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Synchronization can Control Regularization in Neural Systems via Correlated Noise Processes
To learn reliable rules that can generalize to novel situations, the brain must be capable of imposing some form of regularization.
Kernel Methods for the Approximation of Some Key Quantities of Nonlinear Systems
We introduce a data-based approach to estimating key quantities which arise in the study of nonlinear control systems and random nonlinear dynamical systems.
Kernel Methods for the Approximation of Nonlinear Systems
We introduce a data-driven order reduction method for nonlinear control systems, drawing on recent progress in machine learning and statistical dimensionality reduction.
On Invariance in Hierarchical Models
A goal of central importance in the study of hierarchical models for object recognition -- and indeed the visual cortex -- is that of understanding quantitatively the trade-off between invariance and selectivity, and how invariance and discrimination properties contribute towards providing an improved representation useful for learning from data.
