Course Identification

Models and data analysis in neuroscience
20253541

Lecturers and Teaching Assistants

Dr. Alon Rubin, Dr. Shai Bagon, Prof. Omri Barak
Yuval Waserman, Itay Talpir

Course Schedule and Location

2025
First Semester
Thursday, 10:15 - 13:00, Wolfson Auditorium

Tutorials
Sunday, 14:15 - 15:00, WSoS, Rm A
07/11/2024
30/01/2025

Field of Study, Course Type and Credit Points

Life Sciences: Lecture; Elective; Regular; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; 3.00 points

Comments

N/A

Prerequisites

No

Restrictions

30

Language of Instruction

English

Registration by

11/12/2024

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

30%
70%

Evaluation Type

Take-home exam

Scheduled date 1

17/02/2025
N/A
-
Take home exam

Estimated Weekly Independent Workload (in hours)

2

Syllabus

  1. Models in neuroscience: theory and practice
  2. Probability and statistics
  3. Entropy and information
  4. Reinforcement learning
  5. Supervised learning
  6. Linear dimensionality reduction
  7. Non-linear dimensionality reduction
  8. Dynamical systems
  9. Recurrent networks
  10. Deep learning
  11. Model selection

Learning Outcomes

Upon successful completion of this course students should be able to:

  1. Demonstrate familiarity with some of the data analysis techniques and models which are commonly used in neuroscience, and will learn the assumptions which underlie each of them.
  2. Critically read papers which implement the above-mentioned methods and use them for their own data. 

Reading List

N/A

Website

N/A