Informazioni sul corso

This course will introduce students to modeling and data analysis techniques in Computational Neuroscience. 

There are four blocks, taught by seven speakers, each consisting of two or three sessions (for a total of thirteen 2h sessions). The course is passed 1) by completing the tutorials of all four blocks and 2) by handing in two mini-projects (each block will propose a mini project and students must choose and complete 2 out of the 4 possible projects).

The program is the following:
- introduction: 2 sessions on deep learning in neuroscience
- block 1: 2 sessions on generalized linear models (tutorial included + mini-project option)
- block 2: 3 sessions on Reinforcement Learning (tutorial included + mini-project option)
- block 3: 2 sessions on model fitting and selection (tutorial included + mini-project option)
- block 4: 2 sessions on Natural Language Processing (tutorial included + mini-project option)
- 2 final sessions on Bayesian decision-making

Student presence is required. More than 2 unjustified absences will result in an automatic disqualification.