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.
Brain decoding: toward real-time reconstruction of visual perception
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NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
CineNeuron improves fMRI-to-video reconstruction by combining bottom-up semantic enrichment with top-down Mixture-of-Memories integration and outperforms prior methods on benchmarks.
Spiking SDM and transformers implement identical functional operations for sequences via cosine similarity retrieval, unified by a phase-latency isomorphism between spike timing and sinusoidal positional encoding.
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
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.
MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.
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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NeuroFlow: Toward Unified Visual Encoding and Decoding from Neural Activity
NeuroFlow is the first unified flow model for bidirectional visual encoding and decoding from neural activity using NeuroVAE and cross-modal flow matching.
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Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction
CineNeuron improves fMRI-to-video reconstruction by combining bottom-up semantic enrichment with top-down Mixture-of-Memories integration and outperforms prior methods on benchmarks.
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Spiking Sequence Machines and Transformers
Spiking SDM and transformers implement identical functional operations for sequences via cosine similarity retrieval, unified by a phase-latency isomorphism between spike timing and sinusoidal positional encoding.
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OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
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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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Multi-Level Bidirectional Biomimetic Learning for EEG-Based Visual Decoding
MB2L achieves 80.5% top-1 and 97.6% top-5 accuracy on zero-shot EEG-to-image retrieval by using biomimetic modules and bidirectional contrastive learning to align neural and visual features.