Skip to main content

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

  • Article
  • Published:

Recurrent interactions in local cortical circuits

Nature volume 579, pages 256–259 (2020)Cite this article

Subjects

Abstract

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations1,2,3,4. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average5,6. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes5,6,7, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually8. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity7 and photoablating9,10 neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10–20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Ablation effect in simulated cortical L2/3 network depends on excitatory connectivity.
Fig. 2: Touch networks of the barrel cortex are sensitive to ablation.
Fig. 3: Effect of ablations depends on response similarity to ablated cells.

Similar content being viewed by others

Data availability

Data can be found at CRCNS (http://crcns.org/) at https://doi.org/10.6080/K0Z31WWG.

Code availability

Code for the simulations can be found at https://github.com/jwittenbach/ablation-sim. Code used for data analysis can be found at https://github.com/peronlab/ablation.

References

  1. Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. & Suarez, H. H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995).

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Chance, F. S., Nelson, S. B. & Abbott, L. F. Complex cells as cortically amplified simple cells. Nat. Neurosci. 2, 277–282 (1999).

    Article  CAS  PubMed  Google Scholar 

  3. Christie, I. K., Miller, P. & Van Hooser, S. D. Cortical amplification models of experience-dependent development of selective columns and response sparsification. J. Neurophysiol. 118, 874–893 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Miller, K. D. Canonical computations of cerebral cortex. Curr. Opin. Neurobiol. 37, 75–84 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Cossell, L. et al. Functional organization of excitatory synaptic strength in primary visual cortex. Nature 518, 399–403 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lee, W. C. et al. Anatomy and function of an excitatory network in the visual cortex. Nature 532, 370–374 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. Peron, S. P., Freeman, J., Iyer, V., Guo, C. & Svoboda, K. A cellular resolution map of barrel cortex activity during tactile behavior. Neuron 86, 783–799 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Peron, S. & Svoboda, K. From cudgel to scalpel: toward precise neural control with optogenetics. Nat. Methods 8, 30–34 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. König, K., Becker, T. W., Fischer, P., Riemann, I. & Halbhuber, K. J. Pulse-length dependence of cellular response to intense near-infrared laser pulses in multiphoton microscopes. Opt. Lett. 24, 113–115 (1999).

    Article  ADS  PubMed  Google Scholar 

  10. Vladimirov, N. et al. Brain-wide circuit interrogation at the cellular level guided by online analysis of neuronal function. Nat. Methods 15, 1117–1125 (2018).

    Article  CAS  PubMed  Google Scholar 

  11. Rubin, D. B., Van Hooser, S. D. & Miller, K. D. The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex. Neuron 85, 402–417 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Harris, K. D. & Mrsic-Flogel, T. D. Cortical connectivity and sensory coding. Nature 503, 51–58 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  13. Mateo, C. et al. In vivo optogenetic stimulation of neocortical excitatory neurons drives brain-state-dependent inhibition. Curr. Biol. 21, 1593–1602 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Barrett, D. G., Denève, S. & Machens, C. K. Optimal compensation for neuron loss. eLife 5, e12454 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Avermann, M., Tomm, C., Mateo, C., Gerstner, W. & Petersen, C. C. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J. Neurophysiol. 107, 3116–3134 (2012).

    Article  CAS  PubMed  Google Scholar 

  16. Lefort, S., Tomm, C., Floyd Sarria, J. C. & Petersen, C. C. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex. Neuron 61, 301–316 (2009).

    Article  CAS  PubMed  Google Scholar 

  17. Crochet, S., Poulet, J. F., Kremer, Y. & Petersen, C. C. Synaptic mechanisms underlying sparse coding of active touch. Neuron 69, 1160–1175 (2011).

    Article  CAS  PubMed  Google Scholar 

  18. Hires, S. A., Gutnisky, D. A., Yu, J., O’Connor, D. H. & Svoboda, K. Low-noise encoding of active touch by layer 4 in the somatosensory cortex. eLife 4, (2015). https://doi.org/10.7554/eLife.06619

  19. Litwin-Kumar, A. & Doiron, B. Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat. Neurosci. 15, 1498–1505 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Chettih, S. N. & Harvey, C. D. Single-neuron perturbations reveal feature-specific competition in V1. Nature 567, 334–340 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  21. Holmgren, C., Harkany, T., Svennenfors, B. & Zilberter, Y. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. J. Physiol. (Lond.) 551, 139–153 (2003).

    Article  CAS  Google Scholar 

  22. Curtis, J. C. & Kleinfeld, D. Phase-to-rate transformations encode touch in cortical neurons of a scanning sensorimotor system. Nat. Neurosci. 12, 492–501 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Marshel, J. H. et al. Cortical layer-specific critical dynamics triggering perception. Science 365, eaaw5202 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Carrillo-Reid, L., Han, S., Yang, W., Akrouh, A. & Yuste, R. Controlling visually guided behavior by holographic recalling of cortical ensembles. Cell 178, 447–457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Lien, A. D. & Scanziani, M. Tuned thalamic excitation is amplified by visual cortical circuits. Nat. Neurosci. 16, 1315–1323 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Daie, K., Svoboda, K. & Druckmann, S. Targeted photostimulation uncovers circuit motifs supporting short-term memory. Preprint at https://www.bioRxiv.org/content/10.1101/623785v1 (2019).

  27. Hansel, D. & van Vreeswijk, C. The mechanism of orientation selectivity in primary visual cortex without a functional map. J. Neurosci. 32, 4049–4064 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li, N., Daie, K., Svoboda, K. & Druckmann, S. Robust neuronal dynamics in premotor cortex during motor planning. Nature 532, 459–464 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Petersen, C. C. & Crochet, S. Synaptic computation and sensory processing in neocortical layer 2/3. Neuron 78, 28–48 (2013).

    Article  CAS  PubMed  Google Scholar 

  30. Petersen, C. C. H. Sensorimotor processing in the rodent barrel cortex. Nat. Rev. Neurosci. 20, 533–546 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Brette, R. et al. Simulation of networks of spiking neurons: a review of tools and strategies. J. Comput. Neurosci. 23, 349–398 (2007).

    Article  MathSciNet  PubMed  PubMed Central  Google Scholar 

  32. Bureau, I., von Saint Paul, F. & Svoboda, K. Interdigitated paralemniscal and lemniscal pathways in the mouse barrel cortex. PLoS Biol. 4, e382 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Xue, M., Atallah, B. V. & Scanziani, M. Equalizing excitation-inhibition ratios across visual cortical neurons. Nature 511, 596–600 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gentet, L. J. et al. Unique functional properties of somatostatin-expressing GABAergic neurons in mouse barrel cortex. Nat. Neurosci. 15, 607–612 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Yu, J., Gutnisky, D. A., Hires, S. A. & Svoboda, K. Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation. Nat. Neurosci. 19, 1647–1657 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Yu, J., Hu, H., Agmon, A. & Svoboda, K. Recruitment of GABAergic interneurons in the barrel cortex during active tactile behavior. Neuron 104, 412–427 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Stimberg, M., Brette, R. & Goodman, D. F. Brian 2, an intuitive and efficient neural simulator. eLife 8, e47314 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Gorski, J. A. et al. Cortical excitatory neurons and glia, but not GABAergic neurons, are produced in the Emx1-expressing lineage. J. Neurosci. 22, 6309–6314 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  40. Daigle, T. L. et al. A suite of transgenic driver and reporter mouse lines with enhanced brain-cell-type targeting and functionality. Cell 174, 465–480 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Guo, Z. V. et al. Procedures for behavioral experiments in head-fixed mice. PLoS ONE 9, e88678 (2014).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  42. O’Connor, D. H. et al. Vibrissa-based object localization in head-fixed mice. J. Neurosci. 30, 1947–1967 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Guo, Z. V. et al. Flow of cortical activity underlying a tactile decision in mice. Neuron 81, 179–194 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Clack, N. G. et al. Automated tracking of whiskers in videos of head fixed rodents. PLoS Comput. Biol. 8, e1002591 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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 

  46. Huber, D. et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature 484, 473–478 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ahrens, M. B., Paninski, L. & Sahani, M. Inferring input nonlinearities in neural encoding models. Network 19, 35–67 (2008).

    Article  MathSciNet  PubMed  Google Scholar 

  48. Orger, M. B., Kampff, A. R., Severi, K. E., Bollmann, J. H. & Engert, F. Control of visually guided behavior by distinct populations of spinal projection neurons. Nat. Neurosci. 11, 327–333 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Allegra Mascaro, A. L., Sacconi, L. & Pavone, F. S. Multi-photon nanosurgery in live brain. Front. Neuroenergetics 2, 21 (2010).

    PubMed  Google Scholar 

  50. Canty, A. J. et al. In-vivo single neuron axotomy triggers axon regeneration to restore synaptic density in specific cortical circuits. Nat. Commun. 4, 2038 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank S. Druckmann, S. Romani, D. Gutnisky, N. Li, J. Yu, H. Inagaki, N. Sofroniew and M. Economo for comments on the manuscript, A. Hu for histology, and H. Zeng for the Ai162 transgenic mice. Funding was provided by the Howard Hughes Medical Institute. R.P. was supported by the National Institutes of Health (NIH) T32GM007308.

Author information

Author notes

  1. Simon Peron

    Present address: Center for Neural Science, New York University, New York, NY, USA

  2. H. Freyja Ólafsdóttir

    Present address: Donders Institute for Brain Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands

Authors and Affiliations

  1. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Simon Peron, Jason D. Wittenbach, H. Freyja Ólafsdóttir, Jeremy Freeman & Karel Svoboda

  2. Center for Neural Science, New York University, New York, NY, USA

    Ravi Pancholi & Bettina Voelcker

  3. Department of Cell and Developmental Biology, University College London, London, UK

    H. Freyja Ólafsdóttir

Authors
  1. Simon Peron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Pancholi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bettina Voelcker
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jason D. Wittenbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. H. Freyja Ólafsdóttir
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeremy Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karel Svoboda
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.P. and K.S. conceived the project. S.P., R.P. and B.V. performed the experiments, with assistance from H.F.O. J.D.W. performed the modelling, with input from J.F., S.P. and K.S. S.P., R.P., B.V., J.D.W., J.F. and K.S. analysed data and wrote the paper.

Corresponding author

Correspondence to Simon Peron.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended data figures and tables

Extended Data Fig. 1 Effect of ablation in a simulated network with hyper-connectivity (that is, Pconn > 0.4; here, Pconn = 0.44).

Model network responses aligned to input (arrow, bottom). Raster plots show a subset of neurons from an example network. PSTHs show averages across all neurons and networks. Bottom, excitatory neurons within subnetwork. Middle, excitatory neurons outside subnetwork. Top, inhibitory neurons. Left, network response before ablation of 10% of the subnetwork neurons; right, response after ablation.

Extended Data Fig. 2 Multi-photon ablation protocol.

a, Example field of view immediately before (left) and after (right) ablation. Orange arrow denotes target neuron. b, Ablation protocol. Power (orange, top trace) during ablation epochs (100 ms; increased power, orange, top) gradually increased, and the PMT shutter was closed (black bars). During the intervening evaluation epochs, power was lower and constant (orange, top), and the PMT shutter was open. Ablation was terminated when GCaMP fluorescence at the target neuron (green) jumped (orange arrow). c, Success of ablation as a function of neuron depth for all experiments included in this dataset. Individual points give mean success rate for given depth bin; bin size, 25 μm. d, Depth dependence of total energy deposition for successful ablations (successfully ablated cells only: n = 293 cells across 22 sessions, 14 mice; ablations from j250220 and j257218, along with 7 additional ablations from other mice were excluded owing to incomplete logging). Grey dots denote individual ablations. Black dots, means across 25-μm bins. e, As in d, but for peak power needed for ablation (n = 293 cells across 22 sessions, 14 mice).

Extended Data Fig. 3 Multi-photon ablation produces spatially localized effects.

a, Change in calcium event rate (Methods) as a function of minimal distance to an ablated neuron after silent cell ablation. Individual neurons appear as grey points, with dark grey dashed lines showing single mouse averages and the dark black line showing the cross-mouse (n = 8 silent ablation mice) average. Event rates among neurons adjacent to ablated silent cells did not change (event rate before ablation: 0.014 ± 0.008 Hz, grand median ± adjusted MAD; after ablation: 0.014 ± 0.007 Hz; P = 0.055 before versus after ablation, Wilcoxon signed-rank test, paired medians across n = 8 mice; 1,028 neurons across all mice) (Methods). b, Confocal ex vivo image from a mouse perfused 24 h after ablation. Ablation sites are indicated with dashed white circles. Green, GCaMP6s fluorescence; red, mCherry fluorescence; blue, microglial antibody IBA1 fluorescence. c, As in b, but blue shows immunoreactivity for the astrocytic marker GFAP. d, The spatial extent of glial reaction was measured by detecting the fastest intensity decline ridge (dashed white line) in the glial antibody image along lines emanating from the ablation centre at varied angles (Methods). Top, ridge along ablation from b. Bottom left, intensity image in angle-distance space within which the ridge was measured. Bottom right, distribution of reaction radii; all points constitute IBA1 labelling, as no detectable glial reactions were observed with GFAP: 8 out of 10 IBA1-labelled and 0 out of 6 GFAP-labelled ablations retrieved histologically revealed a detectable glial reaction (Methods). These reactions had radii of 11.7 ± 1.7 μm (mean ± s.d.; n = 8 sites). e, Two-photon in vivo images before (left) and after (right) a successful ablation (target, white dotted line). Green, GCaMP6s fluorescence; red, mCherry fluorescence. f, As in e, but after a failure of the ablation protocol to terminate the ablation. Excess energy deposition produced a large lesion (black, centre of image). White arrows denote corresponding points in the two images. Mice (n = 5) with such lesions were excluded from the study.

Extended Data Fig. 4 Effect of ablation on L2/3 model sensory representation increases with the number of ablated neurons.

Ablation effect (change in Rstimulus) as a function of the degree of touch representation degradation (net Rstimulus across ablated neurons). In all cases, we used increased subnetwork connectivity (0.4) (Fig. 1, Methods). The nablated neurons with the highest encoding score were selected for ablation. Grey circles denote individual networks. Black dots denote, median across n = 30 simulated networks for a given number of ablated neurons, indicated in the plot. Beyond nablated > 50, we observed instability, presumably because we did not attempt to restore excitatory-inhibitory balance after ablation; these data were omitted. Correlation of net Rstimulus ablated and ΔRstimulus: R = −0.65, P < 0.01.

Extended Data Fig. 5 Ablation of touch neurons does not produce distance-dependent effects in the whisking representation.

Proximal (15–35 μm to nearest ablated cell) change in Ρwhisking: 0.000 ± 0.192 (grand median ± adjusted MAD); distal (115–135 μm): −0.023 ± 0.186. P value given for Wilcoxon signed-rank test comparing proximal paired to distal (n = 9 mice). Legend as in Fig. 2m.

Extended Data Fig. 6 Behaviour does not account for ablation effects.

a, Pre-touch (dark blue) and post-touch (light blue) distributions of neuron ablation for whisker angle (θ), angle at touch (θ at touch), and net curvature change across all touches (net Δκ at touch) in an example mouse. P values from a Kolmogorov–Smirnov test comparing the distribution of a variable before and after ablation in individual mice. No mice showed a significant (P < 0.05) change in any of the three parameters. b, Fraction of correct trials (left) and number of touches (right) before and after ablation of touch neurons. P values from a Wilcoxon signed-rank test, paired by mouse (n = 9 mice). c, d, As in a and b, but for ablations of silent neurons (n = 8 mice). e, f, As in a and b, but for ablations of whisking neurons (n = 7 mice).

Extended Data Fig. 7 Example effects of ablations of whisking and silent neurons.

a, Example ablation of a whisking neuron. Left, example maps for touch (top, blue) and whisking (bottom, green) cells before ablation. Sphere size corresponds to Rtouch (top) or Rwhisking (bottom) values. Grey dots denote other neurons. These maps exclude the ablated neurons, the position of which is indicated by a faint orange background. Centre, Rtouch (top) and Rwhisking (bottom) values for the ablated population. Right, Rtouch (top) and Rwhisking (bottom) values after ablation, with ablated neurons again excluded. b, As in a, but for ablation of silent neurons.

Extended Data Fig. 8 Simulation of ablation of whisking neurons produces representation degradation in networks with increased, but not equal subnetwork connectivity.

a, Whisking input was simulated by using input with a peak time of 50 ms (bottom), in contrast to 10 ms for touch (top) (Fig. 1). b, Model network responses aligned to input before ablation. Left to right, increasing subnetwork connectivity. Bottom to top, raster plots (each showing a subset of neurons from an example network) and PSTHs (averaged across all neurons and networks) for the three neuronal populations in the model (Fig. 1a, Methods). c, As in b, but after ablation. Thin PSTHs are before ablation. Change in encoding score after ablation: from 0.143 ± 0.024 to 0.104 ± 0.013, grand median ± adjusted MAD. P < 0.001, Wilcoxon signed-rank test paired by network (n = 30 networks).

Extended Data Table 1 Individual mice are listed with the number of neurons imaged, the type(s) of ablation(s) performed, and the number of neurons ablated
Full size table

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peron, S., Pancholi, R., Voelcker, B. et al. Recurrent interactions in local cortical circuits. Nature 579, 256–259 (2020). https://doi.org/10.1038/s41586-020-2062-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2062-x

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

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

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