1–3. Introduction

Algorithms for understanding brain function. Overview of biological structures and measurement techniques involved in the study of learning. Introduction to Bayesian probability theory.

4–7. Block 1: Bayesian Inference

Perception as an inference problem. Generative models and inference algorithms. Inference in the visual system. Representing uncertainty in the brain.

8–11. Block 2: Decision Making

Actions, loss functions, and value functions. Sequential value estimation. Representing uncertainty during decision making. Evidence integration. The drift-diffusion model of evidence integration. Neural representation of decision variables.

12–15. Block 3: Navigation

Sequential decision-making problems: POMDPs. Different forms of uncertainty: model, state, and value uncertainty. Route planning and graph search problems. Elements of navigation algorithms in the brain: place cells, cognitive maps, grid cells, and path integration. Offline and online computations during navigation.

16–19. Block 4: Reinforcement Learning (RL)

Algorithms for solving sequential decision-making problems. Value-based learning and the use of reward prediction errors to model neural activity.

20–23. Block 5: Model-Based Reinforcement Learning

Building world models for sequential decision making. Behavioral markers of model-based and model-free learning. Replay of episodic memories and model-based replay. Using replay algorithms to predict neural data.

24–27. Block 6: Representation Learning

The problem of determining what information to retain and what to discard. Limitations of Bayesian inference and reinforcement learning approaches, and how to go beyond them. Predicting and measuring behavior and neural activity using representation learning algorithms.

28. Student Presentations

Current frontiers in computational neuroscience.