Connectomic reconstruction of a cortical column

The cerebral cortex of mammals has long been proposed to comprise unit-modules, so-called cortical columns. The detailed synaptic-level circuitry of such a neuronal network of about 104 neurons is still unknown. Here, using 3-dimensional electron microscopy, AI-based image processing and automated proofreading, we report the connectomic reconstruction of a defined cortical column in mouse barrel cortex. The cortical column appears as a structural feature in the connectome, without need for geometrical or morphological landmarks. We then used the connectome for definition of neuronal cell types in the column, to determine intracolumnar circuit modules, analyze the logic of inhibitory circuits, investigate the circuits for combination of bottom-up and top-down signals in the column and the specificity of bottom-up and top-down cortical input, search for higher-order circuit structure within homogeneous neuronal populations, and estimate the degree and symmetry of Hebbian learning in the various connection types. With this, we provide a first column-level connectomic description of the cerebral cortex, the likely substrate for a synaptic-level mechanistic understanding of sensory-conceptual integration and learning.