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Found 6 papers, 3 papers with code
Minimum Description Length Control
We propose a novel framework for multitask reinforcement learning based on the minimum description length (MDL) principle.
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
We apply bGPFA to continuous recordings spanning 30 minutes with over 14 million data points from primate motor and somatosensory cortices during a self-paced reaching task.
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
To achieve this, a common approach is to record neural populations in behaving animals, and model these data as emanating from a latent dynamical system whose state trajectories can then be related back to behavioural observations via some form of decoding.
Natural continual learning: success is a journey, not (just) a destination
In contrast, artificial agents are prone to 'catastrophic forgetting' whereby performance on previous tasks deteriorates rapidly as new ones are acquired.
Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations
Sylvester, Lyapunov, and algebraic Riccati equations are the bread and butter of control theorists.
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
A common problem in neuroscience is to elucidate the collective neural representations of behaviorally important variables such as head direction, spatial location, upcoming movements, or mental spatial transformations.
