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Tomoyasu Horikawa, Yukiyasu Kamitani; Generic decoding of seen and imagined objects using features of deep neural networks. Journal of Vision 2016;16(12):372. doi: 10.1167/16.12.372.
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© ARVO (1962-2015); The Authors (2016-present)
Object recognition is a key function in both human and machine vision. Recent studies support that a deep neural network (DNN) can be a good proxy for the hierarchically structured feed-forward visual system for object recognition. While brain decoding enabled the prediction of mental contents represented in our brain, the prediction is limited to training examples. Here, we present a decoding approach for arbitrary objects seen or imagined by subjects by employing DNNs and a large image database. We assume that an object category is represented by a set of features rendered invariant through hierarchical processing, and show that visual features can be predicted from fMRI patterns and that greater accuracy is achieved for low/high-level features with lower/higher-level visual areas, respectively. Furthermore, visual feature vectors predicted by stimulus-trained decoders can be used to identify seen and imagined objects (extending beyond decoder training) from a set of computed features for numerous objects. Successful object identification for imagery-induced brain activity suggests that feature-level representations elicited in visual perception may also be used for top-down visual imagery. Our results demonstrate a tight link between the cortical hierarchy and the levels of DNNs and its utility for brain-based information retrieval. Because our approach enabled us to predict arbitrary object categories seen or imagined by subjects without pre-specifying target categories, we may be able to apply our method to decode the contents of dreaming. These results contribute to a better understanding of the neural representations of the hierarchical visual system during perception and mental imagery.
Meeting abstract presented at VSS 2016
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