no code implementations • 13 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
1 code implementation • 6 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.
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.
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.