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Currently I'm studying mathematics (2nd year) and I think I'm pretty into neuroscience. To "test" this, I purchased Principles of Computational Modelling in Neuroscience and am considering to get Biophysics of Computation: Information Processing in Single Neurons which seems to fit my interests fairly well, as I'd want to steer a middle course: Learn about the biological structure and manner of functioning of neural cells and thus be able to derive and develop models that do not only fulfill one special task (like most machine learning, ... now - an algorithm for face recognition will never challenge its own functioning), but rather resemble our brain.

As Biophysics of Computation was published in 1998, I wanted to know if it is still a good read, or already considered too outdated?

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I recommend Theoretical neuroscience by Abbott and Dayan. amzn.to/18j8Teh – Memming Sep 13 '13 at 17:35
up vote 2 down vote accepted

I've looked through that book briefly but never really got into it... if I could suggest some others, though: Dayan and Abbott's Theoretical Neuroscience is a standard text in computational neuroscience but if I understand your question correctly, you might enjoy Kevin Murphy's Machine Learning: A Probabilistic Perspective, or David MacKay's Information theory, inference, and learning algorithms a bit more. Trappenberg's Fundamentals of computational neuroscience is also one of my favourites, but moreso on the neuroscience side of things.

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