Course Identification

Title:

Models and data analysis in neuroscience

Code:

20233442

Lecturers and Teaching Assistants

Lecturers:

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

TA's:

Itay Talpir, Yuval Waserman

Course Schedule and Location

Year:

2023

Semester:

Second Semester

When / Where:

Tuesday, 09:15 - 12:00, Wolfson Auditorium

Tutorials
Sunday, 13:15 - 14:00, FGS, Rm C

First Lecture:

18/04/2023

End date:

21/07/2023

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; Obligatory; Regular; 3.00 points

Comments

On June 27th, the lecture will be held at FGS room A.
Take-home exam: 01/08/23

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

01/08/2023

Location

N/A

Time

-

Remarks

Take-home exam

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