Dense connectomic reconstruction in layer 4 of the somatosensory cortex Science, 29 Nov 2019:Vol. 366, Issue 6469, eaay3134DOI: 10.1126/science.aay3134 Dense connectomic reconstruction in layer 4 of the somatosensory cortex Alessandro Motta, et.al. Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany. Probabilistic Numerics Group, Max Planck Institute for Intelligent Systems, D-72076 Tübingen, Germany. [paraphrase] The cerebral cortex of mammals houses an enormously complex intercellular interaction network implemented with neuronal processes that are long and thin, branching, and extremely densely packed. Early estimates indicated that 4 km of axons and 400 m of dendrites are compressed into a cubic millimeter of cortical tissue. This high packing density of cellular processes has made the locally dense mapping of neuronal networks in the cerebral cortex challenging. So far, reconstructions of cortical tissue have been either sparse or restricted to small volumes of up to 1500 μm 3 . Consequently, the detailed network architecture of the cerebral cortex is unknown. Particular open questions are to what degree local neuronal circuits are explainable by geometric rules alone and on which spatial scales cortical connectivity is only explainable by innervation preferences beyond such geometric models. Similarly, although numerous cortical neuronal cell types have been described based on protein expression, morphology, and electrophysiological characteristics, and these have been shown to have particular synaptic target patterns, the inverse question—whether, at the level of the dense cortical circuit, axons represent a continuum of synaptic preference or a set of distinct innervation paradigms that would allow for a purely connectomic cell type definition [as has been successful in the retina]—is still open. Next, at the level of synaptic input to the primary dendrites of cortical excitatory cells, it is not known whether the typically three to 10 primary dendrites of a cortical neuron that leave the cell body   homogeneously sample the available excitatory and inhibitory synaptic inputs or if there is an enhanced heterogeneity of synaptic input composition, making it possible to exploit the numerous mechanisms that have been discussed for the nonlinear integration  of local synaptic inputs. Finally, whereas the change of synaptic weights in response to electrical and sensory stimulation has been widely studied and connectomic data consistent with LTP have been described, the fraction of a given cortical circuit that is plausibly shaped by processes related to Hebbian learning under undisturbed conditions is still unknown. Using FocusEM, we obtained the first dense circuit reconstruction from the mammalian cerebral cortex at a scale that allowed the analysis of axonal patterns of subcellular innervation, ~300 times larger than previous dense reconstructions from cortex. Inhibitory axon types    preferentially innervating certain postsynaptic subcellular compartments    could be defined solely on the basis of connectomic information. In addition to inhibitory axons, a fraction of excitatory axons also exhibited such subcellular innervation preferences. The geometrical arrangement of axons and dendrites explained only a moderate fraction of synaptic innervation, revoking coarse random models of cortical wiring. A substantial TC synapse gradient in L4 gave rise to an enhanced heterogeneity of synaptic input composition at the level of single cortical dendrites, which was accompanied by a reduced innervation from AD-preferring inhibitory inputs. The consistency of synapse size between pairs of axons and dendrites signified fractions of the circuit consistent with saturated synaptic plasticity, placing an upper bound on the “learned” fraction of the circuit. FocusEM allowed the dense mapping of circuits in the cerebral cortex at a throughput that enables connectomic screening.