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Abstract
Layer 4 (L4) of mammalian neocortex plays a crucial role in cortical information processing, yet a complete census of its cell types and connectivity remains elusive. Using whole-cell recordings with morphological recovery, we identified one major excitatory and seven inhibitory types of neurons in L4 of adult mouse visual cortex (V1). Nearly all excitatory neurons were pyramidal and all somatostatin-positive (SOM+) non-fast-spiking interneurons were Martinotti cells. In contrast, in somatosensory cortex (S1), excitatory neurons were mostly stellate and SOM+ interneurons were non-Martinotti. These morphologically distinct SOM+ interneurons corresponded to different transcriptomic cell types and were differentially integrated into the local circuit with only S1 neurons receiving local excitatory input. We propose that cell type specific circuit motifs, such as the Martinotti/pyramidal and non-Martinotti/stellate pairs, are used across the cortex as building blocks to assemble cortical circuits.
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1 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
2 Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
3 Department of Anatomic Pathology, University of California San Francisco, San Francisco, CA, USA
4 Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
5 Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany; Department of Computer Science, University of Tübingen, Tübingen, Germany
6 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital, Houston, TX, USA
7 Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA; Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA; Department of Electrical and Computational Engineering, Rice University, Houston, TX, USA