Deep neural networks have set new standards in modeling the responses of large scale populations of neurons to natural stimuli, yielding models that can accurately predict the response of thousands of neurons to novel stimuli. This allows us to treat the model as a functional digital twin of the neural circuit and probe neurons in ways that would not be feasible experimentally. With that, we can derive new hypotheses about the neural circuits that can then be verified in subsequent experiments. In this talk, I will give an overview over the models and their application in understanding the computational properties of visual cortex.
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