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Classification of electrophysiological and morphological neuron types in the mouse visual cortex


Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

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Data availability

Electrophysiological and morphological data supporting the findings of this study are available in the Allen Cell Types Database, Morphological data are also available through the repository59 at

Code availability

The custom electrophysiology data acquisition software (MIES) is available at The Vaa3D morphological reconstruction software, including the Mozak extension, is freely available at and its code is available at The code for electrophysiological and morphological feature analysis is available as part of the open-source Allen SDK repository ( and IPFX repository ( The clustering analysis code will be made available at


  1. 1.

    Zeng, H. & Sanes, J. R. Neuronal cell-type classification: challenges, opportunities and the path forward. Nat. Rev. Neurosci. 18, 530–546 (2017).

  2. 2.

    Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).

  3. 3.

    Harris, K. D. & Shepherd, G. M. G. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

  4. 4.

    Lodato, S. & Arlotta, P. Generating neuronal diversity in the mammalian cerebral cortex. Annu. Rev. Cell Dev. Biol. 31, 699–720 (2015).

  5. 5.

    Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).

  6. 6.

    Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

  7. 7.

    Ascoli, G. A. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).

  8. 8.

    Druckmann, S., Hill, S., Schürmann, F., Markram, H. & Segev, I. A hierarchical structure of cortical interneuron electrical diversity revealed by automated statistical analysis. Cereb. Cortex 23, 2994–3006 (2013).

  9. 9.

    Bortone, D. S., Olsen, S. R. & Scanziani, M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex. Neuron 82, 474–485 (2014).

  10. 10.

    Kim, E. J., Juavinett, A. L., Kyubwa, E. M., Jacobs, M. W. & Callaway, E. M. Three types of cortical layer 5 neurons that differ in brain-wide connectivity and function. Neuron 88, 1253–1267 (2015).

  11. 11.

    Dehorter, N. et al. Tuning of fast-spiking interneuron properties by an activity-dependent transcriptional switch. Science 349, 1216–1220 (2015).

  12. 12.

    Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

  13. 13.

    Zeisel, A. et al. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

  14. 14.

    Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).

  15. 15.

    Peng, H., Ruan, Z., Long, F., Simpson, J. H. & Myers, E. W. V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nat. Biotechnol. 28, 348–353 (2010).

  16. 16.

    Harris, J. A. et al. Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation. Front. Neural Circuits 8, 76 (2014).

  17. 17.

    Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

  18. 18.

    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.e22 (2018).

  19. 19.

    Bernard, A., Sorensen, S. A. & Lein, E. S. Shifting the paradigm: new approaches for characterizing and classifying neurons. Curr. Opin. Neurobiol. 19, 530–536 (2009).

  20. 20.

    Zou, H., Hastie, T. & Tibshirani, R. Sparse principal component analysis. J. Comput. Graph. Stat. 15, 265–286 (2006).

  21. 21.

    Baden, T. et al. The functional diversity of retinal ganglion cells in the mouse. Nature 529, 345–350 (2016).

  22. 22.

    Baudry, J.-P., Raftery, A. E., Celeux, G., Lo, K. & Gottardo, R. Combining mixture components for clustering. J. Comput. Graph. Stat. 19, 332–353 (2010).

  23. 23.

    Hennig, C. Cluster-wise assessment of cluster stability. Comput. Stat. Data Anal. 52, 258–271 (2007).

  24. 24.

    van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

  25. 25.

    Gonchar, Y., Wang, Q. & Burkhalter, A. Multiple distinct subtypes of GABAergic neurons in mouse visual cortex identified by triple immunostaining. Front. Neuroanat. 1, 3 (2008).

  26. 26.

    von Engelhardt, J., Eliava, M., Meyer, A. H., Rozov, A. & Monyer, H. Functional characterization of intrinsic cholinergic interneurons in the cortex. J. Neurosci. 27, 5633–5642 (2007).

  27. 27.

    James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning. (Springer New York, 2013).

  28. 28.

    Breiman, L., Friedman, J., Stone, C. J. & Olshen, R. A. Classification and Regression Trees. (Chapman & Hall/CRC, 1984).

  29. 29.

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

  30. 30.

    Oberlaender, M. et al. Cell type–specific three-dimensional structure of thalamocortical circuits in a column of rat vibrissal cortex. Cereb. Cortex 22, 2375–2391 (2012).

  31. 31.

    Hattox, A. M. & Nelson, S. B. Layer V neurons in mouse cortex projecting to different targets have distinct physiological properties. J. Neurophysiol. 98, 3330–3340 (2007).

  32. 32.

    Schubert, D., Kötter, R., Zilles, K., Luhmann, H. J. & StaigerJ. F.. Cell type-specific circuits of cortical layer IV spiny neurons. J. Neurosci. 23, 2961–2970 (2003).

  33. 33.

    Scorcioni, R., Polavaram, S. & Ascoli, G. A. L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 3, 866–876 (2008).

  34. 34.

    Deitcher, Y. et al. Comprehensive morpho-electrotonic analysis shows 2 distinct classes of L2 and L3 pyramidal neurons in human temporal cortex. Cereb. Cortex 27, 5398–5414 (2017).

  35. 35.

    Egger, V., Nevian, T. & Bruno, R. M. Subcolumnar dendritic and axonal organization of spiny stellate and star pyramid neurons within a barrel in rat somatosensory cortex. Cereb. Cortex 18, 876–889 (2008).

  36. 36.

    Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018).

  37. 37.

    Kanari, L. et al. Objective classification of neocortical pyramidal cells. Cereb. Cortex 29, 1719–1735 (2019).

  38. 38.

    Toledo-Rodriguez, M. Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb. Cortex 14, 1310–1327 (2004).

  39. 39.

    He, M. et al. Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex. Neuron 92, 555 (2016).

  40. 40.

    Paul, A. et al. Transcriptional architecture of synaptic communication delineates GABAergic neuron identity. Cell 171, 522–539.e20 (2017).

  41. 41.

    Tebaykin, D. et al. Modeling sources of interlaboratory variability in electrophysiological properties of mammalian neurons. J. Neurophysiol. 119, 1329–1339 (2018).

  42. 42.

    Bean, B. P. The action potential in mammalian central neurons. Nat. Rev. Neurosci. 8, 451–465 (2007).

  43. 43.

    Zhang, L. et al. Whole-cell recording of the Ca2+-dependent slow afterhyperpolarization in hippocampal neurones: effects of internally applied anions. Pflug. Arch. 426, 247–253 (1994).

  44. 44.

    Kaczorowski, C. C., Disterhoft, J. & Spruston, N. Stability and plasticity of intrinsic membrane properties in hippocampal CA1 pyramidal neurons: effects of internal anions. J. Physiol. 578, 799–818 (2007).

  45. 45.

    Teeter, C. et al. Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9, 709 (2018).

  46. 46.

    Gouwens, N. W. et al. Systematic generation of biophysically detailed models for diverse cortical neuron types. Nat. Commun. 9, 710 (2018).

  47. 47.

    Arkhipov, A. et al. Visual physiology of the layer 4 cortical circuit in silico. PLoS Comput. Biol. 14, e1006535 (2018).

  48. 48.

    Stockton, D. B. & Santamaria, F. Integrating the Allen Brain Institute cell types database into automated neuroscience workflow. Neuroinformatics 15, 333–342 (2017).

  49. 49.

    Tripathy, S. J. et al. Transcriptomic correlates of neuron electrophysiological diversity. PLoS Comput. Biol. 13, e1005814 (2017).

  50. 50.

    Neher, E. Correction for liquid junction potentials in patch clamp experiments. Methods Enzymol. 207, 123–131 (1992).

  51. 51.

    Killick, R., Fearnhead, P. & Eckley, I. A. Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107, 1590–1598 (2012).

  52. 52.

    Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity (Springer Berlin Heidelberg, 1998).

  53. 53.

    Peng, H., Bria, A., Zhou, Z., Iannello, G. & Long, F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat. Protoc. 9, 193–208 (2014).

  54. 54.

    Zhou, Z., Sorensen, S., Zeng, H., Hawrylycz, M. & Peng, H. Adaptive image enhancement for tracing 3D morphologies of neurons and brain vasculatures. Neuroinformatics 13, 153–166 (2015).

  55. 55.

    Zhou, Z., Liu, X., Long, B. & Peng, H. TReMAP: automatic 3D neuron reconstruction based on tracing, reverse mapping and assembling of 2D projections. Neuroinformatics 14, 41–50 (2016).

  56. 56.

    Bria, A., Iannello, G., Onofri, L. & Peng, H. TeraFly: real-time three-dimensional visualization and annotation of terabytes of multidimensional volumetric images. Nat. Methods 13, 192–194 (2016).

  57. 57.

    Langfelder, P., Zhang, B. & Horvath, S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24, 719–720 (2008).

  58. 58.

    Boldog, E. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat. Neurosci. 21, 1185–1195 (2018).

  59. 59.

    Ascoli, G. A., Donohue, D. E. & Halavi, M. NeuroMorpho.Org: a central resource for neuronal morphologies. J. Neurosci. 27, 9247–9251 (2007).

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The authors thank Z. Popovich for creating the Mozak custom user interface for the 3D reconstruction software Vaa3D and the Mozak citizen-scientists who contributed to the reconstruction work. They also thank B. Berg, S. Coulter, C. Dang, and A. Jones for leadership and guidance. This work was funded by the Allen Institute for Brain Science. The study was also supported by award number U01MH105982 to H.Z. from the National Institute of Mental Health and Eunice Kennedy Shriver National Institute of Child Health & Human Development, parts of the National Institutes of Health. The content in this story and the associated study do not necessarily represent the official views of the NIH. We dedicate this paper to the vision, encouragement, and long-term support of our founder, Paul G. Allen.

Author information

H.Z. and C.K. conceptualized the study. T.L.D., B.T., T.N.N., and E.G. contributed to the generation and/or characterization of specific transgenic mouse lines. J.H., M. Garwood, M.R., and N.B provided mouse colony management. N. Dee, S.P., N. Taskin, T.C., M.K., J.S., K.C., H.T., and E.B. prepared tissue slices. A.O., D.H., K.H., S.J., Li. N., L.K., and R.M. performed electrophysiology experiments. T.L., M.M., K. Brouner, A.D., C.H., D.P., A.G., T.E., H.G., and K. Bickley processed slices for biocytin staining. S.C., C.C., M. Gorham, S.D., N. Dotson, K.N., and L.P. imaged biocytin-stained slices and cells. S.A.S., T.D., M.F., A.H., D.S., N. Thatra, R.D., G.W., A.M., R.A.d.F., S.-L.D., and S.K. reconstructed neurons and/or provided anatomical annotations. N.W.G., X.L., C.L., A. Budzillo, J.B., S.A.S., K.G. and K.E.L. performed analyses. J.B., A.O., J.T., B. Lee, P.C., S.A.S., and N. Dee contributed to method development studies. H.P., Z.Z., B. Long, C.F., J.P., C.S., M.S., D.R., T.B., A. Budzillo, D.C., K.E.L., and T.J. designed, wrote, or built tools for pipeline data generation. S.M.S. provided program management support. J.W.P., C.K., H.Z., A. Bernard, J.B., T.L., M.M., N.G., P.R.N., L.P., S.A.S., N. Dee, and S.P. organized and managed pipeline data generation. N.W.G., K.G., Ly. N., W.W., R.Y., D.F., and A.S. organized and managed pipeline data storage and processing. N.W.G., C.A.A., A.A., S.M., H.P., C.T., M.J.H., J.B., T.J., G.S.-L., J.T., B. Lee, G.J.M., E.L., J.W.P., C.K., H.Z., A. Bernard, S.A.S., J.A.H., and B.T. provided scientific direction. N.W.G., C.L., J.B., and S.A.S. prepared the figures. N.W.G., J.B., and S.A.S. wrote the manuscript in consultation with all authors. H.Z., C.K., and A.A. provided substantial review and edits to the manuscript.

Correspondence to Hongkui Zeng.

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The authors declare no competing interests.

Additional information

Journal peer review information: Nature Neuroscience thanks Ruben Armananzas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Supplementary information

Supplementary Figures 1–34, Supplementary Tables 1–4, and Supplementary Note.

Reporting Summary

Supplementary Dataset 1

Feature comparisons between m-types. Morphological features were assessed to identify statistically significant differences between groups using the two-tailed Student’s t-test. The P values were then adjusted for multiple testing using the two-stage Benjamini–Hochberg step-up procedure to control the false discovery rate. The sample sizes for each group are given in the attached spreadsheet.

Supplementary Dataset 2

Transgenic lines used in the study. Inventory of transgenic lines used in this study with additional information including their sources and repository details.

Supplementary Dataset 3

Type classifications and morphological adjustment parameters by cell. The e-types, m-types, and me-types are provided for each cell (when applicable). Cells are listed by ‘specimen ID’, which are the identifiers used in the Allen Cell Types Database ( The morphological parameters listed can be used to adjust the original reconstructions (provided online) for z-shrinkage and slice angle as described in Methods and Supplementary Fig. 33.

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Fig. 1: A pipeline to generate and analyze standardized morpho-electric data at scale.
Fig. 2: Classification of electrophysiological properties.
Fig. 3: Unsupervised classification of spiny neurons into morphological types.
Fig. 4: Unsupervised classification of aspiny neurons into morphological types.
Fig. 5: Classification using paired electrophysiological and morphological data.
Fig. 6: Transcriptomic subclasses and me-types.
Fig. 7: Correspondence of me-types with transcriptomic types.