Topics in Neuroscience (390-0-28)
Topic
Brain function through the lens of computation
Instructors
James E Fitzgerald
Maanasa Natrajan
Meeting Info
Technological Institute LG66: Mon, Wed, Fri 10:00AM - 10:50AM
Overview of class
Understanding brain function is a grand challenge for twenty-first century science that
promises revolutionary applications to medicine and artificial intelligence. Mathematical
modeling can contribute valuably to this understanding by allowing scientists to
formalize experimental findings and reason beyond their intuition.
This course will introduce students to the basic building blocks of neural computation,
as well as illustrate how these building blocks combine to generate myriad brain
functions. We will begin with an overview of several key principles related to neural
network dynamics and neural coding. The bulk of the course will then develop these
principles by illustrating how computational neuroscientists have used them to model
specific sensory, motor, and cognitive functions of the brain. For instance, we'll see how
neural networks can represent the sensory world, generate movement, or store
memories depending on the connections between neurons. We'll also see how these
connections can change to enable learning. Over the decades, computational
neuroscience has enjoyed a rich dialogue with machine learning and data science, and
lectures interspersed throughout the course will explain the practical importance of
computational neuroscience approaches for the modern world.
Computational neuroscience is highly interdisciplinary, and this course is open to
students with a wide variety of backgrounds, including those majoring in Neuroscience,
Data Science, Physics, Applied Mathematics, and Engineering. Problem sets will use
Matlab, and some familiarity with coding is recommended. We recommend that
Neuroscience majors complete the core NEUROSCI 202 and 206 courses first.
Registration Requirements
Students must have completed NEUROSCI 202-0 and NEUROSCI 206-0; or BIOL_SCI 302-0.
Learning Objectives
Understand how the fundamental principles of neural computation combine in
different ways to generate a diversity of brain functions
Learn how to write, run, and modify computer code to simulate models and
analyze neural data
Gain exposure to the conceptual and mathematical tools of computational
neuroscience
Solve example interdisciplinary problems at the interface of computational
neuroscience, artificial intelligence, and medicine.
Teaching Method
The course will be divided into five Units that will cover a wide range of exciting topics in
computational neuroscience.
Unit 1, Course introduction (1 week): e.g., what is computational neuroscience?,
mathematical foundations
Unit 2, Fundamental elements of neural computation (3 weeks): e.g., neurons,
neural coding, synapses, neural networks, learning
Unit 3, Visual processing (2 weeks): e.g., encoding models, efficient coding, deep
neural networks
Unit 4, Motor control (2 weeks): e.g., central pattern generators, low-dimensional
dynamics, recurrent neural networks
Unit 5, Cognition (2 weeks): e.g., perception, memory, decision making
The stated durations of each Unit are approximate and should only be taken to illustrate
the general cadence of the course. The tenth week of the course is set aside as a
reading period, and no new content will be presented.
Evaluation Method
Daily Questions (10% of grade): Students will submit responses to a few multiple-choice
questions after each class to help them consolidate the key concepts presented during
the lecture. The questions will be posted after each class and due before the following
lecture. You are encouraged to discuss the questions with other students in the course,
but everyone must submit their own response. Do not use generative AI.
Problem Sets (70% of undergraduate grade, 50% of graduate grade): There will be nine
weekly problem sets, each of which will count equally to your grade. The problem sets
will be assigned and due on Mondays. You are encouraged to collaborate with other
students in the course, but everyone must prepare their own solutions. Do not use
generative AI.
Research Literature Assessment (20% of graduate grade): Graduate students in the
course will read a paper from the computational neuroscience literature, critically assess
it, and present it orally to the course instructors (i.e., Dr. Fitzgerald, plus course
Teaching Fellows and Teaching Assistants). The specific paper will be selected in
consultation with Dr. Fitzgerald and presented at a time during the course that
coordinates with the paper's scientific themes.
Final Exam (20% of grade): The final will be a take-home exam. This exam will
resemble a problem set. However, you may not collaborate with other students or
discuss the problems. Do not use generative AI. The final may include content from any
aspect of the course.
Class Materials (Required)
I will provide written notes following each lecture, and lecture videos will be posted on
Canvas. I will be developing the lectures to be complete and self-contained, and the
course will not follow a textbook. Students interested to learn more about a topic
covered during the lecture may approach me for additional suggested readings.
Class Materials (Suggested)
None.
Class Notes
This class will be open for registration to non-Neuroscience Majors beginning February 28th, 2025.
Enrollment Requirements
Enrollment Requirements: Students must have completion of MATH 220-2 or test (or transfer) equivalent or higher.
Associated Classes
DIS - Technological Institute L221: Thurs 3:00PM - 4:00PM