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- Thread starter andryd9
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Learn your graph theory, statistics, probability, and linear algebra very well. I don't know too much about general computational neuroscience, but I do know some related fields and subdisciplines like machine learning. Control theory has differential equations that you need to know as well. So those four topics should be good for you to know in regards to mathematics.

For implementations, I can't be sure, but for statistics people like to use R and Matlab, and Python has some really awesome libraries for all kinds math including linear algebra and statistics. I would say Python is also the most accessible to you.

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Matlab is standard in computational neuroscience (and most disciplines) for drafting programs and for less computationally intensive analysis and modelling. Compiled languages are usually a better choice if you need to do something extremely intensive. R is, of course, standard for statistics, but I honestly do most of my analysis in Matlab.

As far as mathematics: A strong background in calculus, linear algebra, statistics, and differential equations is non-negotiable. If you're interested in the biologically realistic modelling of neurons and neural networks, than you really can't get your foot in the door without some familiarity with dynamical systems (I've been working with a fairly abstract model of pre-frontal neural clusters over the last few months, and 90% of what I'm doing involves non-linear dynamics). Beyond that, you can find a use for almost anything; knowledge of stochastic processes is extremely useful, and I've even managed to make use of some topology and analysis.

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