Biologically realistic models of cortical circuits are challenging to build, tune, and analyze due to the large numbers of neurons and their complex interactions.  Reduced, or coarse-grained, models are more tractable, but there is a nontrivial trade-off between tractability and biological fidelity / interpretability.  In this talk, I will describe a coarse-graining strategy inspired by ideas from nonequilibrium statistical mechanics.  The aim is to balance biological realism and computational efficiency.  I will illustrate how this strategy applies to the primate primary visual cortex.  This is joint work with Zhou-Cheng Xiao and Lai-Sang Young.

Note: this is joint CMCF seminar