Our article is out in Annual Review of Vision Science: “Visual Image Reconstruction from Brain Activity via Latent Representation” We trace the path from early brain decoding to modern NeuroAI, highlight progress & pitfalls, and discuss future directions www.annualreviews.org/content/jour...

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

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/...

Out now! @ykamit.bsky.social and colleagues present a neural code conversion method to align brain data across individuals without shared stimuli. The approach enables accurate inter-individual brain decoding and visual image reconstruction across sites. #compneurosky www.nature.com/articles/s43...

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#neurotech#bci"Our findings suggest that […]

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

Our overview of visual image reconstruction from brain activity is in press at Annual Review of Vision Science. We explore its foundations, how the field evolved, where it went astray—and how to set it right arxiv.org/abs/2505.08429

Yukiyasu Kamitani, Misato Tanaka, Ken Shirakawa Visual Image Reconstruction from Brain Activity via Latent Representation https://arxiv.org/abs/2505.08429

Updated preprint, now in press at Neural Networks arxiv.org/abs/2405.10078

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. 🧵

Brain-to-AI and Brain-to-Brain Functional Alignments speakerdeck.com/ykamit/brain...

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

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. ...

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

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

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...