Philosophers of neuroscience have been employing scientific explanation as an epistemic value to evaluate neuroscientific models for the past twenty years. Consequently, they have developed mechanistic and non-mechanistic accounts of neuroscientific explanation. These two types of accounts explicate how to use a specific kind of explanatory value to evaluate the epistemic value of neuroscientific models. This paper presents a case study involving the canonical models from mathematical and computational neuroscience. This case study will show that the above mechanistic and non-mechanistic framework overly focuses on analyzing neuroscientific models as objects with representational contents. As a consequence, it pays less attention to the process of modeling and the epistemic attitudes of modelers; moreover, it can miss some important epistemic values used by modelers. By reconstructing their modeling process, I will identify the relevant modelers’ epistemic attitudes and argue that these modelers use different kinds of epistemic values to evaluate the same type of canonical models. Furthermore, among them, one epistemic value is not captured by the relevant mechanistic and non-mechanistic accounts. I develop a processual framework that centers on the modeling process and modelers’ epistemic attitudes. This process framework is better because it it accommodates both the mechanistic and non-mechanistic accounts of neuroscientific explanation regarding the canonical model in addition to capturing what both accounts leave out.