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Orientation selectivity and the functional clustering of synaptic inputs in primary visual cortex

Nature Neuroscience volume 19, pages 1003–1009 (2016)Cite this article

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Abstract

The majority of neurons in primary visual cortex are tuned for stimulus orientation, but the factors that account for the range of orientation selectivities exhibited by cortical neurons remain unclear. To address this issue, we used in vivo two-photon calcium imaging to characterize the orientation tuning and spatial arrangement of synaptic inputs to the dendritic spines of individual pyramidal neurons in layer 2/3 of ferret visual cortex. The summed synaptic input to individual neurons reliably predicted the neuron's orientation preference, but did not account for differences in orientation selectivity among neurons. These differences reflected a robust input–output nonlinearity that could not be explained by spike threshold alone and was strongly correlated with the spatial clustering of co-tuned synaptic inputs within the dendritic field. Dendritic branches with more co-tuned synaptic clusters exhibited greater rates of local dendritic calcium events, supporting a prominent role for functional clustering of synaptic inputs in dendritic nonlinearities that shape orientation selectivity.

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Figure 1: Response properties of orientation-selective synaptic inputs to neurons in the ferret visual cortex.
Figure 2: Summed spine responses match somatic orientation preference, but are weakly orientation selective.
Figure 3: Location in orientation preference map reliably predicts somatic orientation preference but not somatic or synaptic input selectivity.
Figure 4: Nonlinear synaptic integration in visual cortical neurons.
Figure 5: Functional clustering predicts dendritic nonlinearities and somatic orientation selectivity.
Figure 6: Functional clustering and dendritic hotspots.

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References

  1. Goris, R.L., Simoncelli, E.P. & Movshon, J.A. Origin and function of tuning diversity in macaque visual cortex. Neuron 88, 819–831 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ringach, D.L., Shapley, R.M. & Hawken, M.J. Orientation selectivity in macaque V1: diversity and laminar dependence. J. Neurosci. 22, 5639–5651 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tan, A.Y., Brown, B.D., Scholl, B., Mohanty, D. & Priebe, N.J. Orientation selectivity of synaptic input to neurons in mouse and cat primary visual cortex. J. Neurosci. 31, 12339–12350 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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 

  5. Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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 

  7. Bosking, W.H., Zhang, Y., Schofield, B. & Fitzpatrick, D. Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Malach, R., Amir, Y., Harel, M. & Grinvald, A. Relationship between intrinsic connections and functional architecture revealed by optical imaging and in vivo targeted biocytin injections in primate striate cortex. Proc. Natl. Acad. Sci. USA 90, 10469–10473 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Nauhaus, I., Benucci, A., Carandini, M. & Ringach, D.L. Neuronal selectivity and local map structure in visual cortex. Neuron 57, 673–679 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Priebe, N.J. & Ferster, D. Mechanisms of neuronal computation in mammalian visual cortex. Neuron 75, 194–208 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Losonczy, A. & Magee, J.C. Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50, 291–307 (2006).

    Article  CAS  PubMed  Google Scholar 

  12. Branco, T. & Häusser, M. Synaptic integration gradients in single cortical pyramidal cell dendrites. Neuron 69, 885–892 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Schiller, J., Major, G., Koester, H.J. & Schiller, Y. NMDA spikes in basal dendrites of cortical pyramidal neurons. Nature 404, 285–289 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. 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 

  15. Palmer, L.M. et al. NMDA spikes enhance action potential generation during sensory input. Nat. Neurosci. 17, 383–390 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Lavzin, M., Rapoport, S., Polsky, A., Garion, L. & Schiller, J. Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo. Nature 490, 397–401 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Kleindienst, T., Winnubst, J., Roth-Alpermann, C., Bonhoeffer, T. & Lohmann, C. Activity-dependent clustering of functional synaptic inputs on developing hippocampal dendrites. Neuron 72, 1012–1024 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Takahashi, N. et al. Locally synchronized synaptic inputs. Science 335, 353–356 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Chen, X., Leischner, U., Rochefort, N.L., Nelken, I. & Konnerth, A. Functional mapping of single spines in cortical neurons in vivo. Nature 475, 501–505 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Chen, T.W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Jia, H., Rochefort, N.L., Chen, X. & Konnerth, A. Dendritic organization of sensory input to cortical neurons in vivo. Nature 464, 1307–1312 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Weliky, M., Bosking, W.H. & Fitzpatrick, D. A systematic map of direction preference in primary visual cortex. Nature 379, 725–728 (1996).

    Article  CAS  PubMed  Google Scholar 

  23. Ohki, K. et al. Highly ordered arrangement of single neurons in orientation pinwheels. Nature 442, 925–928 (2006).

    Article  CAS  PubMed  Google Scholar 

  24. Ikezoe, K., Mori, Y., Kitamura, K., Tamura, H. & Fujita, I. Relationship between the local structure of orientation map and the strength of orientation tuning of neurons in monkey V1: a 2-photon calcium imaging study. J. Neurosci. 33, 16818–16827 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Priebe, N.J. & Ferster, D. Inhibition, spike threshold, and stimulus selectivity in primary visual cortex. Neuron 57, 482–497 (2008).

    Article  CAS  PubMed  Google Scholar 

  26. 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 

  27. Schummers, J., Mariño, J. & Sur, M. Synaptic integration by V1 neurons depends on location within the orientation map. Neuron 36, 969–978 (2002).

    Article  CAS  PubMed  Google Scholar 

  28. Tan, A.Y., Chen, Y., Scholl, B., Seidemann, E. & Priebe, N.J. Sensory stimulation shifts visual cortex from synchronous to asynchronous states. Nature 509, 226–229 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mel, B.W., Ruderman, D.L. & Archie, K.A. Translation-invariant orientation tuning in visual “complex” cells could derive from intradendritic computations. J. Neurosci. 18, 4325–4334 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Poirazi, P., Brannon, T. & Mel, B.W. Pyramidal neuron as two-layer neural network. Neuron 37, 989–999 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Poirazi, P. & Mel, B.W. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29, 779–796 (2001).

    Article  CAS  PubMed  Google Scholar 

  32. Poirazi, P., Brannon, T. & Mel, B.W. Arithmetic of subthreshold synaptic summation in a model CA1 pyramidal cell. Neuron 37, 977–987 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Hofer, S.B. et al. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nat. Neurosci. 14, 1045–1052 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Maldonado, P.E., Gödecke, I., Gray, C.M. & Bonhoeffer, T. Orientation selectivity in pinwheel centers in cat striate cortex. Science 276, 1551–1555 (1997).

    Article  CAS  PubMed  Google Scholar 

  35. Gambino, F. et al. Sensory-evoked LTP driven by dendritic plateau potentials in vivo. Nature 515, 116–119 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Greenberg, D.S., Houweling, A.R. & Kerr, J.N. Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nat. Neurosci. 11, 749–751 (2008).

    Article  CAS  PubMed  Google Scholar 

  37. Constantinople, C.M. & Bruno, R.M. Effects and mechanisms of wakefulness on local cortical networks. Neuron 69, 1061–1068 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sellers, K.K., Bennett, D.V., Hutt, A. & Fröhlich, F. Anesthesia differentially modulates spontaneous network dynamics by cortical area and layer. J. Neurophysiol. 110, 2739–2751 (2013).

    Article  PubMed  Google Scholar 

  39. Branco, T., Clark, B.A. & Häusser, M. Dendritic discrimination of temporal input sequences in cortical neurons. Science 329, 1671–1675 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Pologruto, T.A., Sabatini, B.L. & Svoboda, K. ScanImage: flexible software for operating laser scanning microscopes. Biomed. Eng. Online 2, 13 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Peirce, J.W. PsychoPy–Psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using microManager. Curr. Protoc. Mol. Biol. Ch. 14, Unit 14 20 (2010).

  43. Nauhaus, I., Nielsen, K.J., Disney, A.A. & Callaway, E.M. Orthogonal micro-organization of orientation and spatial frequency in primate primary visual cortex. Nat. Neurosci. 15, 1683–1690 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mardia, K.V. & Jupp, P.E. Directional Statistics (Wiley, West Sussex, UK, 2000).

  45. Kalatsky, V.A. & Stryker, M.P. New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38, 529–545 (2003).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors would like to thank the GENIE project for providing GCaMP6, D. Ouimet for surgical assistance, and G. Smith and W. Bosking for discussions. The research was supported by NIH grant EY011488 (D.F.) and by the Max Planck Florida Institute for Neuroscience.

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Authors and Affiliations

  1. Max Planck Florida Institute for Neuroscience, Jupiter, Florida, USA

    Daniel E Wilson, David E Whitney, Benjamin Scholl & David Fitzpatrick

  2. Integrative Biology and Neuroscience Graduate Program, Florida Atlantic University, Jupiter, Florida, USA

    Daniel E Wilson

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Contributions

D.E. Wilson performed surgical procedures and two photon imaging, D.E. Wilson and D.E. Whitney performed intrinsic signal imaging, and D.E. Wilson and B.S. performed whole-cell recordings. All authors analyzed data. D.E. Wilson and D.F. wrote the paper with input from B.S. and D.E. Whitney.

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Correspondence to David Fitzpatrick.

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Integrated supplementary information

Supplementary Figure 1 Backpropagating action potential subtraction

(a) Example dendritic fluorescence trace; (b) Raw spine fluorescence trace plotted with predicted bAP component of spine signal; (c) Predicted synaptic signal after subtraction of predicted bAP.

Supplementary Figure 2 Examples of spine-isolated calcium transients

(a) Single calcium events occur and can be restricted to individual spines; scale bar 5 microns; (b) Responses recorded from four spines and adjacent parent dendritic branches illustrating that spine Ca2+ transients occur independent of dendritic events

Supplementary Figure 3 Precise alignment of two-photon imaging with intrinsic signal imaging

(a) Cranial window with two-photon FOV indicated by dashed white line, with control points indicated by red dots; Scale bar: 500 μm; (b) Intrinsic imaging orientation preference map; (c): Control point (red dots) placement guided by fiduciary markers; (d) Z-Projection of pia through 30 μm of two-photon z-stack; (e) Local orientation preference map.

Supplementary Figure 4 Spine orientation preference weakly matches map orientation preference

Comparison of single spine orientation preference with that of the spine’s location in the orientation preference map across all imaged neurons

Supplementary Figure 5 Computation of spine–soma input–output functions

Left column shows normalized summed spine responses with threshold applied; responses are aligned to the preferred orientation and Gaussian fit is shown as a dashed line; Middle column shows normalized somatic responses; responses are aligned to the preferred orientation and Gaussian fit is shown as a dashed line; Right column shows the spine-soma input output function, computed as interpolated fits of the spine input versus somatic output

Supplementary Figure 6 Spiking orientation selectivity is greater than Vm orientation selectivity

Comparison of spiking with subthreshold orientation selectivity; spiking and subthreshold measurements collected from each neuron are connected by a line

Supplementary Figure 7 Additional clustering analysis

(a) Nearest neighboring spines (n=765) show similar orientation preferences; (b) Apical(n=74) and basal (n= 72) dendritic branches show similar levels of circular dispersion; box boundaries are 25th and 75th percentile values and whiskers are maxima and minima; (c) The circular dispersion values for all branches on each cell are plotted against somatic orientation selectivity; each column of points is a single cell; cells ordered on the x-axis by their orientation selectivity; dashed line indicates cutoff for clustering;

Supplementary Figure 8 Hotspot data processing

(a) Example of isolated hotspot event without uniform dendritic activation; (b) Example of hotspot event before (black) and after (red) correction for uniform (putative bAP) dendritic contamination (dashed line); (c) Hotspot spatial size is not significantly different between events with small and large uniform dendritic activation; box boundaries indicate 25th and 75th percentiles, whiskers are minima and values adjacent to 1.5*IQR+75th percentiles

Supplementary Figure 9 Hotspots predict somatic orientation tuning

Summed spine, hotspot, and somatic tuning curves (dashed lines), spine-soma I/O functions (blue), and hotspot-soma I/O functions (red) for all imaged neurons, as in Supplementary Figure 5

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Wilson, D., Whitney, D., Scholl, B. et al. Orientation selectivity and the functional clustering of synaptic inputs in primary visual cortex. Nat Neurosci 19, 1003–1009 (2016). https://doi.org/10.1038/nn.4323

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