article  CAS  PubMed  Google Scholar 

  59. Prescott, S. A. & De Koninck, Y. Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation. Proc. Natl Acad. Sci. USA 100, 2076–2081 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Shu, Y., Hasenstaub, A., Badoual, M., Bal, T. & McCormick, D. A. Barrages of synaptic activity control the gain and sensitivity of cortical neurons. J. Neurosci. 23, 10388–10401 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Cardin, J. A., Palmer, L. A. & Contreras, D. Cellular mechanisms underlying stimulus-dependent gain modulation in primary visual cortex neurons in vivo. Neuron 59, 150–160 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Ly, C. & Doiron, B. Divisive gain modulation with dynamic stimuli in integrate-and-fire neurons. PLOS Comput. Biol. 5, e1000365 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Miller, K. D. & Troyer, T. W. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J. Neurophysiol. 87, 653–659 (2002).

    Article  PubMed  Google Scholar 

  64. Hansel, D. & van Vreeswijk, C. How noise contributes to contrast invariance of orientation tuning in cat visual cortex. J. Neurosci. 22, 5118–5128 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Bulsara, A., Jacobs, E. W., Zhou, T., Moss, F. & Kiss, L. Stochastic resonance in a single neuron model: theory and analog simulation. J. Theor. Biol. 152, 531–555 (1991).

    Article  CAS  PubMed  Google Scholar 

  66. Wiesenfeld, K. & Moss, F. Stochastic resonance and the benefits of noise: from ice ages to crayfish and SQUIDs. Nature 373, 33–36 (1995).

    Article  CAS  PubMed  Google Scholar 

  67. Khubieh, A., Ratte, S., Lankarany, M. & Prescott, S. A. Regulation of cortical dynamic range by background synaptic noise and feedforward inhibition. Cereb. Cortex 26, 3357–3369 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Atallah, B. V., Bruns, W., Carandini, M. & Scanziani, M. Parvalbumin-expressing interneurons linearly transform cortical responses to visual stimuli. Neuron 73, 159–170 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Katzner, S., Busse, L. & Carandini, M. GABAA inhibition controls response gain in visual cortex. J. Neurosci. 31, 5931–5941 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Article  PubMed  CAS  Google Scholar 

  71. Gentet, L. J., Avermann, M., Matyas, F., Staiger, J. F. & Petersen, C. C. Membrane potential dynamics of GABAergic neurons in the barrel cortex of behaving mice. Neuron 65, 422–435 (2010).

    Article  CAS  PubMed  Google Scholar 

  72. Pala, A. & Petersen, C. C. State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7, e35869 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Polack, P. O., Friedman, J. & Golshani, P. Cellular mechanisms of brain state-dependent gain modulation in visual cortex. Nat. Neurosci. 16, 1331–1339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Poulet, J. F. & Petersen, C. C. Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature 454, 881–885 (2008).

    Article  CAS  PubMed  Google Scholar 

  75. Chen, N., Sugihara, H. & Sur, M. An acetylcholine-activated microcircuit drives temporal dynamics of cortical activity. Nat. Neurosci. 18, 892–902 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Carvalho, T. P. & Buonomano, D. V. Differential effects of excitatory and inhibitory plasticity on synaptically driven neuronal input–output functions. Neuron 61, 774–785 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Mitchell, S. J. & Silver, R. A. Shunting inhibition modulates neuronal gain during synaptic excitation. Neuron 38, 433–445 (2003).

    Article  CAS  PubMed  Google Scholar 

  78. Murphy, B. K. & Miller, K. D. Multiplicative gain changes are induced by excitation or inhibition alone. J. Neurosci. 23, 10040–10051 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Abbott, L. F. & Chance, F. S. Drivers and modulators from push–pull and balanced synaptic input. Prog. Brain Res. 149, 147–155 (2005).

    Article  CAS  PubMed  Google Scholar 

  80. Ayaz, A. & Chance, F. S. Gain modulation of neuronal responses by subtractive and divisive mechanisms of inhibition. J. Neurophysiol. 101, 958–968 (2009).

    Article  PubMed  Google Scholar 

  81. Brozovic, M., Abbott, L. F. & Andersen, R. A. Mechanism of gain modulation at single neuron and network levels. J. Comput. Neurosci. 25, 158–168 (2008).

    Article  CAS  PubMed  Google Scholar 

  82. Vogels, T. P. & Abbott, L. F. Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat. Neurosci. 12, 483–491 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Fellous, J. M., Rudolph, M., Destexhe, A. & Sejnowski, T. J. Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. Neuroscience 122, 811–829 (2003).

    Article  CAS  PubMed  Google Scholar 

  84. Holt, G. R. & Koch, C. Shunting inhibition does not have a divisive effect on firing rates. Neural Comput. 9, 1001–1013 (1997).

    Article  CAS  PubMed  Google Scholar 

  85. Litwin-Kumar, A., Oswald, A. M., Urban, N. N. & Doiron, B. Balanced synaptic input shapes the correlation between neural spike trains. PLOS Comput. Biol. 7, e1002305 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Rosenbaum, R. & Josic, K. Membrane potential and spike train statistics depend distinctly on input statistics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 84, 051902 (2011).

    Article  PubMed  CAS  Google Scholar 

  87. Shea-Brown, E., Josic, K., de la Rocha, J. & Doiron, B. Correlation and synchrony transfer in integrate-and-fire neurons: basic properties and consequences for coding. Phys. Rev. Lett. 100, 108102 (2008).

    Article  PubMed  CAS  Google Scholar 

  88. Tchumatchenko, T. & Wolf, F. Representation of dynamical stimuli in populations of threshold neurons. PLOS Comput. Biol. 7, e1002239 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Arsiero, M., Luscher, H. R., Lundstrom, B. N. & Giugliano, M. The impact of input fluctuations on the frequency–current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. J. Neurosci. 27, 3274–3284 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Higgs, M. H., Slee, S. J. & Spain, W. J. Diversity of gain modulation by noise in neocortical neurons: regulation by the slow afterhyperpolarization conductance. J. Neurosci. 26, 8787–8799 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Hong, S., Ratte, S., Prescott, S. A. & De Schutter, E. Single neuron firing properties impact correlation-based population coding. J. Neurosci. 32, 1413–1428 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lundstrom, B. N., Famulare, M., Sorensen, L. B., Spain, W. J. & Fairhall, A. L. Sensitivity of firing rate to input fluctuations depends on time scale separation between fast and slow variables in single neurons. J. Comput. Neurosci. 27, 277–290 (2009).

    Article  PubMed  Google Scholar 

  93. Rauch, A., La Camera, G., Luscher, H. R., Senn, W. & Fusi, S. Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J. Neurophysiol. 90, 1598–1612 (2003).

    Article  PubMed  Google Scholar 

  94. Larkum, M. E., Senn, W. & Luscher, H. R. Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb. Cortex 14, 1059–1070 (2004).

    Article  PubMed  Google Scholar 

  95. Mehaffey, W. H., Doiron, B., Maler, L. & Turner, R. W. Deterministic multiplicative gain control with active dendrites. J. Neurosci. 25, 9968–9977 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Jarvis, S., Nikolic, K. & Schultz, S. R. Neuronal gain modulability is determined by dendritic morphology: a computational optogenetic study. PLOS Comput. Biol. 14, e1006027 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Quiquempoix, M. et al. Layer 2/3 pyramidal neurons control the gain of cortical output. Cell Rep. 24, 2799–2807.e4 (2018).

    Article  CAS  PubMed  Google Scholar 

  98. Sato, T. K., Haider, B., Hausser, M. & Carandini, M. An excitatory basis for divisive normalization in visual cortex. Nat. Neurosci. 19, 568–570 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Haider, B. & McCormick, D. A. Rapid neocortical dynamics: cellular and network mechanisms. Neuron 62, 171–189 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Silver, R. A. Neuronal arithmetic. Nat. Rev. Neurosci. 11, 474–489 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Nelson, S., Toth, L., Sheth, B. & Sur, M. Orientation selectivity of cortical neurons during intracellular blockade of inhibition. Science 265, 774–777 (1994).

    Article  CAS  PubMed  Google Scholar 

  102. Atallah, B. V., Scanziani, M. & Carandini, M. Atallah et al. reply. Nature 508, E3 (2014).

    Article  CAS  PubMed  Google Scholar 

  103. El-Boustani, S., Wilson, N. R., Runyan, C. A. & Sur, M. El-Boustani et al. reply. Nature 508, E3–E4 (2014).

    Article  CAS  PubMed  Google Scholar 

  104. Wilson, N. R., Runyan, C. A., Wang, F. L. & Sur, M. Division and subtraction by distinct cortical inhibitory networks in vivo. Nature 488, 343–348 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Natan, R. G., Rao, W. & Geffen, M. N. Cortical interneurons differentially shape frequency tuning following adaptation. Cell Rep. 21, 878–890 (2017). This paper shows that distinct cortical interneuron populations differently modulate the gain of frequency-tuned excitatory responses during adaptation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Phillips, E. A. & Hasenstaub, A. R. Asymmetric effects of activating and inactivating cortical interneurons. eLife 5, e18383 (2016). This paper shows that optogenetic activation of GABAergic interneurons in the cortex does not fully capture the impact of inhibition on excitatory neuron response gain.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  107. Seybold, B. A., Phillips, E. A. K., Schreiner, C. E. & Hasenstaub, A. R. Inhibitory actions unified by network integration. Neuron 87, 1181–1192 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Fishell, G. & Rudy, B. Mechanisms of inhibition within the telencephalon: ‘‘where the wild things are’’. Annu. Rev. Neurosci. 34, 535–567 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004).

    Article  CAS  PubMed  Google Scholar 

  110. El-Boustani, S. & Sur, M. Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo. Nat. Commun. 5, 5689 (2014).

    Article  CAS  PubMed  Google Scholar 

  111. Lee, A. M. et al. Identification of a brainstem circuit regulating visual cortical state in parallel with locomotion. Neuron 83, 455–466 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Cardin, J. A. Inhibitory interneurons regulate temporal precision and correlations in cortical circuits. Trends Neurosci. 41, 689–700 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Cone, J. J., Scantlen, M. D., Histed, M. H. & Maunsell, J. H. R. Different inhibitory interneuron cell classes make distinct contributions to visual contrast perception. eNeuro https://doi.org/10.1523/ENEURO.0337-18.2019 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Ayzenshtat, I., Karnani, M. M., Jackson, J. & Yuste, R. Cortical control of spatial resolution by VIP+ interneurons. J. Neurosci. 36, 11498–11509 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Hong, Y. K., Lacefield, C. O., Rodgers, C. C. & Bruno, R. M. Sensation, movement and learning in the absence of barrel cortex. Nature 561, 542–546 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Otchy, T. M. et al. Acute off-target effects of neural circuit manipulations. Nature 528, 358–363 (2015).

    Article  CAS  PubMed  Google Scholar 

  117. Wolff, S. B. & Olveczky, B. P. The promise and perils of causal circuit manipulations. Curr. Opin. Neurobiol. 49, 84–94 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Phillips, E. A. K., Schreiner, C. E. & Hasenstaub, A. R. Cortical interneurons differentially regulate the effects of acoustic context. Cell Rep. 20, 771–778 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Fu, Y. et al. A cortical circuit for gain control by behavioral state. Cell 156, 1139–1152 (2014). This paper provides evidence that VIP + interneurons are activated by locomotion and may contribute to state-dependent visual response gain modulation in mouse V1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Karnani, M. M. et al. Opening holes in the blanket of inhibition: localized lateral disinhibition by VIP interneurons. J. Neurosci. 36, 3471–3480 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Karnani, M. M. et al. Cooperative subnetworks of molecularly similar interneurons in mouse neocortex. Neuron 90, 86–100 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Lee, S., Kruglikov, I., Huang, Z. J., Fishell, G. & Rudy, B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat. Neurosci. 16, 1662–1670 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Pi, H. J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Batista-Brito, R. et al. Developmental dysfunction of VIP interneurons impairs cortical circuits. Neuron 95, 884–895.e9 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Dipoppa, M. et al. Vision and locomotion shape the interactions between neuron types in mouse visual cortex. Neuron 98, 602–615.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Ozeki, H., Finn, I. M., Schaffer, E. S., Miller, K. D. & Ferster, D. Inhibitory stabilization of the cortical network underlies visual surround suppression. Neuron 62, 578–592 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Tsodyks, M. V., Skaggs, W. E., Sejnowski, T. J. & McNaughton, B. L. Paradoxical effects of external modulation of inhibitory interneurons. J. Neurosci. 17, 4382–4388 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Kato, H. K., Asinof, S. K. & Isaacson, J. S. Network-level control of frequency tuning in auditory cortex. Neuron 95, 412–423.e4 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Garcia Del Molino, L. C., Yang, G. R., Mejias, J. F. & Wang, X. J. Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types. eLife 6, e29742 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  130. Litwin-Kumar, A., Rosenbaum, R. & Doiron, B. Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes. J. Neurophysiol. 115, 1399–1409 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  131. Zhou, M. et al. Scaling down of balanced excitation and inhibition by active behavioral states in auditory cortex. Nat. Neurosci. 17, 841–850 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Boly, M. et al. Baseline brain activity fluctuations predict somatosensory perception in humans. Proc. Natl Acad. Sci. USA 104, 12187–12192 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Fox, M. D., Snyder, A. Z., Vincent, J. L. & Raichle, M. E. Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron 56, 171–184 (2007).

    Article  CAS  PubMed  Google Scholar 

  134. Hesselmann, G., Kell, C. A., Eger, E. & Kleinschmidt, A. Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proc. Natl Acad. Sci. USA 105, 10984–10989 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Hesselmann, G., Kell, C. A. & Kleinschmidt, A. Ongoing activity fluctuations in hMT+ bias the perception of coherent visual motion. J. Neurosci. 28, 14481–14485 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Palva, J. M. & Palva, S. Roles of multiscale brain activity fluctuations in shaping the variability and dynamics of psychophysical performance. Prog. Brain Res. 193, 335–350 (2011).

    Article  PubMed  Google Scholar 

  137. Diamond, D. M., Campbell, A. M., Park, C. R., Halonen, J. & Zoladz, P. R. The temporal dynamics model of emotional memory processing: a synthesis on the neurobiological basis of stress-induced amnesia, flashbulb and traumatic memories, and the Yerkes–Dodson law. Neural. Plast. 2007, 60803 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  138. Yerkes, R. M. & Dodson, J. D. The relation of strength of stimulus to rapidity of habit-formation. J. Comp. Neurol. Psychol. 18, 459–482 (1908).

    Article  Google Scholar 

  139. He, B. J. Spontaneous and task-evoked brain activity negatively interact. J. Neurosci. 33, 4672–4682 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Bullock, T., Elliott, J. C., Serences, J. T. & Giesbrecht, B. Acute exercise modulates feature-selective responses in human cortex. J. Cognit. Neurosci. 29, 605–618 (2017).

    Article  Google Scholar 

  141. He, B. J. & Zempel, J. M. Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance. PLOS Comput. Biol. 9, e1003348 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Murphy, P. R., Vandekerckhove, J. & Nieuwenhuis, S. Pupil-linked arousal determines variability in perceptual decision making. PLOS Comput. Biol. 10, e1003854 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  143. Aston-Jones, G. & Cohen, J. D. An integrative theory of locus coeruleus–norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450 (2005).

    Article  CAS  PubMed  Google Scholar 

  144. Murphy, P. R., O’Connell, R. G., O’Sullivan, M., Robertson, I. H. & Balsters, J. H. Pupil diameter covaries with BOLD activity in human locus coeruleus. Hum. Brain Mapp. 35, 4140–4154 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Joshi, S., Li, Y., Kalwani, R. M. & Gold, J. I. Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89, 221–234 (2016).

    Article  CAS  PubMed  Google Scholar 

  146. Reimer, J. et al. Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Erisken, S. et al. Effects of locomotion extend throughout the mouse early visual system. Curr. Biol. 24, 2899–2907 (2014).

    Article  CAS  PubMed  Google Scholar 

  148. Tang, L. & Higley, M. J. Layer 5 circuits in V1 differentially control visuomotor behavior. bioRxiv https://doi.org/10.1101/540807 (2019).

  149. Saleem, A. B., Ayaz, A., Jeffery, K. J., Harris, K. D. & Carandini, M. Integration of visual motion and locomotion in mouse visual cortex. Nat. Neurosci. 16, 1864–1869 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Neske, G. T., Nestvogel, D., Steffan, P. J. & McCormick, D. A. Distinct waking states for strong evoked responses in primary visual cortex and optimal visual detection performance. J. Neurosci. 39, 10044–10059 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  151. Bullock, T., Cecotti, H. & Giesbrecht, B. Multiple stages of information processing are modulated during acute bouts of exercise. Neuroscience 307, 138–150 (2015).

    Article  CAS  PubMed  Google Scholar 

  152. Benjamin, A. V., Wailes-Newson, K., Ma-Wyatt, A., Baker, D. H. & Wade, A. R. The effect of locomotion on early visual contrast processing in humans. J. Neurosci. 38, 3050–3059 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. McGinley, M. J., David, S. V. & McCormick, D. A. Cortical membrane potential signature of optimal states for sensory signal detection. Neuron 87, 179–192 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  154. Barson, D. et al. Simultaneous mesoscopic and two-photon imaging of neuronal activity in cortical circuits. Nat. Methods https://doi.org/10.1038/s41592-019-0625-2 (2019).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  155. Clancy, K. B., Orsolic, I. & Mrsic-Flogel, T. D. Locomotion-dependent remapping of distributed cortical networks. Nat. Neurosci. 22, 778–786 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Shimaoka, D., Harris, K. D. & Carandini, M. Effects of arousal on mouse sensory cortex depend on modality. Cell Rep. 22, 3160–3167 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Single-trial neural dynamics are dominated by richly varied movements. Nat. Neurosci. 22, 1677–1686 (2019). This study reports that animal movements capture the majority of neural variability across the cortex, and those that are task-aligned account for features commonly attributed to cognitive task demands.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Disney, A. A., Alasady, H. A. & Reynolds, J. H. Muscarinic acetylcholine receptors are expressed by most parvalbumin-immunoreactive neurons in area MT of the macaque. Brain Behav. 4, 431–445 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  159. Disney, A. A. & Aoki, C. Muscarinic acetylcholine receptors in macaque V1 are most frequently expressed by parvalbumin-immunoreactive neurons. J. Comp. Neurol. 507, 1748–1762 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  160. Disney, A. A., Domakonda, K. V. & Aoki, C. Differential expression of muscarinic acetylcholine receptors across excitatory and inhibitory cells in visual cortical areas V1 and V2 of the macaque monkey. J. Comp. Neurol. 499, 49–63 (2006).

    Article  CAS  PubMed  Google Scholar 

  161. Disney, A. A. & Reynolds, J. H. Expression of m1-type muscarinic acetylcholine receptors by parvalbumin-immunoreactive neurons in the primary visual cortex: a comparative study of rat, guinea pig, ferret, macaque, and human. J. Comp. Neurol. 522, 986–1003 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  162. Letzkus, J. J. et al. A disinhibitory microcircuit for associative fear learning in the auditory cortex. Nature 480, 331–335 (2011).

    Article  CAS  PubMed  Google Scholar 

  163. Porter, J. T. et al. Selective excitation of subtypes of neocortical interneurons by nicotinic receptors. J. Neurosci. 19, 5228–5235 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Urban-Ciecko, J., Jouhanneau, J. S., Myal, S. E., Poulet, J. F. A. & Barth, A. L. Precisely timed nicotinic activation drives SST inhibition in neocortical circuits. Neuron 97, 611–625.e5 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Disney, A. A., Aoki, C. & Hawken, M. J. Gain modulation by nicotine in macaque V1. Neuron 56, 701–713 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Gil, Z., Connors, B. W. & Amitai, Y. Differential regulation of neocortical synapses by neuromodulators and activity. Neuron 19, 679–686 (1997).

    Article  CAS  PubMed  Google Scholar 

  167. Hasselmo, M. E. & Bower, J. M. Cholinergic suppression specific to intrinsic not afferent fiber synapses in rat piriform (olfactory) cortex. J. Neurophysiol. 67, 1222–1229 (1992).

    Article  CAS  PubMed  Google Scholar 

  168. Kimura, F. Cholinergic modulation of cortical function: a hypothetical role in shifting the dynamics in cortical network. Neurosci. Res. 38, 19–26 (2000).

    Article  CAS  PubMed  Google Scholar 

  169. Kimura, F., Fukuda, M. & Tsumoto, T. Acetylcholine suppresses the spread of excitation in the visual cortex revealed by optical recording: possible differential effect depending on the source of input. Eur. J. Neurosci. 11, 3597–3609 (1999).

    Article  CAS  PubMed  Google Scholar 

  170. Disney, A. A., Aoki, C. & Hawken, M. J. Cholinergic suppression of visual responses in primate V1 is mediated by GABAergic inhibition. J. Neurophysiol. 108, 1907–1923 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Soma, S., Shimegi, S., Osaki, H. & Sato, H. Cholinergic modulation of response gain in the primary visual cortex of the macaque. J. Neurophysiol. 107, 283–291 (2012).

    Article  CAS  PubMed  Google Scholar 

  172. Herrero, J. L., Gieselmann, M. A. & Thiele, A. Muscarinic and nicotinic contribution to contrast sensitivity of macaque area V1 neurons. Front. Neural Circuits 11, 106 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  173. Askew, C., Intskirveli, I. & Metherate, R. Systemic nicotine increases gain and narrows receptive fields in A1 via integrated cortical and subcortical actions. eNeuro https://doi.org/10.1523/ENEURO.0192-17.2017 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Herrero, J. L. et al. Acetylcholine contributes through muscarinic receptors to attentional modulation in V1. Nature 454, 1110–1114 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Pinto, L. et al. Fast modulation of visual perception by basal forebrain cholinergic neurons. Nat. Neurosci. 16, 1857–1863 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Stewart, A. E., Yan, Z., Surmeier, D. J. & Foehring, R. C. Muscarine modulates Ca2+ channel currents in rat sensorimotor pyramidal cells via two distinct pathways. J. Neurophysiol. 81, 72–84 (1999).

    Article  CAS  PubMed  Google Scholar 

  177. Lorenzon, N. M. & Foehring, R. C. Relationship between repetitive firing and afterhyperpolarizations in human neocortical neurons. J. Neurophysiol. 67, 350–363 (1992).

    Article  CAS  PubMed  Google Scholar 

  178. McCormick, D. A. & Prince, D. A. Mechanisms of action of acetylcholine in the guinea-pig cerebral cortex in vitro. J. Physiol. 375, 169–194 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Schwindt, P. C., Spain, W. J. & Crill, W. E. Influence of anomalous rectifier activation on afterhyperpolarizations of neurons from cat sensorimotor cortex in vitro. J. Neurophysiol. 59, 468–481 (1988).

    Article  CAS  PubMed  Google Scholar 

  180. Wang, Z. & McCormick, D. A. Control of firing mode of corticotectal and corticopontine layer V burst-generating neurons by norepinephrine, acetylcholine, and 1S,3R-ACPD. J. Neurosci. 13, 2199–2216 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Eggermann, E. & Feldmeyer, D. Cholinergic filtering in the recurrent excitatory microcircuit of cortical layer 4. Proc. Natl Acad. Sci. USA 106, 11753–11758 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Gulledge, A. T., Park, S. B., Kawaguchi, Y. & Stuart, G. J. Heterogeneity of phasic cholinergic signaling in neocortical neurons. J. Neurophysiol. 97, 2215–2229 (2007).

    Article  CAS  PubMed  Google Scholar 

  183. Gulledge, A. T. & Stuart, G. J. Cholinergic inhibition of neocortical pyramidal neurons. J. Neurosci. 25, 10308–10320 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Dasgupta, R., Seibt, F. & Beierlein, M. Synaptic release of acetylcholine rapidly suppresses cortical activity by recruiting muscarinic receptors in layer 4. J. Neurosci. 38, 5338–5350 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Higley, M. J., Soler-Llavina, G. J. & Sabatini, B. L. Cholinergic modulation of multivesicular release regulates striatal synaptic potency and integration. Nat. Neurosci. 12, 1121–1128 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Giessel, A. J. & Sabatini, B. L. M1 muscarinic receptors boost synaptic potentials and calcium influx in dendritic spines by inhibiting postsynaptic SK channels. Neuron 68, 936–947 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Foehring, R. C., Schwindt, P. C. & Crill, W. E. Norepinephrine selectively reduces slow Ca2+- and Na+-mediated K+ currents in cat neocortical neurons. J. Neurophysiol. 61, 245–256 (1989).

    Article  CAS  PubMed  Google Scholar 

  188. Madison, D. V. & Nicoll, R. A. Actions of noradrenaline recorded intracellularly in rat hippocampal CA1 pyramidal neurones, in vitro. J. Physiol. 372, 221–244 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Mueller, D., Porter, J. T. & Quirk, G. J. Noradrenergic signaling in infralimbic cortex increases cell excitability and strengthens memory for fear extinction. J. Neurosci. 28, 369–375 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  190. Dodt, H. U., Pawelzik, H. & Zieglgansberger, W. Actions of noradrenaline on neocortical neurons in vitro. Brain Res. 545, 307–311 (1991).

    Article  CAS  PubMed  Google Scholar 

  191. Mynlieff, M. & Dunwiddie, T. V. Noradrenergic depression of synaptic responses in hippocampus of rat: evidence for mediation by α1-receptors. Neuropharmacology 27, 391–398 (1988).

    Article  CAS  PubMed  Google Scholar 

  192. Guan, D., Armstrong, W. E. & Foehring, R. C. Electrophysiological properties of genetically identified subtypes of layer 5 neocortical pyramidal neurons: Ca2+ dependence and differential modulation by norepinephrine. J. Neurophysiol. 113, 2014–2032 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  193. Waterhouse, B. D., Mouradian, R., Sessler, F. M. & Lin, R. C. Differential modulatory effects of norepinephrine on synaptically driven responses of layer V barrel field cortical neurons. Brain Res. 868, 39–47 (2000).

    Article  CAS  PubMed  Google Scholar 

  194. Armstrong-James, M. & Fox, K. Effects of ionophoresed noradrenaline on the spontaneous activity of neurones in rat primary somatosensory cortex. J. Physiol. 335, 427–447 (1983).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Bassant, M. H., Ennouri, K. & Lamour, Y. Effects of iontophoretically applied monoamines on somatosensory cortical neurons of unanesthetized rats. Neuroscience 39, 431–439 (1990).

    Article  CAS  PubMed  Google Scholar 

  196. Foote, S. L., Freedman, R. & Oliver, A. P. Effects of putative neurotransmitters on neuronal activity in monkey auditory cortex. Brain Res. 86, 229–242 (1975).

    Article  CAS  PubMed  Google Scholar 

  197. Waterhouse, B. D., Moises, H. C. & Woodward, D. J. Noradrenergic modulation of somatosensory cortical neuronal responses to iontophoretically applied putative neurotransmitters. Exp. Neurol. 69, 30–49 (1980).

    Article  CAS  PubMed  Google Scholar 

  198. Waterhouse, B. D., Moises, H. C. & Woodward, D. J. Alpha-receptor-mediated facilitation of somatosensory cortical neuronal responses to excitatory synaptic inputs and iontophoretically applied acetylcholine. Neuropharmacology 20, 907–920 (1981).

    Article  CAS  PubMed  Google Scholar 

  199. Ego-Stengel, V., Bringuier, V. & Shulz, D. E. Noradrenergic modulation of functional selectivity in the cat visual cortex: an in vivo extracellular and intracellular study. Neuroscience 111, 275–289 (2002).

    Article  CAS  PubMed  Google Scholar 

  200. Seillier, L. et al. Serotonin decreases the gain of visual responses in awake macaque V1. J. Neurosci. 37, 11390–11405 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Watakabe, A. et al. Enriched expression of serotonin 1B and 2A receptor genes in macaque visual cortex and their bidirectional modulatory effects on neuronal responses. Cereb. Cortex 19, 1915–1928 (2009).

    Article  PubMed  Google Scholar 

  202. Dugue, G. P. et al. Optogenetic recruitment of dorsal raphe serotonergic neurons acutely decreases mechanosensory responsivity in behaving mice. PLOS ONE 9, e105941 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  203. Davis, M., Strachan, D. I. & Kass, E. Excitatory and inhibitory effects of serotonin on sensorimotor reactivity measured with acoustic startle. Science 209, 521–523 (1980).

    Article  CAS  PubMed  Google Scholar 

  204. Vijayraghavan, S., Wang, M., Birnbaum, S. G., Williams, G. V. & Arnsten, A. F. Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory. Nat. Neurosci. 10, 376–384 (2007).

    Article  CAS  PubMed  Google Scholar 

  205. Williams, G. V. & Goldman-Rakic, P. S. Modulation of memory fields by dopamine D1 receptors in prefrontal cortex. Nature 376, 572–575 (1995).

    Article  CAS  PubMed  Google Scholar 

  206. Noudoost, B. & Moore, T. Control of visual cortical signals by prefrontal dopamine. Nature 474, 372–375 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Lur, G. & Higley, M. J. Glutamate receptor modulation is restricted to synaptic microdomains. Cell Rep. 12, 326–334 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Athilingam, J. C., Ben-Shalom, R., Keeshen, C. M., Sohal, V. S. & Bender, K. J. Serotonin enhances excitability and gamma frequency temporal integration in mouse prefrontal fast-spiking interneurons. eLife 6, e31991 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  209. Kawaguchi, Y. & Shindou, T. Noradrenergic excitation and inhibition of GABAergic cell types in rat frontal cortex. J. Neurosci. 18, 6963–6976 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Demb, J. B. Multiple mechanisms for contrast adaptation in the retina. Neuron 36, 781–783 (2002).

    Article  CAS  PubMed  Google Scholar 

  211. Farley, B. J., Quirk, M. C., Doherty, J. J. & Christian, E. P. Stimulus-specific adaptation in auditory cortex is an NMDA-independent process distinct from the sensory novelty encoded by the mismatch negativity. J. Neurosci. 30, 16475–16484 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Fishman, Y. I. & Steinschneider, M. Searching for the mismatch negativity in primary auditory cortex of the awake monkey: deviance detection or stimulus specific adaptation? J. Neurosci. 32, 15747–15758 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Kohn, A. & Movshon, J. A. Neuronal adaptation to visual motion in area MT of the macaque. Neuron 39, 681–691 (2003).

    Article  CAS  PubMed  Google Scholar 

  214. Szymanski, F. D., Garcia-Lazaro, J. A. & Schnupp, J. W. Current source density profiles of stimulus-specific adaptation in rat auditory cortex. J. Neurophysiol. 102, 1483–1490 (2009).

    Article  PubMed  Google Scholar 

  215. Ulanovsky, N., Las, L. & Nelken, I. Processing of low-probability sounds by cortical neurons. Nat. Neurosci. 6, 391–398 (2003).

    Article  CAS  PubMed  Google Scholar 

  216. Dean, I., Robinson, B. L., Harper, N. S. & McAlpine, D. Rapid neural adaptation to sound level statistics. J. Neurosci. 28, 6430–6438 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. Barlow, H. in Sensory Communication (MIT Press, 1961).

  218. Niyogi, R. K. & Wong-Lin, K. Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making. PLOS Comput. Biol. 9, e1003099 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. Busse, L., Wade, A. R. & Carandini, M. Representation of concurrent stimuli by population activity in visual cortex. Neuron 64, 931–942 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Hahnloser, R. H., Douglas, R. J. & Hepp, K. Attentional recruitment of inter-areal recurrent networks for selective gain control. Neural Comput. 14, 1669–1689 (2002).

    Article  PubMed  Google Scholar 

  221. Thiele, A. & Bellgrove, M. A. Neuromodulation of attention. Neuron 97, 769–785 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Schwartz, O. & Simoncelli, E. P. Natural signal statistics and sensory gain control. Nat. Neurosci. 4, 819–825 (2001).

    Article  CAS  PubMed  Google Scholar 

  223. Willmore, B. D., Bulstrode, H. & Tolhurst, D. J. Contrast normalization contributes to a biologically-plausible model of receptive-field development in primary visual cortex (V1). Vis. Res. 54, 49–60 (2012).

    Article  PubMed  Google Scholar 

  224. Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J. & Cohen, M. R. Learning and attention reveal a general relationship between population activity and behavior. Science 359, 463–465 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  225. Lee, S., Park, J. & Smirnakis, S. M. Internal gain modulations, but not changes in stimulus contrast, preserve the neural code. J. Neurosci. 39, 1671–1687 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  226. McGinley, M. J. et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  227. Rose, D. & Blakemore, C. Effects of bicuculline on functions of inhibition in visual cortex. Nature 249, 375–377 (1974).

    Article  CAS  PubMed  Google Scholar 

  228. Carandini, M. & Ferster, D. Membrane potential and firing rate in cat primary visual cortex. J. Neurosci. 20, 470–484 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  229. Isaacson, J. S. & Scanziani, M. How inhibition shapes cortical activity. Neuron 72, 231–243 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  230. Zhang, Y. P. & Oertner, T. G. Optical induction of synaptic plasticity using a light-sensitive channel. Nat. Methods 4, 139–141 (2007).

    Article  CAS  PubMed  Google Scholar 

  231. Allen, B. D., Singer, A. C. & Boyden, E. S. Principles of designing interpretable optogenetic behavior experiments. Learn. Mem. 22, 232–238 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  232. Cottam, J. C., Smith, S. L. & Hausser, M. Target-specific effects of somatostatin-expressing interneurons on neocortical visual processing. J. Neurosci. 33, 19567–19578 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  233. Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by US National Institutes of Health (NIH) R01 MH102365, NIH R01 EY022951, NIH R01 MH113852, a Simons Foundation Autism Research Initiative (SFARI) Research Grant, a Smith Family Award for Excellence in Biomedical Research, a Klingenstein Fellowship Award, an Alfred P. Sloan Fellowship, a US National Alliance for Research on Schizophrenia & Depression (NARSAD) Young Investigator Award, a McKnight Fellowship and a grant from the Ludwig Family Foundation to J.A.C.; and a Brown-Coxe fellowship and a NARSAD Young Investigator Award to K.A.F. The authors thank M. J. Higley and members of the Cardin and Higley laboratories for insightful discussions, and Q. Perrenoud for help with illustration.

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  1. Department of Neuroscience, Yale University, New Haven, CT, USA

    Katie A. Ferguson & Jessica A. Cardin

  2. Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA

    Jessica A. Cardin

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  1. Katie A. Ferguson
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  2. Jessica A. Cardin
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Both authors researched data for article, made substantial contributions to discussions of the content, wrote the manuscript and reviewed or edited the manuscript before submission.

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Correspondence to Jessica A. Cardin.

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Nature Reviews Neuroscience thanks C. Angeloni, M. Geffen, J. Reynolds and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Gain modulation

A phenomenon whereby the gain or sensitivity of a neuron to inputs, such as visual stimuli, is altered without changing selectivity.

Input–output (I/O) relationship

The relationship between the inputs a neuron receives (such as synaptic inputs, direct currents or sensory stimulation) and the firing rate responses of that neuron.

Synaptic summation

The summation of synaptic inputs to a neuron either spatially (when nearby synapses are coactive on a dendritic branch) or temporally (when synaptic inputs occur within a short time window mediated by the membrane time constant, τ).

Iceberg effect

An effect whereby, if subthreshold responses to a stimulus are less selective than the neuron’s firing, a linear increase or decrease in activity may alter the neuron’s selectivity by raising or lowering the tuning curve of the neuron across the threshold.

Monocular deprivation

An experimental paradigm in which an animal is deprived of vision from one eye during a critical developmental period. The mature binocular visual cortex then responds predominantly to inputs from the non-deprived eye.

Stochastic resonance

A phenomenon in which the addition of noise non-linearly enhances the information content of a signal, by boosting resonant frequencies over a sensor’s detection threshold (such as a cell’s spike threshold).

Shunting inhibition

A GABAergic synaptic input that minimally affects the membrane potential of a cell that is near the inhibitory synaptic reversal potential, but that leads to a reduction of nearby excitatory postsynaptic potential amplitudes.

Pairwise correlations

A normalized measure of covariation between pairs of neurons that can give insight into their tuning similarity (signal correlations) or shared trial-to-trial variability (noise correlations).

Dendritic saturation

A phenomenon in which an already depolarized dendritic branch shows reduced excitatory responses to temporally correlated excitatory inputs due to reduced driving force.

Synaptic efficacy

The influence that a presynaptic input has on a postsynaptic cell’s probability of firing an action potential.

Adaptation

A decrease in sensitivity to constant or repeated stimuli, leading to reduced stimulus-evoked neural responses over time.

Forward suppression

A rapid form of sensory adaptation whereby the response to a stimulus is reduced when preceded by a stimulus with similar features.

Feedback inhibition

A type of inhibition delivered through recurrent connections: that is, local inhibitory cells target the same population of excitatory cells that drive local inhibitory activity.

Brain states

Spatiotemporal patterns of neural-network activity across the brain that are dynamically regulated by behaviour, the environment and the internal state.

Pupil diameter

The diameter of the pupil of the eye. The diameter is tightly coupled to various emotional and cognitive factors, including global arousal and attention, even when controlling for changes in luminance and depth accommodation.

Attractor dynamics

Temporal patterns that evolve towards a stable state from a large range of starting conditions. Attractor network characterization facilitates the identification of key network properties.

Winner-take-all mechanism

A computational principle in which non-linearities in a recurrent neural network create strong competition between neurons. Only neurons (or sets thereof) with the strongest responses remain active, providing a mechanism for input selection or segregation.

Dimensionality reduction

Reduction of the number of random variables of a system to a smaller set of principal variables to aid analysis.

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Ferguson, K.A., Cardin, J.A. Mechanisms underlying gain modulation in the cortex. Nat Rev Neurosci 21, 80–92 (2020). https://doi.org/10.1038/s41583-019-0253-y

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