The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

Abstract

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types , an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the large thick tufted layer 5 pyramidal cell also known as extra telencephalic cell (ET) . Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive quantitative characterization of their local connectivity and their projections to other cortical areas remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset encompassing primary and higher order visual areas. Our findings provide unexpected insights, as we observed that ETs formed the majority of their local synapses with inhibitory targets and the majority of their inter-areal synapses with excitatory targets. Locally, recurrent synapses with other ET neurons are sparse (~3%) and we identify previously undisclosed excitatory synaptic partners. We further reveal that ET neurons preferentially target specific inhibitory cell types in a feedback loop. These findings provide a novel view of the role of ET pyramidal cells in the cortex, highlighting their limited local recurrent connections while exerting widespread inhibition to regulate local ET networks as they transmit the information subcortically. Our results also highlight a circuit motif where a subclass of excitatory cells forms a sub-circuit with specific inhibitory cell types, providing a framework to investigate other excitatory cell types.

Perisomatic Features Enable Efficient and Dataset Wide Cell-Type Classifications Across Large-Scale Electron Microscopy Volumes

“…Our analysis of the perisomatic region of cells in the mouse visual cortex demonstrates that a surprising amount of cell-type information can be extracted from the somatic regions of brain cells. Our approach has already been used to characterize the connectivity of distinct types of layer 5 Martinotti cells, 17 the inter-related connectivity motifs of layer 5 thick tufted cells (5P-ET) and the surrounding inhibitory sub-network, 18 and to confirm the connectivity profiles of interneurons to cells outside the column. 16 Future work in this dataset and others will likely leverage iterations of dataset wide cell classifiers to discover novel aspects of cell-type specific wiring of cortical circuits along with broader organizational principles of wiring.…”

“…This method is already being used to reveal fundamental aspects of cell type specific wiring of mammalian cortex. 3,[16][17][18] More broadly, the efficacy of this approach provides a roadmap for how to develop a scalable platform for leveraging local features of cells to infer cell-type classifications across large scale image data. Histogram of the radial extent of dendrites from a sample of 1,347 proofread neurons 16 (left) and the cumulative distribution of those cells (right).…”

“…This method is already being used to reveal fundamental aspects of cell type specific wiring of mammalian cortex. 3,16–18 More broadly, the efficacy of this approach provides a roadmap for how to develop a scalable platform for leveraging local features of cells to infer cell-type classifications across large scale image data.…”