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 GutkinLecture 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
Lecture 4: Addiction
Addiction: affects all parts of the brain
Lecture 5: The Binary Neuron model
Teacher: Grégory Dumont
Lecture 6: Biophysics of neurons
Teacher: Grégory Dumont