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Binary and analog variation of synapses between cortical pyramidal neurons.

ABSTRACT: Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm³ volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.

SUBMITTER: Dorkenwald S

PROVIDER: S-EPMC9704804 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Binary and analog variation of synapses between cortical pyramidal neurons.

Dorkenwald Sven S Turner Nicholas L NL Macrina Thomas T Lee Kisuk K Lu Ran R Wu Jingpeng J Bodor Agnes L AL Bleckert Adam A AA Brittain Derrick D Kemnitz Nico N Silversmith William M WM Ih Dodam D Zung Jonathan J Zlateski Aleksandar A Tartavull Ignacio I Yu Szi-Chieh SC Popovych Sergiy S Wong William W Castro Manuel M Jordan Chris S CS Wilson Alyssa M AM Froudarakis Emmanouil E Buchanan JoAnn J Takeno Marc M MM Torres Russel R Mahalingam Gayathri G Collman Forrest F Schneider-Mizell Casey M CM Bumbarger Daniel J DJ Li Yang Y Becker Lynne L Suckow Shelby S Reimer Jacob J Tolias Andreas S AS Macarico da Costa Nuno N Reid R Clay RC Seung H Sebastian HS

eLife 20221116

Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm<sup>3</sup> volume). We used the map to identify constraints on the learning algorithms employed by the corte ...[more]

PMID: 36382887

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Project description:We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA-conductances per synaptic contact (0.88 and 1.31 nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers, and the assumption of five contacts per synaptic connection. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 ± 4.6 mV), spine base (9.7 ± 5.0 mV), and soma (0.3 ± 0.1 mV), and for the spine neck resistance (50-80 M?). Matching the shape and firing pattern of experimental somatic Na+-spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 ± 28 simultaneously activated HL2/L3-HL2/L3 synapses are required for generating (with 50% probability) a somatic Na+ spike. Dendritic NMDA spikes were triggered in the model when 20 ± 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA-spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat somatosensory cortex. These multi-sites non-linear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.

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Binary and analog variation of synapses between cortical pyramidal neurons.

Publications

Binary and analog variation of synapses between cortical pyramidal neurons.

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