Instructor: Jieyu Zheng (Me!)

The updated syllabus is on the course website on Canvas.

Course overview

Course Format

Organizational Meeting: Wednesday, January 15th at 4 PM, 240a CNRB

3 units: meeting 1-hour per week (most of the 10 weeks) with short reading assignments.

How to take this course

Undergraduates and graduates: go to REGIS through access.caltech.edu and select Bi23 sec 1

Faculty, postdocs and staff: go to the first lecture and complete an Audit Registration Form.

Hope to see you there!

Syllabus (subject to changes)

Instructors: Jieyu Zheng, PhD Candidate in Neurobiology, Meister Lab

Meetings: TBD upon the first organizational meeting below

Organizational meeting: Wednesday, January 8th at 4 PM, 240a CNRB

Course Description:

This tutorial offers an engaging exploration of ethology, focusing on how non-human animals learn. We will begin with foundational theories from Charles Darwin and continue learning about the modern ethology, established by Nobel laureates Nikolaas Tinbergen and Konrad Lorenz. We will also cover behaviorism, including work from Ivan Pavlov and B.F. Skinner. The course will then introduce current advancements in neuroethology, computational ethology, and the intersection of biological intelligence and artificial intelligence, featuring guest presentations from researchers in these fields. Throughout the term, students will enjoy a range of readings on animal behaviors in both field and laboratory settings. The course will conclude with a mini ethology project through expert-guided fieldwork. The take-home lesson for the students would not only include an overview of the science of learning, but also an appreciation of behavior as a language of the nature, and perhaps even a special skillset to teach their pets new tricks.

Learning goals:

• Foundations of Ethology: Students will gain a basic understanding of the history and evolution of ethology as a scientific discipline. They will also learn key methods for observing, studying, and analyzing animal behaviors.
• Critical Reading and Literature Review: Through assigned readings from scientific journals and textbook chapters, students will develop skills in critically evaluating academic literature and taking organized notes.
• Project Design and Research Collaboration: Students will engage in hands-on learning through mini-projects. They will design hypothesis-driven research, develop project proposals, and apply research methods introduced in the course. Collaborative teamwork will be emphasized in project design and reporting.
• Presentation and Peer Evaluation: Students will present their project findings and actively participate in peer evaluations, honing their skills in scientific communication and constructive feedback.

Lecture topics:

1. What Is Learning? An introduction to the concept of learning, its definitions, and its role in animal behavior.
2. Charles Darwin and Theory of Evolution: Exploring the evolutionary basis of behavior in humans and animals through Darwin’s contributions.
3. Modern Ethology: The foundational work of Tinbergen and Lorenz in establishing the principles of modern ethology.
4. Behaviorism: Understanding classical and operant conditioning as frameworks for studying behavior.
5. Computational Ethology and Neuroethology: Integrating computational tools and neuroscience to study behavior at multiple levels.
6. Fieldwork and presentations: Hands-on exploration of animal behavior through campus-based field studies. Student presentations of their research projects, synthesizing course concepts and findings.

Format

Course Meetings:

• The course consists of 55-minute weekly meetings held in Chen 240A on a set schedule (discussed over the first organizational meeting on Jan. 8th).
• Each session will include a 45-minute interactive lecture and a 10-minute segment dedicated to student presentations and/or open discussions related to course topics.

Weekly Assignments:

• Assignments will be given weekly for most of the weeks and are due the night before the scheduled class meeting.

Fieldwork Project:

• The fieldwork project will take place in February during lecture time (details TBD)
• Students are required to give an oral presentation of their fieldwork content or submit a short written report.

Grading:

This is a pass/fail (P/F) tutorial with a very light workload. Attendance is crucial, as it directly impacts the collective learning experience. Grading is based on the following components:

• Attendance (50%)
  • Attendance is mandatory for all lecture sessions to ensure full participation in discussions and presentations.
  • Active engagement (including asking questions, answering questions and presentations) during classes is expected.
• Assignments (30%)
  • Reading assignments and small written tasks will be assigned most of the weeks.
• Fieldwork Project (20%)
  • The final grade includes the attendance of the fieldwork lecture (10%) and project planning and presentations (10%).

In short: if you go to all lectures, including the fieldwork, you are guaranteed to pass this class.

Time Management:

• If you have foreseeable commitments, such as a medical school interview or another class project, plan ahead. It’s perfectly acceptable to submit assignments early.
• If you wish to work significantly ahead of schedule, you may petition the instructors to access materials in advance.

Extensions and Missed Lectures:

• In the event of a severe circumstance that prevents you from completing your work or attending a lecture, notify the instructor at least two days in advance.
• Missing four or more lectures will lead to Fail unless extraordinary circumstances are discussed with the instructor.

Communication:

• All course materials and communication will be handled through Canvas. The instructor will also respond to emails during weekdays (usually same-day turnaround).
• There will be no office hours for this course.

Collaboration Policy:

• Collaboration on homework assignments is encouraged unless explicitly stated otherwise. Students may consult with peers or use outside reference materials.
• Citations: Any use of external references, including AI tools such as ChatGPT, Claude, or similar systems, must be properly cited.
• All submitted work must be written independently and reflect your understanding of the subject matter at the time of submission.

This syllabus was polished by ChatGPT 4o.