Abstract: I will discuss the primary statistics-in-neuroscience topic my trainees and I have worked on since the time of my 2017 Fisher Lecture (renamed in 2020 to Distinguished Achievement Award and Lectureship), where I articulated the problem in general terms: it is the problem of identifying interactions among neural populations from large-scale electrophysiological recordings. I will describe statistically natural approaches we have used to document interactions among brain areas based on neural spike trains (using latent variable point process models) and oscillating field potentials (by defining an exponential family on a 24-dimensional torus). I will also highlight some of the biggest high-level lessons I've learned about the nature of statistical inference in science, which suggest ways we as statisticians can continue to improve the scientific process. Relevant papers can be found on my website, as can biographical information.