Pedagogy

Neural Computation

Carnegie Mellon University
Instructor: Xaq Pitkow
Spring, TTh 2:00–3:20, Mellon 115. 3 credits. [Syllabus]

How does the brain work? Understanding the brain requires sophisticated theories to make sense of the collective actions of billions of neurons and trillions of synapses. Word theories are not enough; we need mathematical theories. The goal of this course is to provide an introduction to the mathematical theories of learning and computation by neural systems. These theories use concepts from dynamical systems (attractors, chaos) and concepts from statistics (information, uncertainty, inference) to relate the dynamics and function of neural networks. We will apply these theories to sensory computation, learning and memory, and motor control. Our learning objectives are for you to formalize and mathematically answer questions about neural computations including “what does a network compute?”, “how does it compute?”, and “why does it compute that way?”

3 credits. Prerequisites: linear algebra, probability and statistics, calculus