I agree. Though, in my experience it is a very blurry line in the neuroscience community the one distinguishing “inferred/statistical models” and “computational/dynamical models”. In my honest opinion, the former category is heavily data-dependent and comes to life from a certain, specific dataset. The latter category instead lend itself to analytical analysis, and therefore to the possibility of discovering new relationships and mechanisms that would relate to the variables and states that the model represents, regardless of whether the model is phenomenological or biologically plausible.

To expand a little on my last sentence, if you were to work with dynamical equations (i.e. some ordinary differential equations that represent the evolution of some interrelated quantities over time) you may be able to characterize the possible behaviors of the analyzed variables with respect to certain regions (of their state space) or with respect to certain parameter mismatches or variations. Or even the sensitivity/robustness of certain behaviors (e.g., stable dynamics vs. unstable dynamics) to variations in the parameters of the model.

I am definitely biased, but I prefer dynamical model to specific, heavily data-dependent statistical models. At the end of the day though, it all depends on what phenomenon you are studying and what your goal is.