TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
The wisdom of a crowd of brains: A universal brain encoder
3 Pith papers cite this work. Polarity classification is still indexing.
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A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.
ViBE generates M/EEG signals from visual stimuli by reconstructing neural responses with a TSC-VAE and aligning CLIP image features to its latent space via Q-Former, MSE, and sliced Wasserstein losses.
citing papers explorer
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A foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a multimodal AI model that predicts human brain activity more accurately than linear encoding models and recovers established neuroscientific findings through in-silico testing.
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Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
A meta-optimized in-context learning approach enables training-free cross-subject semantic visual decoding from fMRI by inferring individual neural encoding patterns via hierarchical inference on a few examples.
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ViBE: Visual-to-M/EEG Brain Encoding via Spatio-Temporal VAE and Distribution-Aligned Projection
ViBE generates M/EEG signals from visual stimuli by reconstructing neural responses with a TSC-VAE and aligning CLIP image features to its latent space via Q-Former, MSE, and sliced Wasserstein losses.