Single neocortical neurons are driven by populations of excitatory inputs, which form the basis of neuronal selectivity to features of sensory input. Excitatory connections are thought to mature during development through activity-dependent Hebbian plasticity1, whereby similarity between presynaptic and postsynaptic activity selectively strengthens some synapses and weakens others2. Evidence in support of this process includes measurements of synaptic ultrastructure and in vitro and in vivo physiology and imaging studies3,4,5,6,7,8. These corroborating lines of evidence lead to the prediction that a small number of strong synaptic inputs drive neuronal selectivity, whereas weak synaptic inputs are less correlated with the somatic output and modulate activity overall6,7. Supporting evidence from cortical circuits, however, has been limited to measurements of neighbouring, connected cell pairs, raising the question of whether this prediction holds for a broad range of synapses converging onto cortical neurons. Here we measure the strengths of functionally characterized excitatory inputs contacting single pyramidal neurons in ferret primary visual cortex (V1) by combining in vivo two-photon synaptic imaging and post hoc electron microscopy. Using electron microscopy reconstruction of individual synapses as a metric of strength, we find no evidence that strong synapses have a predominant role in the selectivity of cortical neuron responses to visual stimuli. Instead, selectivity appears to arise from the total number of synapses activated by different stimuli. Moreover, spatial clustering of co-active inputs appears to be reserved for weaker synapses, enhancing the contribution of weak synapses to somatic responses. Our results challenge the role of Hebbian mechanisms in shaping neuronal selectivity in cortical circuits, and suggest that selectivity reflects the co-activation of large populations of presynaptic neurons with similar properties and a mixture of strengths.
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An example electron microscopy image volume is publicly available at https://mpfi.org/download/mpfi-20200401-ferret-v1-ds1. Data presented in Fig. 2 and Extended Data Fig. 3 are available at https://github.com/schollben/StructFuncEM2020. Additional data and published data are available from the corresponding author upon reasonable request.
Code is available from the corresponding author upon reasonable request. The NEURON modelling script is available from https://github.com/schollben/StructFuncEM2020.
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We thank J. Yates for analytical advice, D. Hildebrand for SEM discussion, B. Ujfalussy for help with NEURON modelling, C. Tepohl for surgical assistance, N. Shultz and R. Satterfield for help with perfusions and fixative preparation, and the MPFI ARC for animal care. The authors thank the GENIE project for access to GCaMP6s. This work was supported by NIH grant R01 EY011488 (D.F.), NIH grant K99 EY031137 (B.S.), the Max Planck Florida Institute for Neuroscience, and the Max Planck Society.
The authors declare no competing interests.
Peer review information Nature thanks Davi Bock, Christian Lohmann and Linnaea Ostroff 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 Correlating in vivo synaptic imaging and serial block-face scanning electron microscopy.
a, In vivo two-photon synaptic imaging of L2/3 cortical neurons in ferret visual cortex expressing GCaMP6s is performed under visual stimulation. Imaged cells are identified after perfusion and re-imaged with a confocal microscope. Tissue is trimmed and processed for serial-block-face scanning electron microscopy (SBF-SEM). Imaged cells are identified within the block of tissue via biological fiducials and high-resolution SEM is performed. Finally, imaged cells, dendritic spines, and corresponding synaptic features are volumetrically reconstructed for quantification. Dendrite is shown in brown, spine neck and head are shown in blue, post-synaptic density is shown in red, and presynaptic bouton is shown in green.
Extended Data Fig. 2 Diversity of ultrastructural and functional properties for individual synapses.
a, Distribution of the number of presynaptic boutons contacting single, visually-responsive spines. b, Distribution of visually-responsive spines with a simple or perforated postsynaptic density (PSD). c, Spine head volume and PSD area are strongly correlated in individual synapses (Spearman’s correlation, one-sided test, n = 155 from 5 cells from 3 animals). d, Spine head volume is not correlated with spine neck length (Spearman’s correlation, two-sided test, n = 155 from 5 cells from 3 animals). e, For each synapse reconstructed, a NEURON model (Methods) was used to simulate voltage depolarization in the spine head and soma (ΔVm). A schematic of this model is shown (inset). Spines with longer necks show greater voltage attenuation (ΔVmsoma/ΔVmspine, left) and spines with larger PSDs drive larger ΔVmsoma (right). f, Distributions of spine-soma preference difference for orientation (median = 31.2, IQR = 48.4, n = 155 from 5 cells from 3 animals), direction (median = 106 deg, IQR = 114 deg, n = 155 from 5 cells from 3 animals), and ocular dominance (median = 0.43, IQR = 0.60, n = 155 from 5 cells from 3 animals). Except for direction preference, distributions are significantly different from a uniform distribution (Kruskal–Wallis test). g, Distributions of spine selectivity for direction (left) and orientation (right). Selectivity computed as vector strength (Methods) of peak responses elicited by stimulation of the preferred eye.
Relationships between the difference in direction preference (top), ocular dominance (middle), and tuning correlation (bottom) with synapse spine head volume (blue), PSD area (red), and simulated spine-soma voltage attenuation (grey). Above each plot, correlation magnitude and p-value is shown. For direction preference difference, Circular-Linear correlation coefficient was calculated. For ocular dominance and tuning correlation, Spearman’s correlation coefficient was calculated. All significance tests are one-sided.
Extended Data Fig. 4 Spine-soma functional similarity is uncorrelated with synaptic strength for spines with low residual correlation with dendritic signals.
Relationships between the difference in orientation preference, direction preference, ocular dominance, and tuning correlation with spine head volume (blue), PSD area (red), and simulated spine-soma voltage attenuation (grey). Spines included were required to have rresidual < 0.2. Above each plot, correlation magnitude and p-value is shown. For orientation and direction preference differences, Circular-Linear correlation coefficient was calculated. For ocular dominance and tuning correlation, Spearman’s correlation coefficient was calculated. All significance tests are one-sided.
Extended Data Fig. 5 Spine-soma functional similarity is uncorrelated with synaptic strength for spines with high SNR.
Relationships between the difference in orientation preference, direction preference, ocular dominance, and tuning correlation with spine head volume (blue), PSD area (red), and simulated spine-soma voltage attenuation (grey). Spines included were required to have an SNR >3. Above each plot, correlation magnitude and p-value is shown. For orientation and direction preference differences, Circular-Linear correlation coefficient was calculated. For ocular dominance and tuning correlation, Spearman’s correlation coefficient was calculated. All significance tests are one-sided.
Extended Data Fig. 6 Relationship between spine-soma orientation preference and synaptic strength across cell populations.
Shown are the correlation coefficient (abscissa) and associated p-value (ordinate) for spine population on each cell imaged and reconstructed. The colour of each data point represents the functional property examined. For orientation and direction preference differences, Circular-Linear correlation coefficient was calculated. For ocular dominance and tuning correlation, Spearman’s correlation coefficient was calculated. Dashed line is P = 0.05 cutoff. All significance tests are one-sided.
Extended Data Fig. 7 Spatiotemporal clustering of synaptic events excluding bAPs and comparing spine neck length.
a, Relationship between spine pair distance and trial-to-trial correlation during visual stimulation, excluding stimulus trials with dendritic calcium events (Methods). Data are mean ± s.e.m (black). Also shown are shuffled correlations (grey dashed lines), data are s.e.m (n = 396 pairs from 5 cells from 3 animals). The grey data point denotes significant difference from shuffled correlations (P = 0.001, bootstrapped confidence interval, one-sided test). *P = 0.0023 (significantly different correlation distributions), Wilcoxon rank-sum two-sided test.b, Same as in a for synapse pairs with smaller spine head volume (<0.35 μm3; n = 132 pairs from 5 cells from 3 animals). c, Same as in a for synapse pairs with larger spine head volumes (>0.35 μm3; n = 86 pairs from 5 cells from 3 animals). The grey data point denotes significant difference from shuffled correlations (P = 0.016, bootstrapped confidence interval, one-sided test). *P = 0.0472 (significantly different correlation distributions), Wilcoxon rank-sum two-sided test.d, Relationship between spine pair distance and trial-to-trial correlation during visual stimulation. Note, these data are the same as shown in Fig. 4a. e, Same as in d for synapse pairs with smaller neck lengths (<1.75 μm; n = 112 pairs from 5 cells from 3 animals). f, Same as in d for synapse pairs with longer neck lengths (<1.75 μm; n = 106 pairs from 5 cells from 3 animals).
a, Relationship between spine selectivity (vector strength) for direction (top) and orientation (bottom) and spine head volume (blue), PSD area (red), and simulated spine-some voltage attenuation (grey). Above each plot, Spearman’s correlation coefficient and significance (one-sided test) is shown. b, Same as in a, but for spines with SNR >3. Note that correlation significance persists and for some comparisons, the correlation magnitude is larger.
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Scholl, B., Thomas, C.I., Ryan, M.A. et al. Cortical response selectivity derives from strength in numbers of synapses. Nature 590, 111–114 (2021). https://doi.org/10.1038/s41586-020-03044-3