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Deep learning, computational neuroscience, and cognitive science haveoverlapping goals related to understanding intelligence such that perceptionand behaviour can be simulated in computational systems. In neuroimaging,machine learning methods have been used to test computational models of sensoryinformation processing. Recently, these model comparison techniques have beenused to evaluate deep neural networks (DNNs) as models of sensory informationprocessing. However, the interpretation of such model evaluations is muddied byimprecise statistical conclusions. Here, we make explicit the types ofconclusions that can be drawn from these existing model comparison techniquesand how these conclusions change when the model in question is a DNN. Wediscuss how DNNs are amenable to new model comparison techniques that allow forstronger conclusions to be made about the computational mechanisms underlyingsensory information processing.

How can deep learning advance computational modeling of sensory information processing?