Abstract

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Highlights

  • Dreaming during sleep is a universal human experience and one that is often accompanied by highly realistic visual scenes spontaneously generated by the brain

  • We examined whether hierarchical visual feature representations common to perception are recruited to represent dreamed objects in the brain

  • We used the decoders trained to decode visual features of seen object images and showed that the feature values decoded from brain activity during dreaming positively correlated with feature values associated with dreamed object categories at mid- to high-level deep convolutional neural network (DNN) layers

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Summary

Introduction

Dreaming during sleep is a universal human experience and one that is often accompanied by highly realistic visual scenes spontaneously generated by the brain. The most striking characteristic of visual dreaming is its similarity to the visual experience during waking hours, and dreaming generally incorporates features that are typical of the waking experience, such as shapes, objects, and scenes These phenomenological similarities are considered to be underlain by neural substrates common to both the awake and sleep states, and a number of studies have sought to address the neural commonalities and differences of these contrasting states by analyses of regional brain. The authors used decoders trained to predict categorical labels of viewed objects and scenes, the labels of which were constructed from subjects’ dream reports They thereby demonstrated decoding of dream contents from brain activity patterns during sleep using stimulus-trained decoders. Their results suggest that visual dream contents are represented by discriminative brain activity patterns similar to perception at least in higher visual areas

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