Neuronal circuits in the cerebral cortex of mammals defy individual neuron identification within large sets of homotypic cells (for example the hundreds of excitatory neurons in each layer of a cortical column). This is in contrast to brains of many other species, in which many, most or all neurons carry individual, genetically determined identity.
Therefore connectomic analysis in the cerebral cortex requires methods that can deal with unsorted unidentified large connectivity matrices. We have developed an approach for approximate Bayesian circuit model distinction based on measured connectomes. Here we will discuss the application of such methods to actual columnar connectomes, and the comparison to more conventional clustering approaches. Furthermore, we will report on connectomic analysis of models of synaptic plasticity given statically mapped connectomes.
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