Yuki Kamitani
@ykamit.bsky.social
210 followers 130 following 13 posts
Yukiyasu Kamitani | 神谷之康 Neuroscientist and brain decoder http://kamitani-lab.ist.i.kyoto-u.ac.jp http://youtube.com/@ATRDNI/videos https://twitter.com/ykamit
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ykamit.bsky.social
New preprint from our lab, led by Otsuka-san. In brain–AI alignment, linear transformations are common—but when features ≫ samples, outputs collapse onto the training set, breaking zero-shot predictions. We mathematically show how data and model sparsity can help avoid this.
arxiv.org/abs/2509.15832
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. Achieving such reconstruction with limited training data requ...
arxiv.org
Reposted by Yuki Kamitani
plosbiology.org
Reconstructing sounds from #fMRI data is limited by its temporal resolution. @ykamit.bsky.social &co develop a DNN-based method that aids reconstruction of perceptually accurate sound from fMRI data, offering insights into internal #auditory representations @plosbiology.org 🧪 plos.io/4fhNw1Z
Schematic overview of the proposed sound reconstruction pipeline. Left:  DNN feature extraction from sound. A deep neural network (DNN) extracts auditory features at multiple levels of complexity using a hierarchical framework. Right: Sound reconstruction. The reconstruction pipeline starts with decoding DNN features from fMRI responses using trained brain decoders. The audio generator then transforms these decoded features into the reconstructed sound.
ykamit.bsky.social
Can we hear what's inside your head? 🧠→🎶
Our new paper, led by Jong-Yun Park, presents an AI-based method for reconstructing arbitrary natural sounds directly from a person's brain activity measured with fMRI.
journals.plos.org/plosbiology/...
Reposted by Yuki Kamitani
seeingwithsound.mas.to.ap.brid.gy
Spurious reconstruction from brain activity https://www.sciencedirect.com/science/article/pii/S0893608025003946 by @ykamit et al.; more information in the Bluesky thread https://bsky.app/profile/kencan7749.bsky.social/post/3lri44htxek25 #bci #neurotech #neuroscience

"Our findings suggest that […]
Original post on mas.to
mas.to
Reposted by Yuki Kamitani
kencan7749.bsky.social
Our paper is now accepted at Neural Networks!

This work builds on our previous threads in X, updated with deeper analyses.

We revisit brain-to-image reconstruction using NSD + diffusion models—and ask: do they really reconstruct what we perceive?

Paper: doi.org/10.1016/j.ne...
🧵1/12
Redirecting
doi.org
Reposted by Yuki Kamitani
arxiv-cs-cv.bsky.social
Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa
Visual Image Reconstruction from Brain Activity via Latent Representation
https://arxiv.org/abs/2505.08429
Reposted by Yuki Kamitani
martinhebart.bsky.social
I promised to write about my thoughts on the status of the field of neuroAI, some of the big challenges we are facing, and the approaches we are taking to address them. This is super selective on the topic of finding a good model but in my view it affects the field as a whole. Here we go. 🧵
ykamit.bsky.social
New preprint, led by Ken Shirakawa. Using inappropriate generative AI methods and naturalistic data, seemingly realistic but spurious visual image reconstruction can be generated from brain data (even from random data). We describe it and formulate how it occurs

arxiv.org/abs/2405.10078
Spurious reconstruction from brain activity
Advances in brain decoding, particularly visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As these methods aim...
arxiv.org
ykamit.bsky.social
These stimuli were presumably adjacent video frames extracted from a single movie. Was this known to the community?
ykamit.bsky.social
While investigating the issues of questionable brain decoding and reconstruction (osf.io/nmfc5/#!), Misato Tanaka from my lab found that the seminal paper by Nishimoto, Naselaris, Benjamini, Yu, & Gallant (2011) appears to use highly similar stimuli across both training and test sets. ...
ykamit.bsky.social
It enables inter-site visual image reconstruction (Deeprecon⬌THINGS⬌NSD), where a source subject's brain data is analyzed using a model trained on a dataset from a different site to reconstruct the viewed image, achieving near-equivalent quality to within-site performance
ykamit.bsky.social
New preprint from our lab! Led by Wang-san, this work introduces a content-loss-based functional alignment of brain data, which does not require shared stimuli between subjects/datasets; greatly expanding the potential of data reuse
arxiv.org/abs/2403.11517
ykamit.bsky.social
Exhibition of Pierre Huyghe "Liminal"
From 17 March 2024 to 24 November 2024 At Punta della Dogana, Venice, Italy
www.pinaultcollection.com/palazzograss...
We provided brain-decoded images and moves for some of the works
ykamit.bsky.social
Exhibition of Mogens Jacobsen"Restruktion" at Ringsted Gallery, Denmark, where one of the works was created in collaboration with my lab
kunsten.nu/artguide/cal...