Syllabus for SSII (2026)#

Welcome to BIPN 145! This is a course about how we study the nervous system. You’ll be asked to think, plan, and write like a neuroscientist.

You may be surprised about some of the topics in this course. You’ll spend a lot of time learning about nervous systems, but we’ll also spend some time talking about experimental design, technology, ethics, and analyzing big data sets.

Instructional Team#

Role

Name

Email

Instructor

Ashley Juavinett

ajuavine@ucsd.edu

Teaching Assistant

Catherine Ngoc Phan

cnp005@ucsd.edu

Reader

Julianne Lee

jul144@ucsd.edu

Staff Research Associate

Breonna Gonzalez

Office hours: By appointment

Lecture: TuWThF 11:00a–12:20p in MALK B90

Lab: TuWThF 1:00p–4:20p in YORK 1310

Course learning objectives#

  • Collect and evaluate neural data from various organisms

  • Apply principles of neural communication to multiple model systems

  • Describe the breadth of techniques in neuroscience and the experimental questions they are suited to answer

  • Develop an appreciation for and practical insight into the process of research

  • Communicate research to peers as well as a broader audience

Attendance policies#

You are expected to attend our in-person lab sessions, but lecture attendance is not mandatory. However, as you soon will see, even our lecture sessions will not be unidirectional — these will be active learning sessions where we co-create our learning. These lecture sessions will be podcasted/recorded, but you will be expected to make up any in-class activities that you missed.

If you cannot attend a lab session due to illness or for any other reason, please contact both Dr. J and our teaching assistant(s) ASAP so that we can work with you on accommodations.

Enrolled and waitlisted students must attend the first lab session. Additional details: http://biology.ucsd.edu/go/ug-labs. You do not need to inform us if you will be missing a lecture session.

Additional resources#

There is a list of resources listed here to help you thrive in this course and on campus. If there is anything you think we can help you out with, please feel free to reach out to the TA or Dr. J.

Grading#

  • QUIZZES & ASSIGNMENTS (350 pts, 20–50 pts each) — Includes quizzes for individual lab activities and pre-lab quizzes.

  • EARTHWORM REPORT (125 pts) — Your first lab report will summarize our earthworm experiments.

  • FINAL PROJECT (250 pts) — Project proposal, presentation, and written report.

  • MIDTERMS (250 pts, 125 pts each)

  • PROFESSIONALISM (25 pts)

Late assignments#

  • For individual assignments only, you have a 3-day late bank that you may use over the course of the session to extend your deadlines without penalty.

  • You can request a late bank before the deadline by filling out this form: https://forms.gle/SAeRJKyCWRtdhbFx7.

  • Assignments not protected by the late bank will lose -10% for each day they are late.

  • In the case of extenuating circumstances requiring a submission more than three days late, please contact Dr. J directly.

Additional notes on grading#

  • Final scores will be converted to letter grades, where A = 90–100%, B = 80–89.99%, C = 70–79.99%, D = 60–69.99%, and F = 0–59.99%. For plus and minus grades, A+ = 97–100, A = 93–96.99, A- = 90–92.99, B+ = 87–89.99, B = 83–86.99, B- = 80–82.99, and so on.

  • Final scores are as you see them on Canvas, once all of your assignments are graded. There is no rounding up to the closest score.

Please note that add/drop deadlines are different for lab courses than lecture courses. Students who drop a Biology lab class after the end of the second class meeting will be assigned a “W”. Additional details: http://biology.ucsd.edu/go/ug-labs. Summer Session II enrollment deadlines: drop without a “W” by August 14, 2026, and drop with a “W” by August 21, 2026 (full calendar).

Course philosophy#

A note on our course’s environment#

We’ll be working together to create an equitable and inclusive environment of mutual respect, in which we all feel comfortable to share our moments of confusion, ask questions, and challenge our understanding. Everyone should be able to succeed in this course. If you do not feel that is the case please let me know.

To help accomplish this:

  • I’ll ask for your preferred name & pronouns on our incoming survey. If these change over the course of the session, please let me know.

  • Please don’t hesitate to come and talk with me if you feel like your performance in the class is being impacted by your experiences outside of class.

  • I am constantly learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it.

  • As a participant, you should also strive to honor the diversity of your classmates.

On the equity & diversity of our course content#

Despite the way we typically conceive of the scientific process, much of science is subjective and is historically built on a small subset of privileged voices. In this class, we will make an effort to show the work of diverse scientists, but limits still exist on this diversity. I acknowledge that it is possible that there may be both overt and covert biases in the material due to the lens with which it was written, even though the material is primarily of a scientific nature. Integrating a diverse set of experiences is important for a more comprehensive understanding of science. To this end, we will discuss diversity in neuroscience as part of the course from time to time.

Course accommodations#

If you need accommodations for this course due to a disability, please contact the Office for Students with Disabilities (osd@ucsd.edu) for an Authorization for Accommodation letter. Please speak with me in the first week of class if you intend to apply for accommodations. For more information, visit http://disabilities.ucsd.edu.

This course, and the work it entails, is for you#

So, you won’t benefit if others (or a generative AI) do your work. Cases of academic dishonesty or cheating will be first handled by me, and then by the Academic Integrity Office. If you become aware of cheating in this class, you can anonymously report it: https://academicintegrity.ucsd.edu/

Lab safety is important#

Enrolled and waitlisted students must successfully complete the Biology Lab Safety Training and Assessment before the first lab session: https://biolabclass-safetyquiz.ucsd.edu/introduction. You will not be allowed into the lab for the second in-person lab session unless you have successfully passed the safety assessment. Note that you do not need to bring your own personal protective equipment (e.g. a lab coat or goggles) for this course. We will provide gloves when needed.

Course management & texts#

Lab manual#

The lab manual is contained on the same website as this syllabus.

Canvas#

This course will be using Canvas to manage content and grades. You can log in by going to http://canvas.ucsd.edu. If you need any technical assistance with Canvas, please alert your instructor and send an email to servicedesk@ucsd.edu.

Textbook#

There is no mandated textbook for this course, but most of the background material can be found in Purves et al. (2018) Neuroscience. We’ll also use Carter & Shieh (2015) Guide to Research Techniques in Neuroscience, which can be found online here (link is also under Resources on Canvas). In addition, for each module I have curated resources that will be useful to you. You can find these on Canvas, or on the course website.

Software for this class#

It will be helpful to have the following software on your computer, since you’ll often need to rely on a personal computer. If you have any issues with these or would prefer not to download anything onto your personal computer, you are welcome to rely on a teammate or reach out to us for additional accommodations. If you need a laptop for the session, you can request one.

Microsoft Office#

It may be useful to have Microsoft Office in this course. You can find it here.

LabChart Reader#

If you can, please download LabChart Reader on your personal computer. We’ll be using this to analyze data you collect.