In science, theories are essential for encapsulating knowledge obtained from data, making predictions, and building models that make simulations and technological applications possible. Neuroscience -- along with cognitive science -- however, is a young field with fewer established theories (than, say, physics). One consequence of this fact is that new practitioners in the field sometimes find it difficult to know what makes a good theory. Moreover, the use of conceptual theories and models in the field has endured some criticisms: theories have low quantitative prediction power; models have weak transparency; etc. Addressing these issues calls for identifying the elements of theory in neuroscience. In this talk I will try to present and discuss, with case studies, the following: (1) taxonomies by which the different dimensions of a theory can be assessed. (2) criteria for the goodness of a theory. (3 )trade-offs between agreement with the natural world and representational consistency in the theory/model world.