Testing the Cortical Column Conjecture

Many contemporary theories of neural information processing suggest that the neocortex employs algorithms composed of repeated instances of a limited set of computing primitives. There is a recognized need for tools that interrogate the structure of the cortical microcircuits believed to embody these primitives. The cortical column conjecture suggests that neurons in the neocortex are connected in a graph that exhibits motifs representing repeated processing modules. Carey Priebe and his collaborators will present a notional demonstration of how statistical inference on graphs can inform our understanding of cortical computing.

By modeling the cortical graph as a hierarchical stochastic block model (HSBM), with induced subgraphs, which are themselves independent stochastic block models, a natural question is to estimate the extent to which identified subgraphs share common structure. This will require addressing the problem of identifying candidate subgraphs, and of determining the impact of imperfect subgraph identification on subsequent inference. The application of this connectomics theory and the associated methods will be demonstrated via a bio-inspired, large-scale simulation study.

If this lecture is videotaped, it will be posted here after production.