19 citations · 33 across the 6 of their papers we have counts for
6 papers
Applicability of scaling laws to vision encoding models
Takuya Matsuyama, Kota S Sasaki, Shinji Nishimoto
In this paper, we investigated how to build a high-performance vision encoding model to predict brain activity as part of our participation in the Algonauts Project 2023 Challenge.…
Brain2Music: Reconstructing Music from Human Brain Activity
Timo I. Denk, Yu Takagi, Takuya Matsuyama +4
The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world. In this paper, we introduce a metho…
Improving visual image reconstruction from human brain activity using latent diffusion models via multiple decoded inputs
Yu Takagi, Shinji Nishimoto
The integration of deep learning and neuroscience has been advancing rapidly, which has led to improvements in the analysis of brain activity and the understanding of deep learning…
Voluntary control of semantic neural representations by imagery with conflicting visual stimulation
Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto +10
Neural representations of visual perception are affected by mental imagery and attention. Although attention is known to modulate neural representations, it is unknown how imagery…
Brain-mediated Transfer Learning of Convolutional Neural Networks
Satoshi Nishida, Yusuke Nakano, Antoine Blanc +3
The human brain can effectively learn a new task from a small number of samples, which indicate that the brain can transfer its prior knowledge to solve tasks in different domains.…
Describing Semantic Representations of Brain Activity Evoked by Visual Stimuli
Eri Matsuo, Ichiro Kobayashi, Shinji Nishimoto +2
Quantitative modeling of human brain activity based on language representations has been actively studied in systems neuroscience. However, previous studies examined word-level rep…