Topics in Neuroscience (390-0-28)
Topic
Brain function through the lens of computation
Instructors
James Eliot Fitzgerald
James Fitzgerald is a theoretical neuroscientist with active interests in sensory processing, learning and memory, motor control, whole-brain dynamics, and neural networks. His interdisciplinary training encompasses physics, mathematics, and biology.
Meeting Info
Technological Institute LG68: 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 the course is geared towards students
with a wide variety of backgrounds, including those majoring in Neuroscience, Data Science, Physics, Applied Mathematics, and Engineering. Problem sets will use Mathlab, and some familiarity with coding in Mathlab is recommended. We recommend that Neuroscience majors complete the core NEUROSCI 202-0 and 206-0 courses first.
Registration Requirements
MATH 220-2 or higher.
Learning Objectives
Understand how the fundamental principles of neural computation quantitatively combine to
generate diverse brain functions
Learn how to run and modify computer code to simulate models and analyze neural data
Gain exposure to the conceptual and mathematical tools of computational neuroscience
Understand how computational neuroscience can be applied in artificial intelligence and
medicine.
Class Materials (Required)
Instructor will provide reading material related to lectures.
Class Materials (Suggested)
None.
Class Notes
This course will open up to non-majors on February 22nd.
Enrollment Requirements
Enrollment Requirements: Students must have completion of MATH 220-2 or test (or transfer) equivalent.