51 citations · 55 across the 12 of their papers we have counts for
12 papers
Phantom Evidence: How and Why Generative AI Manufactures False Positives in Science
Yukiyasu Kamitani, Ken Shirakawa
Four centuries ago Francis Bacon warned against the anticipations of nature, hasty generalization that wins assent on a few facts, and set against it the table of absence: checking…
Beyond Object-Level Alignment: Do Brains and DNNs Preserve the Same Transformations?
Yukiyasu Kamitani
Brain-DNN alignment is usually assessed through stimulus-level correspondence or stimulus-set geometry. Inspired by category theory, we operationalize a different question: do brai…
Advancing credibility and transparency in brain-to-image reconstruction research: Reanalysis of Koide-Majima, Nishimoto, and Majima (Neural Networks, 2024)
Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka +2
A recent high-profile study by Koide-Majima et al. (2024) claimed a major advance in reconstructing visual imagery from brain activity using a novel variant of a generative AI-base…
Readout Representation: Redefining Neural Codes by Input Recovery
Shunsuke Onoo, Yoshihiro Nagano, Yukiyasu Kamitani
Sensory representation is typically understood through a hierarchical-causal framework where progressively abstract features are extracted sequentially. However, this causal view f…
Overcoming Output Dimension Collapse: When Sparsity Enables Zero-shot Brain-to-Image Reconstruction at Small Data Scales
Kenya Otsuka, Yoshihiro Nagano, Yukiyasu Kamitani
Advances in brain-to-image reconstruction are enabling us to externalize the subjective visual experiences encoded in the brain as images. A key challenge in this task is data scar…
Visual Image Reconstruction from Brain Activity via Latent Representation
Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa
Visual image reconstruction, the decoding of perceptual content from brain activity into images, has advanced significantly with the integration of deep neural networks (DNNs) and…
Spurious reconstruction from brain activity
Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka +4
Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As the…
Inter-individual and inter-site neural code conversion without shared stimuli
Haibao Wang, Jun Kai Ho, Fan L. Cheng +4
Inter-individual variability in fine-grained functional brain organization poses challenges for scalable data analysis and modeling. Functional alignment techniques can help mitiga…
Sound reconstruction from human brain activity via a generative model with brain-like auditory features
Jong-Yun Park, Mitsuaki Tsukamoto, Misato Tanaka +1
The successful reconstruction of perceptual experiences from human brain activity has provided insights into the neural representations of sensory experiences. However, reconstruct…
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…
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features
Tomoyasu Horikawa, Yukiyasu Kamitani
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 demonst…
Generic decoding of seen and imagined objects using hierarchical visual features
Tomoyasu Horikawa, Yukiyasu Kamitani
Object recognition is a key function in both human and machine vision. While recent studies have achieved fMRI decoding of seen and imagined contents, the prediction is limited to…