I believe that the following courses are a minimum math/engineering/(+cs) requirement for theoretical neuroscience research, specifically, Eliasmith's NEF/SPA approach. I wonder what other tools might be useful. Concretely, I am trying to workout the marginal utility of "learning more math just in case you'll need to apply it in some new context + improved mathematical maturity" versus "learning more neuroscience". Can you please list some math/engineering/(+cs) courses you found integral to your research and provide context?
Math: Precalculus, Calculus 1, Calculus 2, Multivariable Calculus, Ordinary Differential Equations, Linear Algebra (to Eigenvalues), Discrete Mathematics and Logic, Optimization and Numerical Methods
Engineering: Systems and Signals, Control Systems, Pattern Recognition, Information Theory and Applications, Machine Learning (Neural Networks in particular)
CS: Data Structures and Algorithms, Object-Oriented Software Development, Neuromorphic Computing