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Looking for background on RNN’s and their use in modeling prefrontal cortex [Discussion]

Hi all,

I’m about to start a PhD in cognitive/computational neuroscience and I was having trouble finding some good background on this but I was wondering if anyone here has some good suggestions for reviews or landmark pieces of literature on the study of RNN’s for modeling neural dynamics especially in prefrontal cortex?

I’m mostly thinking along the lines of Earl Miller’s recent work in applying models using reservoir computing or the Shenoy labs use of a sequential variational autoencoder (LFADS) for modeling neural state space trajectories and the associated background. I have a BS and MS in Applied Math so technical reviews that unify and lend generality are preferred such as, my all-time favorite, A Unifying Review of Gaussian Linear Models by Roweis and Ghahramani (but for RNN’s). I don’t suppose there are any books about these yet.

Also, theoretical perspectives regarding the training and topology of RNN’s are also of much interest!

Thanks in advance!

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.