Computational Neuroscience

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Recommended Background: Familiarity with basic concepts in linear algebra, calculus, and probability theory. Specifically, ability to understand simple equations involving vectors and matrices, differentiate simple functions, and understand what a probability distribution is. For video lectures reviewing these topics, please visit the <a href="https://www.khanacademy.org/math/linear-algebra">Linear Algebra</a>, <a href="https://www.khanacademy.org/math/calculus">Calculus</a>, and <a href="https://www.khanacademy.org/math/probability">Probability</a> sections of Khanacademy.org. For homeworks, some familiarity with Matlab/Octave or Python would be useful. No prior background in neuroscience is required.
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Recommended Textbook: The lectures will roughly follow topics covered in the textbook <a href="http://www.gatsby.ucl.ac.uk/~dayan/book/" title="Link: http://www.gatsby.ucl.ac.uk/~dayan/book/">Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems</a> by Peter Dayan and Larry Abbott (MIT Press).
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