Abstract:         Biologically realistic models in neuroscience are challenging to build and to simulate due to the large numbers of neurons, their complex interactions, and the large number of unknown physiological parameters.  Reduced, or coarse-grained, models are more tractable, but it is not always clear how to make sense of models too far removed from neuroanatomy and physiology.  In this talk, I will report on a recently proposed approach to coarse-graining cortical models that aims to balance biological realism and computational efficiency, and illustrate its use on models of specific cortical circuits in primates.  This is joint work with Zhuo-Cheng Xiao (PhD UA Applied Math 2020, now at NYU-Shanghai) and Lai-Sang Young (NYU).