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Alexander Huth

@alex_ander

Interested in how & what the brain computes. Professor of Stats & Neuro UC Berkeley. Married to the incredible @Libertysays. he/him
Berkeley, CA alexhuth.github.io Joined April 2008
1.7K Following 5.1K Followers 2.8K Posts

Latest posts 5

Alexander Huth @alex_ander
Really excited about our new work on aphasia! Even in fairly profound aphasia, we can recover semantic maps through visual stimuli and use them to decode language. This is a big step! Language BCIs in aphasia might be possible!
Jerry Tang @jerryptang Apr 9
We're excited to share our new study on decoding brain activity in participants with post-stroke aphasia! We think this is an important step towards cognitive brain-computer interfaces for patients with language disorders

biorxiv.org/content/10.648…

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Alexander Huth @alex_ander
This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!
Sheila Macrine, Ph.D. Verified account @MacrinePhD Dec 21, 2025
A massive discovery in neuroscience: fMRI signals don't always match true neural activity. In ~40% of cases, signals increased where activity actually decreased. This challenges the core assumptions of tens of thousands of studies.
#Neuroscience #fMRI #BrainResearch #CognitiveScience #MedicalResearch neurosciencenews.com/fmri-neural-ac… via @neurosciencenew
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Alexander Huth @alex_ander
Oops—to be clear this data is @zaidzada_'s wonderful Podcast ECoG dataset, not new ECoG data! Thanks to those authors for releasing this great corpus @samnastase @HassonLab
Alexander Huth @alex_ander Aug 18, 2025
Finally, we tested whether the same interpretable embeddings could also be used to model ECoG data from Nima Mesgarani's lab. Despite the fact that our features are less well-localized in time than LLM embeddings, this still works quite well!
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Alexander Huth @alex_ander
New paper with @NeuroRJ, @csinva, @SunaGuo, Gavin Mischler, Jianfeng Gao, & @NimaMesgarani: We use LLMs to generate VERY interpretable embeddings where each dimension corresponds to a scientific theory, & then use these embeddings to predict fMRI and ECoG. It WORKS!
bioRxiv Neuroscience @biorxiv_neursci Aug 13, 2025
Evaluating scientific theories as predictive models in language neuroscience biorxiv.org/content/10.110… #biorxiv_neursci
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Alexander Huth @alex_ander
New paper with @mujn1461 & @preston_lab! We propose a simple model for human memory of narratives: we uniformly sample incoming information at a constant rate. This explains behavioral data much better than variable-rate sampling triggered by event segmentation or surprisal.
bioRxiv Neuroscience @biorxiv_neursci Aug 1, 2025
Efficient uniform sampling explains non-uniform memory of narrative stories biorxiv.org/content/10.110… #biorxiv_neursci
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