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Structure and function of a neocortical synapse

Abstract

In 1986, electron microscopy was used to reconstruct by hand the entire nervous system of a roundworm, the nematode Caenorhabditis elegans1. Since this landmark study, high-throughput electron-microscopic techniques have enabled reconstructions of much larger mammalian brain circuits at synaptic resolution2,3. Nevertheless, it remains unknown how the structure of a synapse relates to its physiological transmission strength—a key limitation for inferring brain function from neuronal wiring diagrams. Here we combine slice electrophysiology of synaptically connected pyramidal neurons in the mouse somatosensory cortex with correlated light microscopy and high-resolution electron microscopy of all putative synaptic contacts between the recorded neurons. We find a linear relationship between synapse size and strength, providing the missing link in assigning physiological weights to synapses reconstructed from electron microscopy. Quantal analysis also reveals that synapses contain at least 2.7 neurotransmitter-release sites on average. This challenges existing release models and provides further evidence that neocortical synapses operate with multivesicular release4,5,6, suggesting that they are more complex computational devices than thought, and therefore expanding the computational power of the canonical cortical microcircuitry.

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Fig. 1: Electrophysiology and ultrastructure of the same synapses.
Fig. 2: At most light-microscopy contacts, no synapses are formed between recorded neurons.
Fig. 3: Synapse size predicts synaptic transmission strength.
Fig. 4: Synapses between L2/3 pyramidal neurons operate with multivesicular release.

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Data availability

A preprint of this study was published on bioRxiv (https://doi.org/10.1101/2019.12.13.875971). Source data underlying all graphical representations are provided with the paper. Further data generated here are available from corresponding author upon request. Source data are provided with this paper.

Code availability

Custom-written code is available on GitHub (https://github.com/gschuhknecht/SynapseStructureFunction).

References

  1. White, J. G., Southgate, E., Thomson, J. N. & Brenner, S. The structure of the nervous system of the nematode Caenorhabditis elegans. Phil. Trans. Royal Soc. B 314, 1–340 (1986).

    ADS  CAS  Google Scholar 

  2. Kasthuri, N. et al. Saturated reconstruction of a volume of neocortex. Cell 162, 648–661 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Motta, A. et al. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 366, eaay3134 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Loebel, A. et al. Multiquantal release underlies the distribution of synaptic efficacies in the neocortex. Front. Comput. Neurosci. 3, 27 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Huang, C.-H., Bao, J. & Sakaba, T. Multivesicular release differentiates the reliability of synaptic transmission between the visual cortex and the somatosensory cortex. J. Neurosci. 30, 11994–12004 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rollenhagen, A. et al. Structural properties of synaptic transmission and temporal dynamics at excitatory layer 5B synapses in the adult rat somatosensory cortex. Front. Synaptic Neurosci. 10, 24 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Del Castillo, J. & Katz, B. Quantal components of the end-plate potential. J. Physiol. 124, 560–573 (1954).

    Article  PubMed Central  Google Scholar 

  8. Larkman, A., Hannay, T., Stratford, K. & Jack, J. Presynaptic release probability influences the locus of long-term potentiation. Nature 360, 70–73 (1992).

    Article  ADS  CAS  PubMed  Google Scholar 

  9. Nusser, Z. et al. Cell type and pathway dependence of synaptic AMPA receptor number and variability in the hippocampus. Neuron 21, 545–559 (1998).

    Article  CAS  PubMed  Google Scholar 

  10. Kharazia, V. N. & Weinberg, R. J. Immunogold localization of AMPA and NMDA receptors in somatic sensory cortex of albino rat. J. Comp. Neurol. 412, 292–302 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Harris, K. M., Jensen, F. E. & Tsao, B. Three-dimensional structure of dendritic spines and synapses in rat hippocampus (CA1) at postnatal day 15 and adult ages: implications for the maturation of synaptic physiology and long-term potentiation. J. Neurosci. 12, 2685–2705 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Arellano, J. I., Benavides-Piccione, R., DeFelipe, J. & Yuste, R. Ultrastructure of dendritic spines: correlation between synaptic and spine morphologies. Front. Neurosci. 1, 131–143 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bopp, R., Holler-Rickauer, S., Martin, K. A. C. & Schuhknecht, G. F. P. An ultrastructural study of the thalamic input to layer 4 of primary motor and primary somatosensory cortex in the mouse. J. Neurosci. 37, 2435–2448 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Matsuzaki, M., Honkura, N., Ellis-Davies, G. C. R. & Kasai, H. Structural basis of long-term potentiation in single dendritic spines. Nature 429, 761–766 (2004).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. Holtmaat, A. & Svoboda, K. Experience-dependent structural synaptic plasticity in the mammalian brain. Nat. Rev. Neurosci. 10, 647–658 (2009).

    Article  CAS  PubMed  Google Scholar 

  16. Jovanic, T. et al. Competitive disinhibition mediates behavioral choice and sequences in Drosophila. Cell 167, 858–870 (2016).

    Article  CAS  PubMed  Google Scholar 

  17. Silver, R. A., Lübke, J., Sakmann, B. & Feldmeyer, D. High-probability uniquantal transmission at excitatory synapses in barrel cortex. Science 302, 1981–1984 (2003).

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Hardingham, N. R. et al. Quantal analysis reveals a functional correlation between presynaptic and postsynaptic efficacy in excitatory connections from rat neocortex. J. Neurosci. 30, 1441–1451 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rudolph, S., Tsai, M.-C., von Gersdorff, H. & Wadiche, J. I. The ubiquitous nature of multivesicular release. Trends Neurosci. 38, 428–438 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Frerking, M. & Wilson, M. Saturation of postsynaptic receptors at central synapses? Curr. Opin. Neurobiol. 6, 395–403 (1996).

    Article  CAS  PubMed  Google Scholar 

  21. da Costa, N. M. & Martin, K. A. C. Selective targeting of the dendrites of corticothalamic cells by thalamic afferents in area 17 of the cat. J. Neurosci. 29, 13919–13928 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Markram, H., Lübke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. 500, 409–440 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Feldmeyer, D., Lübke, J. & Sakmann, B. Efficacy and connectivity of intracolumnar pairs of layer 2/3 pyramidal cells in the barrel cortex of juvenile rats. J. Physiol. 575, 583–602 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nat. Neurosci. 7, 621–627 (2004).

    Article  CAS  PubMed  Google Scholar 

  25. Larkum, M. E., Waters, J., Sakmann, B. & Helmchen, F. Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. J. Neurosci. 27, 8999–9008 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ulrich, D. & Stricker, C. Dendrosomatic voltage and charge transfer in rat neocortical pyramidal cells in vitro. J. Neurophysiol. 84, 1445–1452 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Jaffe, D. B. & Carnevale, N. T. Passive normalization of synaptic integration influenced by dendritic architecture. J. Neurophysiol. 82, 3268–3285 (1999).

    Article  CAS  PubMed  Google Scholar 

  28. Araya, R., Vogels, T. P. & Yuste, R. Activity-dependent dendritic spine neck changes are correlated with synaptic strength. Proc. Natl Acad. Sci. USA 111, E2895–E2904 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Harris, K. M. & Stevens, J. K. Dendritic spines of CA1 pyramidal cells in the rat hippocampus: serial electron microscopy with reference to their biophysical characteristics. J. Neurosci. 9, 2982–2997 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Nair, D. et al. Super-resolution imaging reveals that AMPA receptors inside synapses are dynamically organized in nanodomains regulated by PSD95. J. Neurosci. 33, 13204–13224 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tang, A.-H. et al. A trans-synaptic nanocolumn aligns neurotransmitter release to receptors. Nature 536, 210–214 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Raghavachari, S. & Lisman, J. E. Properties of quantal transmission at CA1 synapses. J. Neurophysiol. 92, 2456–2467 (2004).

    Article  Google Scholar 

  33. Taschenberger, H., Scheuss, V. & Neher, E. Release kinetics, quantal parameters and their modulation during short-term depression at a developing synapse in the rat CNS. J. Physiol. 568, 513–537 (2005).

    Article  Google Scholar 

  34. Holderith, N. et al. Release probability of hippocampal glutamatergic terminals scales with the size of the active zone. Nat. Neurosci. 15, 988–997 (2012).

    Article  Google Scholar 

  35. Stepanyants, A., Hof, P. R. & Chklovskii, D. B. Geometry and structural plasticity of synaptic connectivity. Neuron 34, 275–288 (2002).

    Article  Google Scholar 

  36. Shepherd, G. M., Stepanyants, A., Bureau, I., Chklovskii, D. & Svoboda, K. Geometric and functional organization of cortical circuits. Nat. Neurosci. 8, 782–790 (2005).

    Article  Google Scholar 

  37. Reimann, M. W., King, J. G., Muller, E. B., Ramaswamy, S. & Markram, H. An algorithm to predict the connectome of neural microcircuits. Front. Comput. Neurosci. 9, 120 (2015).

    Google Scholar 

  38. Wahl, L. M., Pouzat, C. & Stratford, K. J. Monte Carlo simulation of fast excitatory synaptic transmission at a hippocampal synapse. J. Neurophysiol. 75, 597–608 (1996).

    Article  CAS  PubMed  Google Scholar 

  39. Emes, R. D. & Grant, S. G. N. Evolution of synapse complexity and diversity. Annu. Rev. Neurosci. 35, 111–131 (2012).

    Article  CAS  PubMed  Google Scholar 

  40. Oertner, T. G., Sabatini, B. L., Nimchinsky, E. A. & Svoboda, K. Facilitation at single synapses probed with optical quantal analysis. Nat. Neurosci. 5, 657–664 (2002).

    Article  CAS  PubMed  Google Scholar 

  41. Tong, G. & Jahr, C. E. Multivesicular release from excitatory synapses of cultured hippocampal neurons. Neuron 12, 51–59 (1994).

    Article  CAS  PubMed  Google Scholar 

  42. Haas, K. T. et al. Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency. eLife 7, e31755 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hardingham, N. R. et al. Extracellular calcium regulates postsynaptic efficacy through group 1 metabotropic glutamate receptors. J. Neurosci. 26, 6337–6345 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Hardingham, N. R., Hardingham, G. E., Fox, K. D. & Jack, J. J. B. Presynaptic efficacy directs normalization of synaptic strength in layer 2/3 rat neocortex after paired activity. J. Neurophysiol. 97, 2965–2975 (2007).

    Article  PubMed  Google Scholar 

  45. Hestrin, S., Sah, P. & Nicoll, R. A. Mechanisms generating the time course of dual component excitatory synaptic currents recorded in hippocampal slices. Neuron 5, 247–253 (1990).

    Article  CAS  PubMed  Google Scholar 

  46. Guzman, S. J., Schlögl, A. & Schmidt-Hieber, C. Stimfit: quantifying electrophysiological data with Python. Front. Neuroinform. 8, 16 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Cardona, A. et al. TrakEM2 software for neural circuit reconstruction. PLoS ONE 7, e38011 (2012).

    Article  ADS  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  48. da Costa, N. M. & Martin, K. A. C. The proportion of synapses formed by the axons of the lateral geniculate nucleus in layer 4 of area 17 of the cat. J. Comp. Neurol. 516, 264–276 (2009).

    Article  PubMed  Google Scholar 

  49. Nevian, T., Larkum, M. E., Polsky, A. & Schiller, J. Properties of basal dendrites of layer 5 pyramidal neurons: a direct patch-clamp recording study. Nat. Neurosci. 10, 206–214 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  51. 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  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Ferrarese, L. et al. Dendrite-specific amplification of weak synaptic input during network activity in vivo. Cell Rep. 24, 3455–3465 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stuart, G. & Spruston, N. Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. J. Neurosci. 18, 3501–3510 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Ujfalussy, B. B., Makara, J. K., Lengyel, M. & Branco, T. Global and multiplexed dendritic computations under in vivo-like conditions. Neuron 100, 579–592 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Koch, C. Biophysics of Computation: Information Processing in Single Neurons. (Oxford Univ. Press, 2004).

  57. Jack, J. J. B., Kullmann, D. M., Larkman, A. U., Major, G. & Stratford, K. J. Quantal analysis of excitatory synaptic mechanisms in the mammalian central nervous system. Cold Spring Harb. Symp. Quant. Biol. 55, 57–67 (1990).

    Article  CAS  PubMed  Google Scholar 

  58. Jack, J. J., Larkman, A. U., Major, G. & Stratford, K. J. Quantal analysis of the synaptic excitation of CA1 hippocampal pyramidal cells. Adv. Second Messenger Phosphoprotein Res. 29, 275–299 (1994).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank O. Ohana for training and sharing expertise in patch-clamp recordings; S. Solinas for assistance with NEURON simulations; S. Soldado-Magraner, A. Huber and J. C. Anderson for constant support; and M. Buchholz and F. Engert for comments on the manuscript. As members of the Institute of Neuroinformatics, we are signatories of the Basel Declaration. This work was supported by the Swiss Society for Neuroscience and the Neuroscience Center Zurich (to G.F.P.S.) and by funding from the University of Zurich (to K.A.C.M.).

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Authors and Affiliations

Authors

Contributions

Authors are listed alphabetically. K.A.C.M. and G.F.P.S. designed experiments; G.F.P.S. performed electrophysiology experiments; S.H., G.K. and G.F.P.S. performed histology, light-microscopic reconstructions, and correlated light and electron microscopy; G.K. and G.F.P.S. performed PSD reconstructions; G.F.P.S. and K.J.S. analysed electrophysiology data; G.F.P.S. and K.J.S. developed SMAQ; K.A.C.M. and K.J.S. supervised the work; K.A.C.M., G.F.P.S. and K.J.S. wrote the paper.

Corresponding author

Correspondence to Gregor F. P. Schuhknecht.

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The authors declare no competing interests.

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Peer review information Nature thanks Tianyi Mao, Andreas Tolias and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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 Complete light-microscopic reconstruction of presynaptic axons and postsynaptic dendrites of connected neurons preserved in the 300-μm slice.

a, Full 3D light-microscopy reconstruction of a recurrently connected pair; an axon of the blue cell formed one synapse with a dendrite of the black cell; axons of the black cell formed two synapses with dendrites of the blue cell and three non-synaptic contacts; arrows show regions of interest illustrated in subsequent panels; cortical surface indicated. b, Side-projection of the same reconstruction, showing how the acute slice was resliced into four sections (indicated); regions of interest are highlighted as in a. c, Alignment of sections 1 and 2. All neurites that were cut during reslicing (arrowheads) could be aligned between sections. Note that the soma of the black neuron was cut during reslicing (arrows), with the subsequent alignment (right) indicating that there was virtually no tissue loss during reslicing. d, e, Neurites terminating at the top of section 1 (that is, cut during acute slice preparation for electrophysiology). Note that neurites that were cut during acute slice preparation ended in typical biocytin-filled blebs at the cut surface (arrowheads). d, The axon of the blue neuron terminates at the surface of section 1 (arrowhead, membrane bleb at slice surface). e, Dendrite of the black neuron terminating at the surface of section 1 (arrowhead, membrane bleb at slice surface). f, g, For experiments to be included in the final data set, neurites that terminated within sections needed to end in well labelled terminals; when neurites showed signs of incomplete biocytin filling, as in h, i (for example, ‘fading out’ within sections), experiments were discarded. f, Axon of black cell terminating in well labelled bouton in the middle of section. g, Dendrite of the blue cell terminating in the middle of the section in a number of dendritic spines emerging from the dendritic shaft. h, i, Examples of incompletely labelled neurites in an experiment that was discarded (a different experiment to that shown in ag). h, In incompletely labelled dendrite segments (arrowheads), dendritic shafts appeared faint and spines were not filled; arrow, dendritic labelling fading completely, rendering dendrite untraceable. Panels show different dendrites in the same experiment. i, The biocytin label fades in an axon (arrowheads), while interspersed boutons are densely labelled; arrow, axonal label fading completely, rendering the axon untraceable.

Extended Data Fig. 2 Tilt-series and colour-inverted micrographs facilitate reconstruction of synaptic ultrastructure in biocytin-filled dendritic spines.

ac, Identification and reconstruction of synaptic ultrastructure following slice recordings (synapse 1 in Fig. 1). a, Tilted and inverted micrographs for three cross-sections through a synaptic contact. Left column, synaptic specializations were often ambiguous in untilted micrographs. Centre left column, collage of micrographs from sections that were physically tilted inside the electron microscope (tilt angle indicated) facilitated the detection of synaptic specializations. Centre right, tilted micrographs from the centre left column were colour-inverted, highlighting subtle contrast differences and aiding identification of the PSD (arrows omitted for clarity, except arrowheads indicating the PSD). Right column, reconstruction of presynaptic and postsynaptic morphologies with aid from tilted and inverted micrographs. b, En-face representation (2D projection) of the reconstructed PSD from a. c, 3D electron-microscopic reconstruction of the complete synaptic contact shown in a, b, with the same colour scheme as in a; black, dendritic spine head. df, Identification and reconstruction of synaptic ultrastructure for a different synapse (synapse from a different experiment to that shown in ac); panel layout analogous to that in ac. d, Left column, untilted micrographs did not permit clear identification of the synaptic cleft and PSD. Centre left column, tilted micrographs revealed the synaptic cleft and PSD. Centre right column, inversion of tilted micrographs facilitated identification of the PSD (arrows omitted for clarity, except for arrowheads indicating the PSD). Right column, reconstruction of presynaptic and postsynaptic morphologies with help from tilted and inverted micrographs. e, En-face representation (2D projection) of the reconstructed PSD from d. f, 3D electron-microscopic reconstruction of the complete synaptic contact shown in e, f; same colour scheme as in d.

Extended Data Fig. 3 Overview across the ten connected neuron pairs, showing important anatomical features of recovered synapses and corresponding EPSPs.

ac, Catalogue showing distances of recorded synapses on dendrites, reconstructed PSDs, and recorded EPSP waveforms. Experiments sorted by decreasing mean EPSP amplitude. a, Dendritic distances from the identified synapses to the soma. The collapsed dendritic tree is represented schematically ranging from the soma (bottom) to a distance of 250 μm (top, cut off); dendritic distances at which respective synapses were found are indicated by spine locations (grey balls and sticks). No distinction was made between apical and basal dendrites; all synapses were formed on dendritic spines, except the proximal synapse in experiment 10, which was formed on a dendritic shaft (indicated). b, Morphologies of reconstructed PSDs. En-face representations (2D projections) of PSDs highlight the ranges of identified sizes and shapes. PSDs were positioned next to spines according to their respective dendritic distances as indicated in a. See scale bar at bottom right; asterisks indicate PSDs with complex morphologies. c, Waveforms of evoked EPSP recordings. Black, average waveform of EPSP recordings during stable epochs for each connection; grey, ten randomly selected single-trial EPSP waveforms taken during stable epochs of recording. d, EPSP rise times correlate with mean dendritic distances of identified synapses to soma. Shown are the correlation coefficient (r) and P value from a two-tailed, non-parametric Spearman correlation; line was fit using linear regression.

Source data

Extended Data Fig. 4 Statistical robustness of the relationship between synaptic size and strength.

a, b, Bootstrap resampling from experimentally recorded EPSP distributions shows that the synaptic size–strength correlation is very robust when 100 EPSPs are averaged per experiment. a, Relationship between resampled mean EPSP amplitudes and cPSD area for the ten connected pairs. Black error bars indicate the range of mean EPSP amplitudes that resulted from a total of 10,000 resampling runs; grand means of resampled mean EPSP amplitudes (black dots) were fit with a line (dashed black) of slope 7.96 ± 2.44. Grey dots show experimentally measured EPSPs during stable recordings; the grey line shows the fit to experimentally measured mean EPSP amplitudes (both as in Fig. 3c). b, Histogram of P values for 10,000 correlations between resampled mean EPSP amplitudes and cPSD area. cf, The synaptic size–strength correlation is weak on a trial-to-trial basis. ce, Three example plots showing relationships between cPSD area and only a single evoked EPSP amplitude for each connected pair, which was randomly selected from stable recordings. Green, significant correlation; red, no significant correlation; grey dots and lines as in Fig. 3c. f, Histogram of P values for 10,000 correlations between randomly chosen single-trial EPSPs and cPSD area, such as the examples in ce. Shown are correlation coefficients (r) and P values from two-tailed, non-parametric Spearman correlations.

Source data

Extended Data Fig. 5 Experimental validation of the compartmental model.

The model was used to reproduce results obtained from combined dendritic and somatic patch-clamp recordings in barrel cortex L2/3 (ref. 25) and L5 pyramidal cells (ref. 26). Without further tuning, electrical coupling in the model was in excellent agreement with experimental data for depolarizing current injections, confirming that the model captured the electrical behaviour necessary to realistically simulate the transfer of excitatory synaptic currents from dendrites to soma. Note that transfer resistance (the steady-state form of transfer impedance) is symmetrical, that is, of the same magnitude in both somatodendritic and dendrosomatic directions. ac, Dendritic voltage responses to somatic current injections in the model cell are in good agreement with experimental data from L2/3 pyramidal cells (see Fig. 1a, b in ref. 25). a, Compartmental model with simulated somatic and dendritic recording sites indicated; the distance from the dendritic recording site to the soma was chosen to match ref. 25. b, Bottom, current steps injected at soma; middle, somatic voltage responses to somatic current injections; top, voltage traces at the dendritic recording site shown in a in response to somatic current injections were in good agreement with experimental traces from ref. 25. c, Current–voltage relationships at soma (grey) and dendritic recording site (blue) of the model cell. The model’s linear current–voltage responses for depolarizing subthreshold current injections (which constitute the relevant factor for simulating the propagation of excitatory synaptic currents in dendrites) were in excellent agreement with dendritic recordings in L2/3 pyramidal neurons25. df, Comparison of somatodendritic transfer resistance in the model with experimental data obtained using dendritic recordings in L5 pyramidal cells (see Fig. 1d in ref. 26). d, Transfer resistance was measured between 338 dendritic locations (blue circles) and the soma of the model. e, Transfer resistance in the model cell as a function of dendritic distance to soma; grey bar, distance range that corresponds to the dendritic recordings in ref. 26. f, Transfer resistances for dendritic locations at 100–130 μm from the soma were in excellent agreement with the experimental measurements of ref. 26.

Source data

Extended Data Fig. 6 Relationship between synaptic strength and postsynaptic morphology.

a, Relationship between cPSD area and estimated cIsyn; data were fit with a line of slope 1.04 ± 0.27. b, Comparison of estimated Isyn for PSDs with simple morphologies and complex morphologies (asterisks in Extended Data Fig. 3); means ± s.d. indicated; P value obtained by non-parametric Mann–Whitney test. c, Relationship between estimated Isyn and corresponding dendritic spine volume; data were fit with a line of slope 0.77 ± 0.32. d, Relationship between estimated Isyn and the length of the corresponding dendritic spine necks. Lines were fit using linear regression; 95% confidence intervals for estimated Isyn are indicated with error bars in a, c, d. a, c, d, Shown are the correlation coefficients (r) and P values of two-tailed, non-parametric Spearman correlations.

Source data

Extended Data Fig. 7 Approximating Isyn from EPSP amplitude and transfer impedance.

a, For connections comprising a single synapse, Isyn can be estimated using Ohm’s Law from the experimentally recorded somatic EPSP amplitude and the simulated distance-dependent transfer impedance. b, When two synapses (A and B) were identified in electron microscopy, we assumed that the ratio of IsynA to IsynB followed the ratio of PSDA to PSDB, and that synaptic potentials sum linearly at the soma.

Extended Data Fig. 8 Estimating Isyn with different combinations of model parameters.

ac, Relationship between PSD area and Isyn, estimated using four different parameter combinations in the compartmental model. The white circles and dashed lines show Isyn estimated from the default model used throughout this paper (as in Fig. 3f); for clarity, data points are shifted slightly horizontally. a, b, Estimates of Isyn were remarkably robust to changes in Ri and Rm. Parameters were tuned such that the model cell reproduced our somatically recorded current–voltage responses. Thus, changing Ri required Rm to be adjusted in the opposite direction. This appeared to have little net effect on the transfer impedance, which was then used to estimate Isyn. c, Lowering Cm resulted in lower estimates of Isyn. We chose a value for Cm of 1 μF cm−2 because of its widespread use in previous L2/3 models; we note, however, that Cm values below 2 μF cm−2 resulted in unrealistically short membrane time constants when compared with our in vitro recordings from the neuron that was later reconstructed for the model cell (data not shown). Models with Cm values of 2 μF cm−2 reproduce our experimental current–voltage responses markedly better and thus provide more realistic fits. Thus, the plot does not resemble an accurate estimate of Isyn, but rather a minimum bound for Isyn as a function of parameter tuning. d, Table summarizing model parameter combinations and non-parametric Spearman correlation coefficients (r), P values of correlations, and slopes of linear regressions for the four resulting PSD–Isyn relationships; varied parameters are indicated with bold font. The specific combinations of model parameters were chosen because they conformed with experimental measurements or were used in previous models of L2/3 neurons (see Methods). Note that all tested models resulted in statistically significant PSD–Isyn relationships.

Source data

Extended Data Fig. 9 Quantal analysis using SMAQ provides similar results and uncertainty compared with the method of fitting binomial models to peaky histograms.

ae, EPSP amplitude histograms from five experiments contained equally spaced peaks (suggesting discrete multiples of quantal size) but failed our anatomical quality criteria and were excluded from structure–function analyses. These histograms could be fit successfully with a simple quantal binomial model to extract quantal parameters (black lines overlaid on histograms). In addition, quantal parameters were estimated using SMAQ. The insets compare the quantal parameters found by fitting peaky histograms and by SMAQ. N, number of release sites; P, release probability; Q, quantal size; n, number of entries per histogram. f, Comparison of solutions for quantal parameters and associated 95% confidence intervals given by the method of fitting peaky histograms (black) and SMAQ (blue) across the five experiments shown in ae. Dark blue, Bayesian-inspired confidence intervals for SMAQ; light blue, confidence intervals for SMAQ derived from the same bootstrap resampling algorithm used to derive confidence intervals for the method of fitting peaky histograms (black). Solutions provided by the two methods were compared with the non-parametric Wilcoxon matched-pairs test (two-tailed P values shown in bold). For all three quantal parameters, solutions given by the two methods were not significantly different (see text).

Source data

Extended Data Fig. 10 Release probability and quantal size across the ten connected pairs.

a, SMAQ solutions for release probability (left-hand y-axis) and quantal size (right-hand y-axis) with corresponding 95% confidence intervals for the ten connected pairs. Pairs are sorted by decreasing number of release sites, as in Fig. 4c. b, Histograms of SMAQ solutions for release probability and quantal size, plotted on the same y-axes as in a.

Source data

Supplementary information

Supplementary Information

Supplementary Discussion: Discussion of previous experimental and modeling work in rodent barrel cortex and our own data highlighting that dendritic trees of L2/3 pyramidal neurons behave as passive, linear structures under the experimental paradigm used in our study – thus justifying the use of a passive compartmental model.

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Video 1

Series of consecutive cross-sections through synapse shown in Fig. 1f and Extended Data Fig. 2a. The video shows micrographs of all consecutive sections through the synaptic contact (left panel), collages of optimal tilt angles for all micrographs containing synaptic specializations (centre left panel), color-inverted images of these tilted micrographs (centre right panel) and our reconstruction (right panel). The video contains all information used by the authors to identify and reconstruct PSDs.

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Holler, S., Köstinger, G., Martin, K.A.C. et al. Structure and function of a neocortical synapse. Nature 591, 111–116 (2021). https://doi.org/10.1038/s41586-020-03134-2

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