Lecture 1: Reinforcement learning
Stimulus $u_i$ Reward $r_i$ Expected reward: $v_i ≝ w u_i$ Prediction error: $δ_i = r_i - v_i$ Loss: $L_i ≝ δ_i^2$
Computational neuroscience (M1 ENS Ulm)
Teacher: Boris GutkinExercise 1: softmax Gibbs-policy
Exercise 1: softmax Gibbs-policy
Exercise Sheet 2: Temporal-Difference learning with discounting
Exercise Sheet 2: Temporal-Difference learning
Lecture 2: Receiver-operating characteristics
Neurons act as if they were performing reinforcement learning.
Lecture 3: Population Coding and Decision-Making
Population Coding: how neural activities relate to an animal’s behavior
Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning
Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning
Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis
Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis
Lecture 4: Addiction
Addiction: affects all parts of the brain
Lecture 5: The Binary Neuron model
Teacher: Grégory Dumont
Exercise Sheet 5: Perceptrons and Hopfield networks
Exercise Sheet 5: Perceptrons and Hopfield networks
Lecture 6: Biophysics of neurons
Teacher: Grégory Dumont
Exercise Sheet 6: integrate-and-Fire Neuron
Exercise Sheet 6: Integrate-and-Fire Neuron