Curriculum

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Descriptions

STAT 366: NEUROSCI 366-0 Brain Function Through the Lens of Computation

Description

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.