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We aim to understand how the brain represents information about the real world, toward such a goal, we demonstrated that our algorithm can model and read out perceptual contents in the form of sentences from human brain activity. To do this, we modified an existing network model that could generate sentences from images using a deep neural network, a model of visual system, followed by an RNN (recurrent neural network), a model that can generate sentences. Specifically, using our dataset of movies and movie-evoked brain activity, we trained a new model that could infer activation patterns of DNN from brain activity.

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