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.
Real-time computation at the edge of chaos in recurrent neural networks.Neural Computation, 16(7):1413–1436
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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.