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Which topics in a dual degree in Cognitive science and Computer science at the graduate level or systems neuroscience (experimental + theory) graduate degree will better prepare for a research career in theoretical neuroscience?

(trying to frame it in a more objective manner, as stackexchange forums prefer that)

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There are several approaches to theoretical neuroscience. I am currently taking the physics/mathematics approach: modelling the currents in neurons and coupling several neurons together through differential equations.

In Computer Science, the tendency is more towards machine learning: Bayesian statistics, artificial neural networks, signal processing, robotics, artificial intelligence. It really depends on what level you want to engage the field. For instance, if you want to study vision science, you'll want to learn more about vision and color perception and some basic physics/optics. But I also think neurochemistry and neurophysics is important, just for breadth.

Computer scientists often use the leaky integrate and fire model or rate coding models. There's something called the neural engineering framework that seeks to make networks of neurons do standard logical operations. One of their projects is SPAUN, a whole brain simulation.

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What should be the subjects to focus on if I were in a neuroscience program? Are you in a graduate program? What topics in physics/maths are you taking? –  Zeph Mar 6 at 21:15
    
My undergraduate was in physics, so I took all the standard physics courses. During my Master's degree, I took a lot of classic neuroscience courses and a Nonlinear Dynamics course through the physics department. For my PhD, I am taking Mathematical Cellular Biology, Perturbation Theory, Asymptotic Analysis, and Numerical Methods in Partial Differential Equations. I've also taken a neuroscience course where we discuss, report, and present on the current literature in neuroscience. –  Keegan Keplinger Mar 7 at 5:43

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