 David Kaplan and Craver (2011) focus the mechanist perspective critically on dynamical systems mathematical models popular in recent systems and computational neuroscience. They argue that such models are explanatory only if there exists a “plausible mapping” between elements in the model and elements in the modeled system. At bottom is their Model-to-Mechanism-Mapping (3M) Constraint on explanation. The variables in a genuinely explanatory model correspond to components, activities, or organizational features of the system being explained. And the dependencies posited among variables in the model, typically expressed mathematically in systems and computational neuroscience, correspond to causal relations among the system’s components. Kaplan and Craver justify the 3M Constraint on grounds of explanatory norms, common to both science and common sense. All other things being equal, they insist, explanations that provide more relevant details about a system’s components, activities, and organization, more likely will answer more questions about how the system will behave in a variety of circumstances, than will an explanation that provides fewer (mechanistic) details. “Relevant” here pertains to the functioning of the specific mechanism. Models from systems and computational neuroscience that violate the 3M Constraint are thus more reasonably thought of as mathematical descriptions of phenomena, not explanations of some “non-mechanistic” variety.