BrainCause recovers known visual localizations and finds new candidate representations by validating causal specificity via counterfactual stimuli and encoding models, showing activation alone produces many false positives.
The wisdom of a crowd of brains: A universal brain encoder
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
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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From Activation to Causality: Discovery of Causal Visual Representations in the Human Brain
BrainCause recovers known visual localizations and finds new candidate representations by validating causal specificity via counterfactual stimuli and encoding models, showing activation alone produces many false positives.
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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.