Instructor: Michelle R. Greene, Ph.D
Email: mgreene2@bates.edu
Office hours: Option 1: Book
me via calendar
Option 2: Stop by Hathorn 111 - I’m happy to meet when my door is
open!
Logistics: T/Th 8:00am - 9:20 (Hathorn 207) Lab: T or Th 1:00-4:00 (Pettengill 339 (Tue) or 227 (Th))
Prerequisites: NS/PY 160 or 200 and
PSYC 218 or any 200-level mathematics course
The brain has been described as the most complex object in the universe. How do three pounds of “electrical meat” give rise to your thoughts, memories, plans, and perceptions? In this course, we will examine methods for reverse engineering the brain and models for understanding its functions. You will be implementing these methods and models yourself in Python, thus also introducing you to the basics of scientific computing along the way!
“All models are wrong, but some models are useful” — George
Box
“The world you perceive is a drastically simplified model of the
world” — Herb Simon
How does the brain work? You’ve been taking neuroscience courses for a couple of years now — with all of the detailed knowledge that you’ve gained, how much of it helps you answer the question of ‘how the brain works’? What does it even mean to say that you understand the workings of the brain? This course takes an epistemological stance based on computation. In other words, our standard for understanding the brain is this: if you can express a neural process in mathematics, and if those expressions fit real-world data, then we understand this process. This is a rigorous definition, and as you will see in this course, it is a standard that we are still striving to achieve in most corners of neuroscience. This presents us with a tremendous intellectual opportunity: there are many ways to make a fundamental contribution to neuroscience, and we have more resources at our disposal now than ever!
The goal of this course is to give you experience in implementing significant models in neuroscience that span several levels of analysis, ranging from the single neuron to the level of the whole organism. Some of these models are historically important, such as the Nobel Prize-winning model of the action potential from Hodgkin and Huxley. Others involve using state-of-the-art machine learning in order to gain insight into brain imaging. As a consequence of this course, you will have the methods at your disposal to analyze real data in just about any neuroscience lab you enter.
As a side effect of this process, you will learn the basics of scientific computer programming. Programming is the literacy of the 21st century. As computers play a larger role in our lives, a gulf has emerged between those who use computer programs and those who write computer programs. As computer programming has obviated many professions and stands on the cusp of killing more, programming skills are a great way to future-proof your life, no matter what your post-Bates plans might be.
I won’t lie — this is not an easy course. You will likely be introduced to new mathematical concepts. You will be wearing many different hats as we shift levels of analysis from physics, through chemistry, biology, psychology, logic, computer science, and even philosophy. If you are new to computer programming, you will be learning an entirely new language by immersing yourself in it. The word choice here is intentional: success in this course will require frequent and deep engagement with difficult problems. However, I can also promise you that your perseverance will be richly rewarded with a new, deeper view of the neuroscience landscape, new frameworks for thinking, and new tools at your disposal.
By the end of the course, you should be able to:
Think computationally about neuroscience problems at several levels of analysis. This means clearly articulating the problem at hand with well-defined inputs and outputs, applying appropriate quantitative reasoning to the journey between input to output, and quantitatively evaluating the quality of your model.
Apply knowledge of the fundamentals of computer programming by manipulating, analyzing, and visualizing real-world datasets.
Conceptually articulate the underlying mathematics of these models. This means being able to describe foundational concepts in linear algebra, differential equations, and probability theory such as vector spaces, eigenvectors and eigenvalues, numerical methods for integration; and be able to use these concepts in implementing models of neural activity.
Be able to communicate effectively and persuasively about core concepts in computational neuroscience to audiences of other students in written form.
Pre-class work: There will be a reading on Perusall that you should complete before each class session. Perusall is a social reading annotation tool. In the PDF, you may highlight passages and figures for comment or question, follow up on the comments and questions or your colleagues, and use “upvoting” to endorse questions and comments. It’s a really great way of getting more out of your reading. Your annotations on Perusall will be due at midnight the night before each class. Each class will have a short question to reflect on in a Google form, due at the same time.
Class: The questions and comments from Perusall will lead to in-class discussion of the readings. Some class sessions will also feature a series of exercises in a Google Colab notebook that will prepare you for lab. If you are unable to make the live class session, it will be recorded via class capture.
Lab: There are a total of 10 labs during the course
of the semester. Labs allow you to actually implement the content you
are learning in lecture, and will give you practice in analyzing real
neural data. In some cases, you will replicate Nobel Prize-winning
work!
Each lab is contained in a Google Colab notebook. Notebooks combine code
with plain text explanations, graphs, and equations, and are a threfore
fantastic way to communicate in the languages of scientific computing.
While most labs will be completed in the time allotted for lab, if you
need extra time to finish, you may have one week past your scheduled lab
to turn in the work.
Project: You will be contributing to a student-written open textbook in this course. Each of the other three course components (class, lab, and pre-class work) will be integrated into this project.
No single assignment listed below is worth more than 10% of your final grade. This is intentional. This allows anyone to bounce back from a less-than-optimal score on any assignment. If you approach this course with consistent effort, you will succeed!
Daily reading annotations in Perusall: 0.22% per reading * 23
readings = 5% of total
There will be a reading posted for each class period on Perusall. You
can find our class Perusall link at the top of our Lyceum page. Perusall
is a social reading annotation tool. In the PDF, you may highlight
passages and figures for comment or question, follow up on the comments
and questions or your colleagues, and use “upvoting” to endorse
questions and comments. It’s a really great way of getting more out of
your reading. As the annotations will in part guide the class
activities, you are expected to complete your annotations by
midnight Eastern before each class session.
Important note: Perusall will assign you a “grade”, but
it will not be used in this class. The primary reason for this is that
their algorithm is a trade secret and I do not feel comfortable
evaluating you with an algorithm that I may or may not agree with.
Instead, your annotations will be graded on the following 0-2 scale: 0:
no annotation; 1: minimal annotation; 2: complete annotation. Please
feel free to reach out if you have any questions about what constitutes
a complete annotation.
Daily pre-class survey: 0.22% per survey * 23 sessions = 5%
of total
Each class will have a pre-class question to consider based on your
reading. I will provide a Google form (linked on this syllabus) where
you can respond. Some of the questions will be subjective, and others
are designed to stretch your comfort zone. As such, these will be graded
in a binary manner (complete / incomplete). As your responses will in
part guide the class activities, you are expected to complete your
annotations by midnight before each class session.
Daily post-class survey: 0.87% per survey * 23 sessions = 20%
of total
At the end of each class, I will release another Google form survey.
This will ask you to reflect on what the main take-aways were from the
class session, provide a final response for the pre-class question, and
sometimes practice a key Python skill or two. Your post-class survey
is due at 11:59p on each class day.
Labs: 3% per lab * 10 labs = 30% of total
There are a total of 10 labs during the course of the semester. Labs
allow you to actually implement the content you are learning in lecture
and will give you practice in analyzing real neural data. In some cases,
you will replicate Nobel Prize-winning work!
Each lab is contained in a Google Colab notebook. Notebooks combine
code with plain text explanations, graphs, and equations, and are a
therefore a fantastic way to communicate in the languages of scientific
computing. Although I will accept completed labs within one week after
the scheduled start of lab, most labs can be completed in the 3 hour
time allotment. Labs will be graded on a 0-2 scale as follows: 0:
Absent; 1: Major errors or incomplete; 2: Good (modal grade). In
order to pass the class, you must achieve at least 60% on your
labs.
Open textbook project: 40% total (distributed in several
components)
Our class project is to contribute to a student-written open textbook for
the course. This is a collaborative effort that we will be engaged with
for the whole semester Why a textbook? A few reasons:
Students in the Fall 2019 version of this course wrote version 0.0 and Fall 2020 students created the latest version of this textbook: nine chapter drafts that broadly cover the course topics. This semester, we will work to augment and improve their work. You will contribute to two chapters with a partner, and provide feedback on two other chapters as a peer reviewer over the course of the semester.
On Lyceum, there will be an interest form for various topics and types of exercises along with their due dates. Please fill out your preferences. I will do my best to ensure that each student has at least one chapter near their top choice(s). There will be four phases to each project: a critique of the current draft, a rubric, a draft, and a final draft. Your work will be peer reviewed by another student, and you will be tasked with reviewing another assignment. Your grade for the project will be broken down as:
Grade | Percentage | Grade | Percentage | |
---|---|---|---|---|
A+ | >95% | B- | 77-79% | |
A | 90-94% | C+ | 74-76% | |
A- | 87-89% | C | 70-73% | |
B+ | 84-86% | D | 50-72% | |
B | 80-83% | F | <50 |
Unfortunately, math can be a topic that brings up angst and trauma. It certainly was for me as a student! I want us to work to dismantle the culture of “math people” and “non-math people” — we are all people who think mathematically.
This class is structured to be appropriate to those with a variety of formal backgrounds, and it is therefore more conceptual than mathematical. I want you to understand and be able to explain what the various techniques and models do, not necessarily conduct mathematical proofs that demonstrate how they work. The most important thing to remember is: ask lots of questions! I am more than willing to repeat or slow down if things get difficult or to explain it from another direction.
Collaboration is the basis of all scientific discovery and is instrumental in the learning process. I strongly encourage students to work on class notebooks and labs together if possible. If you are working with other students on a class notebook or lab, you must give written credit to the collaborators, and the short answer questions must be in your own words.
Class communications will take place via Bates email. Please consider your Bates email to be the default place to look for class-related information and get into the habit of checking it daily. Your email messages to me will receive a response within 24 hours.
I firmly believe that all people, regardless of age, gender, sexual orientation, race, or religion can not only understand but find beauty, empowerment, and joy in computational neuroscience. I am firmly committed to making this class a safe space where everyone’s questions are heard and everyone’s ideas are respected. Specifically, this means:
Cheating is bad, I think we can all agree to that. The less acknowledged truth is that it’s not even worth it. Cheating cheapens the value of your work and everyone else’s, and a single violation can literally ruin your entire academic and professional career. Please remind yourself of the Bates College policy on academic integrity and its definitions of plagiarism, use/misuse of sources, and cheating. Students’ work will be closely scrutinized for plagiarism and violations of the College policy will not be tolerated. If you are concerned that your collaboration might put you at risk of an academic integrity violation, please come see me during office hours as soon as possible. In my experience, violations of academic integrity are acts of desperation. If you are ever feeling desperate enough that a few extra points in this course seem to be worth risking so much, please consider talking to someone first — that could be me, a friend, or even someone at CAPS. I want you to succeed, and am happy to talk to you if you are feeling undue pressure from this course or anything else.
The following policies apply to the following course components:
If I must cancel class due to weather or an emergency, I will inform you via the class email list. Please consider your Bates email to be the default place to look for class-related information and get into the habit of checking it daily. Similarly, if I test positive for Covid-19, have symptoms of Covid-19, or need to quarantine after spending time with someone who tests positive for Covid-19, the class will temporarily be moved to remote format via Zoom.
Lab: None
Chapter preferences: Due September 8 at 11:59p
Critiques due: None
Rubrics due: None
Drafts due: None
Peer reviews due: None
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Lab: Introduction
to Python, part 1
Critiques due: What is computational neuroscience?
September 14 11:59p
Rubrics due: What is computational neuroscience?
September 16 11:59p
Drafts due: None
Peer reviews due: None
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Lab: Introduction
to Python, part 2
Critiques due: Python September 21 11:59p
Rubrics due: Python September 23 11:59p
Drafts due: None Peer reviews due:
None
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Integrate
and Fire
Critiques due: None
Rubrics due: None
Drafts due: What is computational neuroscience? &
Python September 30 11:59p
Peer reviews due: None
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Hodgkin
and Huxley Model
Critiques due: Passive membrane models October 5
11:59p
Rubrics due: Passive membrane models October 7
11:59p
Drafts due: None
Peer reviews due: What is computational neuroscience?
& Python October 7 11:59p
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Firing
rates
Critiques due: Hodgkin and Huxley October 12
11:59p
Rubrics due: Hodgkin and Huxley October 14 11:59p
Drafts due: Passive membrane models October 14
11:59p
Peer reviews due: None
Final drafts due: What is computational neuroscience?
& Python October 14 11:59p
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: None
Critiques due: Firing rates October 18 11:59p
Rubrics due: Firing rates October 18 11:59p
Drafts due: Hodgkin and Huxley October 21 11:59p
Peer reviews due: Passive membrane models October 21
11:59p
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Reverse
correlation
Critiques due: None
Rubrics due: None
Drafts due: Firing rates October 28 11:59p
Peer reviews due: Hodgkin and Huxley October 28
11:59p
Final drafts due: Passive membrane models October 28
11:59p
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Linear
algebra
Critiques due: Reverse correlation November 2
11:59p
Rubrics due: Reverse correlation November 4
11:59p
Drafts due: None
Peer reviews due: Firing rates November 4 11:59p
Final drafts due: Hodgkin and Huxley November 4
11:59p
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Neural
networks
Critiques due: None
Rubrics due: None
Drafts due: Reverse correlation November 11
11:59p
Peer reviews due: None
Final drafts due: Firing rates November 11 11:59p
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: None
Critiques due: None
Rubrics due: None
Drafts due: None
Peer reviews due: Reverse correlation November 18
11:59p
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: None
Critiques due: None
Rubrics due: None
Drafts due: None
Peer reviews due: None
Final drafts due: None
Lab: Decoding
Critiques due: None
Rubrics due: None
Drafts due: None
Peer reviews due: None
Final drafts due: Reverse correlation December 2
11:59p
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
Lab: Representational
Similarity Analysis
Critiques due: None
Rubrics due: None
Drafts due: None
Peer reviews due: None
Final drafts due: None
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos:
To read before class:
Pre-class question:
Post-class question:
Optional supplementary videos: