Lecture 1: Bayesian perception
Lecturer: Pantelis Leptourgos
Machine Learning applied to Neuroscience (M1 ENS Ulm)
Teacher: Sophie DenèveLecture 2: Probabilistic approaches to neural computation: the Bayesian Brain
Lecturer: Lyudmila Kushnir
Problem Set 1: Gaussian Neuronal Noise & Discriminability
Problem 1: Gaussian Neuronal Noise
Lecture 3: Supervized and Unsupervized learning
Lecturer: Mirjana Maras
Lecture 4: Pooled population data
Lecturer: Mirjana Maras
Lecture 5: Reinforcement Learning
Lecturer: Lyudmila Kushnir
Problem Set 3: Reinforcement Learning
Problem 1
Lecture 6: Graphical Models and Inference
Lecturer: Pantelis Leptourgos
Lecture 7: Receptive fields
Lecturer: Sophie Denève
Lab: K-means and expectation maximization algorithms
Lecturer: Lyudmila Kushnir
Lecture 8: Coding and computing with balanced spiking networks
Lecturer: Sophie Denève
Lab: Efficient balanced networks
Lecturer: Lyudmila Kushnir
[Project] Memory Evolutive Neural Systems (MENS)
Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.