The recent advances in deep learning have done more than just make money for startups and tech companies. They've also infiltrated neuroscience! Deep neural networks---models originally inspired by the basics of the nervous system---are finding ever more applications in the quest to understand the brain. We talk about many of those uses in the episode. After first describing more traditional approaches to modeling behavior, we talk about how neuroscientists compare deep net models to real brains using both performance and neural activity. We then get into the attempts by the field of machine learning to understand their own models and how ML and neuroscience can share methods (and maybe certain cultural tendencies). Finally we talk about the use of deep nets to generate stimuli specifically tailored to drive real neurons to their extremes. Throughout, we notice how deep learning is "complicating the narrative", ask "are deep nets normative models?", and struggle to talk about a topic we actually know about.

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