New preprint from our lab led by Onoo-san. We propose readout representation, defining neural codes by what can be recovered, not what caused them. Inputs remain recoverable from distant features, revealing expansive, redundant codes that align neural activation with meaning arxiv.org/abs/2510.12228

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/...

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. ...

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

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