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Our latest preprint! H.A Takato Horii Shinji Nishimoto arxiv.org/abs/2505.12746 We investigated to what extent Multimodal LLMs replicate human high-dimensional emotion structures using unsupervised alignment. MLLMs achieve unprecedented accuracy at capturing emotion

Our paper on OptiNiSt is finally out in PLoS Computational Biology! OptiNiSt, developed by the OIST team (Yamane et al.), is an easy-to-use software for analyzing neural data. Araya contributed to the software development—excited for further collaboration!

I'm visiting Singapore 🇸🇬 23-27 May and giving talks on "Neuro-Inspired AI" 🧠 at NUS on Monday 26 and NTU Singapore on Tuesday 27. Talk details below, please message me if you'd like to meet during my visit 👋

Excited to share our new paper in iScience! 🎉 cell.com/iscience/fullt… We present an unsupervised alignment framework applied to neural recordings, enabling comparison of neural representations across individuals and brain areas—without relying on stimulus correspondence.

Our method paper is out with Ken Takeda, Masaru Sasaki, Kota Abe! "Unsupervised alignment in neuroscience: Introducing a toolbox for Gromov–Wasserstein optimal transport" sciencedirect.com/science/articl… In recent years, our lab has been using this unsupervised alignment framework to

Speaking at AI for Good Summit, Geneva, 8-11 July in the session on Brain-Computer Interfaces. I'm looking forward to discussing the implications of the scaling law we discovered for EEG data along with our Moonshot project on neurotechnology. aiforgood.itu.int/event/brain-co…

Wow, this research is amazing! They developed Upconversion Contact Lenses (UCLs) which enable humans to see near-infrared. I'd be interested in using this to study qualia of previously invisible spectrum. cell.com/cell/fulltext/…

Decoding cortical responses from visual input using an endovascular brain-computer interface doi.org/10.1088/1741-2… IOPPBio

Hypothesis on the Functional Advantages of the Selection-Broadcast Cycle Structure: Global Workspace Theory and Dealing with a Real-Time World arxiv.org/abs/2505.13969

🧵Training binary or spiking neural networks is hard. Gradients vanish, surrogates are noisy, batchnorm is brittle. We propose a Bayesian approach based on KL divergence minimization—and it works. Paper: arxiv.org/abs/2505.17962 Work by James Walker % Moein Khajehnejad 1/6

Happy to share a poster for the upcoming workshop on AI consciousness in Prague with our wonderful keynote speakers Anil Seth and Kathinka Evers. You can find the CfR and more info here: emw.flu.cas.cz

💡Leading researchers and AI companies have raised the possibility that AI models could soon be sentient. I’m worried that too few people are thinking about this. Let’s change that. I’m excited to announce a Digital Sentience Consortium. Check out these funding opps.👇

This new grant program on digital sentience presents a great opportunity for those interested in working in the field of consciousness and AI. I'd love to see more people consider their career in this exciting research theme. longview.org/digital-sentie…

AI x Speech Tokyo AI (TAI) event on 5th June! 📢 We have Qi Chen (Paraparas) on ASR, Nathania Nah (Tokyo Tech) on disentanglement in speech, and Meishu Song (UTokyo | 東京大学) on speech-to-speech tech

New preprint! We continue our exploration of paralinguistic features in the human brain with “Encoding of speech modes and loudness in ventral precentral gyrus” led by PhD student Aparna Srinivasan 🧵1/5

Towards a Generalized Theory of Observers arxiv.org/abs/2504.16225

1/ Can AI be conscious? My @BBSjournal target article on ‘Conscious AI and biological naturalism’ is now open for commentary proposals. Deadline is June 12. Take-home: real artificial consciousness is very unlikely along current trajectories. cambridge.org/core/journals/…

In a new Editorial, Cecilia Laschi discusses #embodied #intelligence in soft robots and delves into how articles in this month’s special issue interpret and implement this concept in different ways. More in Science #Robotics: scim.ag/43WzbUC

From PISA to AI: How the OECD is measuring what AI can do #OECD