Tomoyasu Horikawa

Mind Captioning: Evolving descriptive text of mental content from human brain activity

This paper develops a method to generate descriptive text of mental content from brain activity, showing it can capture structured visual semantics and generalize to recalled content.

Reconstructing visual illusory experiences from human brain activity

This study reconstructs visual illusory experiences from brain activity using a decoding technique and deep neural networks, showing that reconstructions match illusory percepts.

Inter-individual deep image reconstruction

This study develops a neural code converter to align brain activity patterns across individuals, enabling inter-individual visual image reconstruction while preserving hierarchical representations.

Attentionally modulated subjective images reconstructed from brain activity

This study reconstructs visual images from brain activity during attention tasks, showing reconstructions align with attended images rather than unattended ones.

The neural representation of visually evoked emotion is high-dimensional, categorical, and distributed across transmodal brain regions

The paper identifies neural substrates of a high-dimensional emotion space using fMRI responses to 2185 videos, showing emotion categories better predict brain activity than affective dimensions.

End-to-end deep image reconstruction from human brain activity

This paper trains a deep neural network end-to-end to reconstruct images from human brain fMRI data, showing improved accuracy with more training data.

Characterization of deep neural network features by decodability from human brain activity

The paper decodes deep neural network features from human brain activity using fMRI data and presents a dataset showing correlations in decoding accuracy across subjects.

Deep image reconstruction from human brain activity

This paper introduces a method to reconstruct images from human brain activity by optimizing pixel values to match deep neural network features across multiple layers, showing it generates new images rather than matching exemplars.

Position information encoded by population activity in hierarchical visual areas

The paper decodes object position from fMRI data across visual areas, finding similar prediction accuracy despite differences in receptive field size.