A new Spectral Riemannian Alignment Score (S-RAS) based on expected projected Fisher metrics quantifies local sensitivity in neural representations and supports layer matching, training dissociations, and brain data analysis.
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A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.
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Beyond Activation Alignment: The Geometry of Neural Sensitivity
A new Spectral Riemannian Alignment Score (S-RAS) based on expected projected Fisher metrics quantifies local sensitivity in neural representations and supports layer matching, training dissociations, and brain data analysis.
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Modeling sequential cognitive states via population level cortical dynamics
A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.