#visual image reconstruction

[kamitani-lab]

Overcoming Output Dimension Collapse: How Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales

Advances in brain-to-image reconstruction are enabling us to externalize the subjective visual experiences encoded in the brain as images. A

Otsuka et al., arxiv 2025

[kamitani-lab]

Pierre Huyghe | In Imaginal

[kamitani-lab]

Natural sounds can be reconstructed from human neuroimaging data using deep neural network representation

Reconstructing sounds from fMRI data has been challenging due to its limited temporal resolution. This study develops method based on a deep

[kamitani-lab]

Inter-individual and inter-site neural code conversion without shared stimuli - Nature Computational Science

A neural code conversion method is introduced using deep neural network representations to align brain data across individuals without share

[kamitani-lab]

Advancing neural decoding with deep learning - Nature Computational Science

A recent study introduces a neural code conversion method that aligns brain activity across individuals without shared stimuli, using deep n

[kamitani-lab]

Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integra

[kamitani-lab]

Spurious reconstruction from brain activity | alphaXiv

View recent discussion. Abstract: Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the s

alphaXivで生成した日本語ブログ

[kamitani-lab]

Spurious reconstruction from brain activity

Advances in brain decoding, particularly in visual image reconstruction, have sparked discussions about the societal implications and ethica

Neural Networks 2025