Kei Majima

Distinct roles of monkey OFC-subcortical pathways in adaptive behavior

This study identifies distinct OFC-subcortical pathways in macaques that support experience-based and inference-based value updating during adaptive decision-making.

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.

Mental image reconstruction from human brain activity

This study improves image reconstruction from brain activity by incorporating semantic information, enabling reconstruction of both seen and imagined images.

Functional Diversity of Dopamine Axons in Prefrontal Cortex During Classical Conditioning

The paper uses two-photon calcium imaging to show that mesocortical dopamine axons in the medial PFC exhibit selectivity for aversive cues during classical conditioning.

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.

Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

The paper introduces a brain hierarchy score to measure how brain-like deep neural networks are, finding that high-performance networks are not necessarily brain-like.

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.

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.

Sparse ordinal logistic regression and its application to brain decoding

This study introduces sparse ordinal logistic regression (SOLR) for decoding ordinal variables from brain data, showing improved performance over existing methods.

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.