I would agree, but I don't think they're that much more interesting than the neurons of many "biologically plausible"-type neuronal nets, which allow for inhibition/exhibition, etc, but are obviously still limited in variation between individuals.

Where I think the really interesting parts missing are those in real neural circuits do, especially since the connections are more heterogenous, and the dynamics are much more fluid and time-dependent, whereas typical at-scale computer neurons obviously operate at fixed time steps, and don't exactly have bursters etc.

I wrote a very large scale distributed simulations for real neurons (straight from imaging datasets) when I was working in a TMS lab, and a longtime side project I always wanted to get going was some kind of pipeline for spitting out neuromechanical/cable-theoretic RTL tailored to specific morphologies and networking them according to their connectomes. I don't even think I would see any performance improvements, it just feels fun.

I think the timing aspect is especially interesting here. We tend to focus on making individual artificial neurons more sophisticated, but a lot of the interesting behavior might come from how the whole circuit evolves over time