Theoretical Neuroscience: what tools are in your mathematical toolkit? 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 &quot;learning more math just in case you'll need to apply it in some new context + improved mathematical maturity&quot; versus &quot;learning more neuroscience&quot;. 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
Luckily, I think your list of requirements is already too long.
Your primary toolkit is going to be:

Linear Algebra
Probability and statistics
python

If you want, you can add:

Signal processing (filtering)
Machine Learning (mostly just applications of linear algebra)

I am currently TAing at an online summer school called Neuromatch Academy.  Check out their syllabus (3 week course) for an overview of tools useful in theoretical neuroscience.  homepage: https://neuromatch.io/academy/ syllabus, tutorials: https://github.com/NeuromatchAcademy/course-content
Out of interest, why NEF?  There are a wealth of frameworks out there, and NEF is relatively limited in its application.