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Cortical gamma band synchronization through somatostatin interneurons

Nature Neuroscience volume 20, pages 951–959 (2017)Cite this article

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Abstract

Gamma band rhythms may synchronize distributed cell assemblies to facilitate information transfer within and across brain areas, yet their underlying mechanisms remain hotly debated. Most circuit models postulate that soma-targeting parvalbumin-positive GABAergic neurons are the essential inhibitory neuron subtype necessary for gamma rhythms. Using cell-type-specific optogenetic manipulations in behaving animals, we show that dendrite-targeting somatostatin (SOM) interneurons are critical for a visually induced, context-dependent gamma rhythm in visual cortex. A computational model independently predicts that context-dependent gamma rhythms depend critically on SOM interneurons. Further in vivo experiments show that SOM neurons are required for long-distance coherence across the visual cortex. Taken together, these data establish an alternative mechanism for synchronizing distributed networks in visual cortex. By operating through dendritic and not just somatic inhibition, SOM-mediated oscillations may expand the computational power of gamma rhythms for optimizing the synthesis and storage of visual perceptions.

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Figure 1: Visually induced gamma rhythms in V1 of the awake, running mouse are stimulus-dependent.
Figure 2: Cortical somatostatin interneurons are essential for visually induced gamma rhythms.
Figure 3: A computational model of visually induced gamma oscillations including both PV-type and SOM-type inhibitory neurons.
Figure 4: SOM neuron photostimulation can entrain gamma rhythmicity in vivo and in vitro.
Figure 5: SOM interneurons synchronize distal ensembles in V1 of awake, behaving mice.

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  • 16 June 2017

    In the version of this article initially published online, ref. 14 was given as Saleem, A. et al. On the origin and modulation of narrow-band gamma oscillations in mouse primary visual cortex. Perception 45, abstr. 702 (2016). The correct reference is Saleem, A.B. et al. Subcortical source and modulation of the narrowband gamma oscillation in mouse visual cortex. Neuron 93, 315–322 (2017). The error has been corrected in the print, PDF and HTML versions of this article.

References

  1. Singer, W. & Gray, C.M. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995).

    Article  CAS  PubMed  Google Scholar 

  2. Buzsáki, G. & Draguhn, A. Neuronal oscillations in cortical networks. Science 304, 1926–1929 (2004).

    Article  CAS  PubMed  Google Scholar 

  3. Traub, R.D. & Whittington, M.A. Cortical Oscillations in Health and Disease (Oxford University Press, 2010).

  4. Fries, P. Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annu. Rev. Neurosci. 32, 209–224 (2009).

    Article  CAS  PubMed  Google Scholar 

  5. Whittington, M.A., Cunningham, M.O., LeBeau, F.E.N., Racca, C. & Traub, R.D. Multiple origins of the cortical γ rhythm. Dev. Neurobiol. 71, 92–106 (2011).

    Article  PubMed  Google Scholar 

  6. Fries, P., Nikolić, D. & Singer, W. The gamma cycle. Trends Neurosci. 30, 309–316 (2007).

    Article  CAS  PubMed  Google Scholar 

  7. Womelsdorf, T. et al. Modulation of neuronal interactions through neuronal synchronization. Science 316, 1609–1612 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Bartos, M., Vida, I. & Jonas, P. Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks. Nat. Rev. Neurosci. 8, 45–56 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Buzsáki, G. & Wang, X.J. Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Sohal, V.S., Zhang, F., Yizhar, O. & Deisseroth, K. Parvalbumin neurons and gamma rhythms enhance cortical circuit performance. Nature 459, 698–702 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Cardin, J.A. et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature 459, 663–667 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Adesnik, H., Bruns, W., Taniguchi, H., Huang, Z.J. & Scanziani, M. A neural circuit for spatial summation in visual cortex. Nature 490, 226–231 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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 

  14. Saleem, A.B. et al. Subcortical source and modulation of the narrowband gamma oscillation in mouse visual cortex. Neuron 93, 315–322 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gieselmann, M.A. & Thiele, A. Comparison of spatial integration and surround suppression characteristics in spiking activity and the local field potential in macaque V1. Eur. J. Neurosci. 28, 447–459 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Jia, X., Smith, M.A. & Kohn, A. Stimulus selectivity and spatial coherence of gamma components of the local field potential. J. Neurosci. 31, 9390–9403 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Perry, G., Hamandi, K., Brindley, L.M., Muthukumaraswamy, S.D. & Singh, K.D. The properties of induced gamma oscillations in human visual cortex show individual variability in their dependence on stimulus size. Neuroimage 68, 83–92 (2013).

    Article  PubMed  Google Scholar 

  18. Wang, X.J. & Buzsáki, G. Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J. Neurosci. 16, 6402–6413 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bosman, C.A. et al. Attentional stimulus selection through selective synchronization between monkey visual areas. Neuron 75, 875–888 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Jia, X., Xing, D. & Kohn, A. No consistent relationship between gamma power and peak frequency in macaque primary visual cortex. J. Neurosci. 33, 17–25 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gray, C.M., König, P., Engel, A.K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).

    Article  CAS  PubMed  Google Scholar 

  22. Biederlack, J. et al. Brightness induction: rate enhancement and neuronal synchronization as complementary codes. Neuron 52, 1073–1083 (2006).

    Article  CAS  PubMed  Google Scholar 

  23. Perrenoud, Q., Pennartz, C.M.A. & Gentet, L.J. Membrane potential dynamics of spontaneous and visually evoked gamma activity in V1 of awake mice. PLoS Biol. 14, e1002383 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Jagadeesh, B., Gray, C.M. & Ferster, D. Visually evoked oscillations of membrane potential in cells of cat visual cortex. Science 257, 552–554 (1992).

    Article  CAS  PubMed  Google Scholar 

  25. Wilson, H.R. & Cowan, J.D. Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24 (1972).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Jadi, M.P. & Sejnowski, T.J. Cortical oscillations arise from contextual interactions that regulate sparse coding. Proc. Natl. Acad. Sci. USA 111, 6780–6785 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jadi, M.P. & Sejnowski, T.J. Regulating cortical oscillations in an inhibition-stabilized network. Proc. IEEE Inst. Electr. Electron. Eng. 102, http://dx.doi.org/10.1109/JPROC.2014.2313113 (2014).

  28. 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 

  29. Gilbert, C.D. & Wiesel, T.N. Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci. 9, 2432–2442 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 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 

  31. 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 

  32. Colgin, L.L. et al. Frequency of gamma oscillations routes flow of information in the hippocampus. Nature 462, 353–357 (2009).

    Article  CAS  PubMed  Google Scholar 

  33. Neuenschwander, S. & Singer, W. Long-range synchronization of oscillatory light responses in the cat retina and lateral geniculate nucleus. Nature 379, 728–732 (1996).

    Article  CAS  PubMed  Google Scholar 

  34. Koepsell, K. et al. Retinal oscillations carry visual information to cortex. Front. Syst. Neurosci. 3, 4 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Storchi, R. et al. Modulation of fast narrowband oscillations in the mouse retina and dLGN according to background light intensity. Neuron 93, 299–307 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ray, S. & Maunsell, J.H.R. Differences in gamma frequencies across visual cortex restrict their possible use in computation. Neuron 67, 885–896 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Thiele, A. & Stoner, G. Neuronal synchrony does not correlate with motion coherence in cortical area MT. Nature 421, 366–370 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Shadlen, M.N. & Movshon, J.A. Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77, 111–125 (1999).

    Article  CAS  PubMed  Google Scholar 

  40. Larkum, M.E., Nevian, T., Sandler, M., Polsky, A. & Schiller, J. Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle. Science 325, 756–760 (2009).

    Article  CAS  PubMed  Google Scholar 

  41. Smith, S.L., Smith, I.T., Branco, T. & Häusser, M. Dendritic spikes enhance stimulus selectivity in cortical neurons in vivo. Nature 503, 115–120 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Losonczy, A., Makara, J.K. & Magee, J.C. Compartmentalized dendritic plasticity and input feature storage in neurons. Nature 452, 436–441 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Murayama, M. et al. Dendritic encoding of sensory stimuli controlled by deep cortical interneurons. Nature 457, 1137–1141 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Hilscher, M.M. et al. Chrna2-Martinotti cells synchronize layer 5 type A pyramidal cells via rebound excitation. PLoS Biology 15, e2001392 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Zhang, S. et al. Selective attention. Long-range and local circuits for top-down modulation of visual cortex processing. Science 345, 660–665 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Lepousez, G., Mouret, A., Loudes, C., Epelbaum, J. & Viollet, C. Somatostatin contributes to in vivo gamma oscillation modulation and odor discrimination in the olfactory bulb. J. Neurosci. 30, 870–875 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Uhlhaas, P.J. & Singer, W. Abnormal neural oscillations and synchrony in schizophrenia. Nat. Rev. Neurosci. 11, 100–113 10 (2010).

    Article  CAS  PubMed  Google Scholar 

  48. Jadi, M.P., Behrens, M.M. & Sejnowski, T.J. Abnormal gamma oscillations in N-methyl-D-aspartate receptor hypofunction models of schizophrenia. Biol. Psychiatry 79, 716–726 (2016).

    Article  CAS  PubMed  Google Scholar 

  49. Hamm, J.P. & Yuste, R. Somatostatin interneurons control a key component of mismatch negativity in mouse visual cortex. Cell Rep. 16, 597–604 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lin, L.C. & Sibille, E. Reduced brain somatostatin in mood disorders: a common pathophysiological substrate and drug target? Front. Pharmacol. 4, 110 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  PubMed  Google Scholar 

  52. Mitra, P. & Bokil, H. Observed Brain Dynamics (Oxford University Press, 2008).

  53. Hill, D.N., Mehta, S.B. & Kleinfeld, D. Quality metrics to accompany spike sorting of extracellular signals. J. Neurosci. 31, 8699–8705 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Muñoz, W., Tremblay, R. & Rudy, B. Channelrhodopsin-assisted patching: in vivo recording of genetically and morphologically identified neurons throughout the brain. Cell Rep. 9, 2304–2316 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Ma, Y., Hu, H., Berrebi, A.S., Mathers, P.H. & Agmon, A. Distinct subtypes of somatostatin-containing neocortical interneurons revealed in transgenic mice. J. Neurosci. 26, 5069–5082 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pluta, S. et al. A direct translaminar inhibitory circuit tunes cortical output. Nat. Neurosci. 18, 1631–1640 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Vinck, M., van Wingerden, M., Womelsdorf, T., Fries, P. & Pennartz, C.M. The pairwise phase consistency: a bias-free measure of rhythmic neuronal synchronization. Neuroimage 51, 112–122 (2010).

    Article  PubMed  Google Scholar 

  58. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.M. FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intel. Neurosc. http://dx.doi.org/10.1155/2011/156869 (2011).

  59. Adesnik, H. & Scanziani, M. Lateral competition for cortical space by layer-specific horizontal circuits. Nature 464, 1155–1160 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Bokil, H., Andrews, P., Kulkarni, J.E., Mehta, S. & Mitra, P.P. Chronux: a platform for analyzing neural signals. J. Neurosci. Methods 192, 146–151 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  61. 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 

  62. 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 

  63. 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 

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Acknowledgements

The authors thank D. Taylor for technical assistance, K. Chesnov and D. Abdelhalim for help with histology, and J. Isaacson, M. Feller, D. Feldman, Y. Dan and B. Atallah for comments on the manuscript. This work was supported by NEI grant R01EY023756-01. H.A. is a New York Stem Cell Foundation – Robertson Investigator. This work was supported by The New York Stem Cell Foundation. J.V. was supported by grants from the Swiss National Foundation (P300PA_164719 and P2FRP3_155172). M.P.J. was supported by NEI grant 5K99EY025026-02 and Howard Hughes Medical Institute (HHMI). T.J.S. was supported by HHMI.

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Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, USA

    Julia Veit, Richard Hakim & Hillel Adesnik

  2. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA

    Julia Veit, Richard Hakim & Hillel Adesnik

  3. Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, USA

    Monika P Jadi & Terrence J Sejnowski

  4. Division of Biological Sciences, University of California at San Diego, La Jolla, California, USA

    Monika P Jadi & Terrence J Sejnowski

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Contributions

H.A., J.V. and M.P.J. conceived of the study. J.V. performed all in vivo extracellular experiments; H.A. performed the in vivo patch-clamp experiments; and R.H. performed all brain slice experiments. M.P.J. conceived of the computational model and carried out all simulations. T.J.S. provided guidance to the computational study.

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Correspondence to Hillel Adesnik.

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Integrated supplementary information

Supplementary Figure 1 Features of narrowband high and low gamma power in awake mice.

a) Plot of normalized high gamma power versus visual stimulus size (n = 25 mice, significant effect of size on center-frequency power, p < 0.001, Kruskal-Wallis ANOVA). Error bars are s.e.m. b) As for a) but for increasing visual stimulus contrast and fixed size of 12 degrees (n = 8 mice, significant effect of contrast on center-frequency power, p = 0.002, Kruskal-Wallis ANOVA). c) As for a) but for increasing luminance at 0% contrast (significant effect of luminance on high gamma power, p = 0.003, Kruskal-Wallis ANOVA, n = 4 mice). d) Example power spectra for increasing luminance and 0% contrast. e) Histogram of the center frequency of the narrowband high gamma peak across 25 mice. f) Representative power spectrum in a mouse while running and while still, in the absence of any visual stimulus (grey screen). Line width denotes mean ± s.e.m. g) Plot of the high gamma power between running and still conditions (p < 0.001, n = 22 mice, signed rank test). h) Example power spectra for visually-induced gamma across several stimulus sizes demonstrated a decrease in peak frequency with grating diameter. i) Population data for peak gamma frequency versus stimulus size (p = 0.014, n = 32 mice, significant effect of stimulus size on peak frequency, Kruskal-Wallis ANOVA, error bars are s.e.m).

Supplementary Figure 2 Preferred gamma phases of the spiking of different cell types.

a) Histogram of mean spike phases for each RS cell during stimulation with the largest size grating. 0˚ and 180˚ represent the peak and trough of the oscillation, respectively. The red arrow signifies the resultant vector length. The number denotes the circular mean of the distribution in degrees. b) Same as a) but for PV/FS cells. c) Same as a) but for SOM cells.

Supplementary Figure 3 Impact of SOM neuron suppression on cortical dynamics.

a) Example image of a V1 brain section from a SOM-Cre;Rosa-LSL-tdtomato mouse injected with a Cre-dependent AAV virus driving eNpHR3.0-YFP. Scale bar is 130μm. b) Scatter plots of gamma power in control conditions (black) and during optogenetic suppression of SOM cells. From left to right: relative gamma power (n = 14 mice, p < 0.001), peak/trough power (n = 13 mice, p = 0.03), absolute power when the illumination light turned on after the visual stimulus (n = 14 mice, p < 0.001), absolute power when the light turned on before the visual stimulus (n = 7 mice, p = 0.02), and absolute power in the high gamma band ~60Hz (n = 11 mice, p = 0.08). All tests: Wilcoxon signed rank test. c) Example PSTH from a putative SOM neuron expressing eNpHR3.0 in the absence (black) and presence (red) of light. d) Population average change of the power spectrum in SOM-Cre mice during optogenetic suppression. Upper and lower edge of filled area denote mean ± standard error (n = 14 mice). e) Scatter plot of the peak frequency of the visually-driven gamma rhythm and the peak frequency of the change in gamma power during SOM neuron optogenetic suppression (r = 0.78, p < 0.001, n = 14 mice). Data points have been jittered with Gaussian noise for display purposes only to avoid overlap. f) Average pairwise phase consistency spectrum for FS units in SOM-Cre mice in the absence (black) and presence (red) of light, n = 31 cells, thickness of line denotes mean ± standard error.

Supplementary Figure 4 Impact of PV neuron suppression on cortical dynamics.

a) Example image of a V1 brain section from a PV-Cre;Rosa-LSL-tdtomato mouse injected with a Cre-dependent AAV virus driving eNpHR3.0-YFP. Scale bar is 130μm. b) Scatter plots of gamma power in control conditions (black) and during optogenetic suppression of PV cells. From left to right: relative power (n = 18 mice, p = 0.06), peak/trough power (n = 18 mice, p = 0.95), absolute power when the illumination light turned on after the visual stimulus (n = 18 mice, p < 0.001), absolute power when the light turned on before the visual stimulus (n = 8 mice, p = 0.008), and absolute power in the high gamma band ~60Hz (n = 13 mice, p = 0.008). All tests: Wilcoxon signed rank test. c) Average pairwise phase consistency spectrum for RS units in PV-Cre mice in the absence (black) and presence (red) of light, n=105, thickness of line denotes mean ± standard error. d) Example PSTH of a putative eNpHR3.0-expressing FS/PV unit during visual stimulation in the absence (black) and presence (red) of light. e) Population average change of the power spectrum in PV-Cre mice during optogenetic suppression. Upper and lower edge of filled area denote mean ± standard error (n = 18 mice). f) Histogram of the optogenetic modulation index (OMI) for isolated regular spiking, putative excitatory units in SOM-Cre;eNpHR3.0 mice. g) Dose response curve for the change in absolute visually induced gamma power in four different conditions as a function of light intensity. Yellow: SOM-Cre;eNpHR3.0 mice; Light green: PV-Cre;eNpHR3.0 mice; Dark green: PV-Cre:eArCH3.0 mice; black: control mice not expressing any opsin. Error bars are s.e.m. h) As in g) but for induced gamma power. i) Example power spectra of the visual responses to a large grating with (red) and without (black) light in a SOM-Cre mouse not expressing eNpHR3.0. Thickness of line denotes mean ± standard error. j) Left: Example power spectra form a PV-CRE:eArCH3.0 mouse under control (black) and illumination (red). Right: Example LFP traces during visual stimulation with a large grating under control (black) and during illumination (red). Thickness of line denotes mean ± standard error. k) As in j), but in another mouse.

Supplementary Figure 5 Impact of SOM suppression on firing rates and synaptic currents in the model and in vivo.

a) Model prediction for the simulated optogenetic modulation index of PCs for various levels of SOM suppression with increasing surround levels in the model. b) As in a) but for PV neurons during SOM suppression. c) Histogram of the optogenetic modulation index (OMI) for isolated regular spiking, putative excitatory units in SOM-Cre;eNpHR3.0 mice. d) As in c) but for fast spiking units. e) Model prediction for the firing rate of PCs (black), PVs (green) and SOMs (yellow) as a function of total gamma power in the model with the standard parameters (same plot as Fig 3d). f) As in e) but the connection from SOM to PV has been set to zero. g) Example single trial traces of IPSCs in the absence (black) and presence (red) of light to suppress SOM cells. h) Average power spectra for IPSCs in control and photo-suppression conditions for the example cell in e).

Supplementary Figure 6 Impact of brain state and anesthesia.

a) Photo-stimulation of PV neurons in V1 of awake (black, n = 6 mice, same curve as Fig 4b), V1 of isoflurane-anesthetized (gray, n = 4 mice) and S1 of isoflurane anesthetized (blue, n = 3 mice) PV-Cre mice. Anesthesia shifts the peak resonant frequency (p<0.001, significant effect of anesthesia on fold increase (F = 10.01, d.f. = 1) and p<0.001 frequency-anesthesia interaction (F = 7.54, d.f. = 10), 2-way-ANOVA) but the curves for the different brain regions both peak at 48Hz (p=0.44, for effect of brain region on fold increase, 2-way-ANOVA, F = 0.6, d.f. = 1). Error bars denote s.e.m. b) Scatter plot of the absolute peak gamma power for optogenetic suppression of SOM and PV neurons in awake, but non-running conditions (SOM n = 11 mice, p < 0.001; PV: n = 13 mice, p = 0.001, Wilcoxon signed rank test). c) Scatter plot of the fold change of optogenetic suppression for SOM and PV mice comparing running and quiescence conditions.

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Veit, J., Hakim, R., Jadi, M. et al. Cortical gamma band synchronization through somatostatin interneurons. Nat Neurosci 20, 951–959 (2017). https://doi.org/10.1038/nn.4562

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