Course Identification

Title:

Models and data analysis in neuroscience

Code:

20253541

Lecturers and Teaching Assistants

Lecturers:

Dr. Alon Rubin, Dr. Shai Bagon, Prof. Omri Barak

TA's:

Yuval Waserman, Itay Talpir

Course Schedule and Location

Year:

2025

Semester:

First Semester

When / Where:

Thursday, 10:15 - 13:00, Wolfson Auditorium

Tutorials
Sunday, 14:15 - 15:00, WSoS, Rm A

First Lecture:

07/11/2024

End date:

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

Participants:

30

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Interim:

30%

Final:

70%

Evaluation Type

Take-home exam

Scheduled date 1

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Estimated Weekly Independent Workload (in hours)

2

Syllabus

Models in neuroscience: theory and practice
Probability and statistics
Entropy and information
Reinforcement learning
Supervised learning
Linear dimensionality reduction
Non-linear dimensionality reduction
Dynamical systems
Recurrent networks
Deep learning
Model selection

Learning Outcomes

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

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.
Critically read papers which implement the above-mentioned methods and use them for their own data.

Reading List

N/A

Website

N/A