Search Results for author: Kristopher T. Jensen

Found 4 papers, 3 papers with code

An introduction to reinforcement learning for neuroscience

no code implementations13 Nov 2023 Kristopher T. Jensen

We then provide an introduction to deep reinforcement learning with examples of how these methods have been used to model different learning phenomena in the systems neuroscience literature, such as meta-reinforcement learning (Wang et al., 2018) and distributional reinforcement learning (Dabney et al., 2020).

Distributional Reinforcement Learning Meta Reinforcement Learning +3

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

1 code implementation6 Oct 2022 Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin A. Dunn

These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds.

Gaussian Processes

Natural continual learning: success is a journey, not (just) a destination

1 code implementation NeurIPS 2021 Ta-Chu Kao, Kristopher T. Jensen, Gido M. van de Ven, Alberto Bernacchia, Guillaume Hennequin

In contrast, artificial agents are prone to 'catastrophic forgetting' whereby performance on previous tasks deteriorates rapidly as new ones are acquired.

Continual Learning

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data

1 code implementation NeurIPS 2020 Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin

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.

Variational Inference

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