Block-sparse featurizers recover visual concepts as two- to four-dimensional manifolds and describe activations more compactly than direction-based methods via minimum-description-length comparison.
High-dimensional geometry of population responses in visual cortex
5 Pith papers cite this work, alongside 571 external citations. Polarity classification is still indexing.
5
Pith papers citing it
571
external citations · Crossref
years
2026 5verdicts
UNVERDICTED 5representative citing papers
Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
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.
A neural-network approximation of heteroclinic dynamics, interpretable as an Amari-type neural-field system, reproduces sequential transitions among cognitive states.
Recurrent networks driven by low-dimensional sensory dynamics generically embed those dynamics as smooth internal manifolds, with prediction accuracy forcing state separation up to a resolution limit set by prediction error.
citing papers explorer
-
Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds
Block-sparse featurizers recover visual concepts as two- to four-dimensional manifolds and describe activations more compactly than direction-based methods via minimum-description-length comparison.
-
From Chaos to Synchrony in Recurrent Excitatory-Inhibitory Networks with Target-Specific Inhibition
Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.
-
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.
-
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.
-
Embedding of Low-Dimensional Sensory Dynamics in Recurrent Networks: Implications for the Geometry of Neural Representation
Recurrent networks driven by low-dimensional sensory dynamics generically embed those dynamics as smooth internal manifolds, with prediction accuracy forcing state separation up to a resolution limit set by prediction error.