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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Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
DINA is a dual-tower contrastive model that aligns images with mouse V1 neural activity to enable decoding and shows that low-level visual structure, not semantics or fine details, primarily supports the alignment.
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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Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
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Interpreting V1 Population Activity via Image-Neural Latent Representation Alignment
DINA is a dual-tower contrastive model that aligns images with mouse V1 neural activity to enable decoding and shows that low-level visual structure, not semantics or fine details, primarily supports the alignment.