fmri

This paper investigates whether fMRI representations from different subjects' visual cortices can be aligned using unsupervised geometric methods, finding evidence for approximately isometric structure across individuals, extending the Platonic Representation Hypothesis to human brains.

NeuralSet is a new Python package that provides fast, scalable preprocessing and embedding tools for multimodal neuro-AI data including fMRI, EEG, MEG, ECoG, spikes, plus text, audio, video and images.

This paper investigates whether Brain Score, a metric comparing language model representations to human fMRI activations during reading, is truly capturing human-like language processing or merely structural similarity. The researchers train language models on diverse natural languages and non-linguistic structured data (genome, Python, nested parentheses), finding that models trained on different languages and even non-linguistic sequences achieve similar Brain Score performance, suggesting the metric may not be sensitive enough to distinguish human-specific processing.

This paper introduces a meta-optimized approach for semantic visual decoding from fMRI signals that generalizes to novel subjects without fine-tuning, using in-context learning to infer unique neural encoding patterns from a small set of image-brain activation examples. The method achieves strong cross-subject and cross-scanner generalization without requiring anatomical alignment or stimulus overlap.