Course Identification

Introduction to neuroscience: Behavioral neuroscience
20223142

Lecturers and Teaching Assistants

Prof. Nachum Ulanovsky, Prof. Rony Paz, Dr. Yoram Gutfreund, Dr. Liora Las, Prof. Dov Sagi, Dr. Saikat Ray , Prof. Tali Kimchi
N/A

Course Schedule and Location

2022
Second Semester
Thursday, 09:15 - 12:00, FGS, Rm A
31/03/2022
19/08/2022

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; Regular; 3.00 points
Life Sciences (Computational and Systems Biology Track): Lecture; Elective; Regular; 3.00 points

Comments

Schedule changes:

31/3/2022 -- the first lecture of the course -- will be on ZOOM at 9:15-12:00.
14/4/2022 -- the third lecture of the course -- will be on ZOOM at 9:15-12:00.

?26/4/2022 (Tuesday) -- Special lecture at 14:15-17:00. (this is INSTEAD of 28/4/2022 - Yom Ha'Shoa.) Wolfson Auditorium
?31/5/2022 (Tuesday) -- Special lecture at 14:15-17:00 Wolfson Auditorium
?7/6/2022 (Tuesday) -- Special lecture at 14:15-17:00 Wolfson Auditorium

Prerequisites

No

Restrictions

100

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

100%

Evaluation Type

Examination

Scheduled date 1

14/07/2022
FGS, Rm C
0900-1300
N/A

Scheduled date 2

10/08/2022
N/A
0900-1300
N/A

Estimated Weekly Independent Workload (in hours)

1

Syllabus

Introduction to Neuroscience: Behavioral Neuroscience

מבוא לבסיס המוחי של התנהגות

Lecturers: Prof. Nachum Ulanovsky, Prof. Tali Kimchi, Prof. Rony Paz

 

This course will introduce students to Behavioral Neuroscience, first by providing an in-depth introduction to behavior, and then focusing on two different approaches that are common in the field: One approach ("neuropsychological") is to study animals in artificial well-controlled tasks, the other ("neuroethological") approach utilizes the animal's natural behaviors.  We will introduce general aspects, and will contrast and compare these two approaches by focusing on several well-studied, classic example systems.

 

Part A:  Introduction to Brain and Behavior  (Kimchi)

  1. Introduction to Behavior.
  2. Hormones, genes and behaviors: Mechanisms underlying social and reproductive behaviors.
  3. Neurobiology of social behaviors.  (Guest lecture by: Dr. Saikat Ray, Weizmann Institute)

 

Part B:  Neural mechanisms of Behavior – the Neuroethological approach  (Ulanovsky)

  1. Sensory ecology: evolutionary adaptations of animal sensory systems to their environment.
  2. Example system #1: Echolocation in bats: Sensory ecology, echolocation behavior, principles of biosonar signal design, neural processing.
  3. Example system #2: Multisensory integration in the brain of the barn owl. (Guest lecture by: Prof. Yoram Gutfreund, Technion)
  4. Example system #3: The bird song system: behavior, neuroanatomy, physiology, models.  (Guest lecture by: Dr. Liora Las, Weizmann Institute)
  5. Example system #4: Neurobiology of spatial cognition.  Introduction to spatial memory, orientation and navigation: (i) Navigational strategies in different animals. (ii) Sensory mechanisms of navigation: vision, magnetic navigation, etc.  The navigation circuits in the mammalian brain: Place cells, grid cells, head-direction cells.
  6. Summary of the neuroethological approach. Choosing the right behavior and the right animal model.  Natural Neuroscience.  Comparative Neuroscience.

 

Part C:  Neural mechanisms of Behavior – the Neuropsychological approach  (Paz)

  1. Introduction: Basic concepts, standard behavioral tasks.  Example system #5: Fear learning and its neural circuits.
  2. Examples system #6: Reward-based learning and its neural basis.
  3. Example system #7: Decision-making in the brain.
  4. Visual psychophysics, visual perception.  (Guest lecture by: Prof. Dubi Sagi, Weizmann Institute)

 

Learning Outcomes

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

  1. Describe classical model systems in behavioral neuroscience.
  2. Demonstrate sufficient knowledge required to take more advanced courses in neuroscience.

Reading List

N/A

Website

N/A