Search Results for author:
Found 6 papers, 1 papers with code
Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution
Neuroscience has long been an essential driver of progress in artificial intelligence (AI).
A Local Temporal Difference Code for Distributional Reinforcement Learning
Recent theoretical and experimental results suggest that the dopamine system implements distributional temporal difference backups, allowing learning of the entire distributions of the long-run values of states rather than just their expected values.
Dynamic allocation of limited memory resources in reinforcement learning
We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards.
Neurons and Cognition
Optimal decision-making with time-varying evidence reliability
The work presented here describes the computation and characterization of optimal decision-making in the more realistic case of an evidence reliability that varies across time even within a trial.
Demixing odors - fast inference in olfaction
Thus, at the behavioral level, the two algorithms make very similar predictions.
Complex Inference in Neural Circuits with Probabilistic Population Codes and Topic Models
This ability requires a neural code that represents probability distributions and neural circuits that are capable of implementing the operations of probabilistic inference.
