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Found 3 papers, 1 papers with code
LINOCS: Lookahead Inference of Networked Operators for Continuous Stability
Such systems, often modeled as dynamical systems, typically exhibit noisy high-dimensional and non-stationary temporal behavior that renders their identification challenging.
Testing for context-dependent changes in neural encoding in naturalistic experiments
We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data.
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics
Learning interpretable representations of neural dynamics at a population level is a crucial first step to understanding how observed neural activity relates to perception and behavior.
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