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.
High-dimensional geometry of population responses in visual cortex
4 Pith papers cite this work, alongside 571 external citations. Polarity classification is still indexing.
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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
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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.
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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.
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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.