Course Identification

Models and data analysis in neuroscience
20213192

Lecturers and Teaching Assistants

Dr. Alon Rubin, Dr. Shai Bagon, Prof. Michail Tsodyks, Prof. Omri Barak, Dr. Oren Forkosh
Pritish Patil

Course Schedule and Location

2021
Second Semester
Thursday, 14:15 - 16:00, Belfer, Botnar Auditorium

Tutorials
Monday, 13:00 - 14:00, Belfer, Botnar Auditorium
25/03/2021
10/07/2021

Field of Study, Course Type and Credit Points

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

Comments

N/A

Prerequisites

No

Restrictions

30

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

30%
70%

Evaluation Type

Examination

Scheduled date 1

05/08/2021
Ebner Auditorium
0900-1200
N/A

Scheduled date 2

15/08/2021
Ebner Auditorium
0900-1200
N/A

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