PHIL544A

028723

This course introduces you to the field of computational cognitive neuroscience for understanding how the brain secretes the mind. We focus on simulations of cognitive and perceptual processes, using neural network models that bridge the gap between biology and behavior. We first consider the basic biological and computational properties of individual neurons and networks of neurons, followed by learning mechanisms that allow networks to be adaptive and to perform reasonably complex tasks. We examine a range of cognitive phenomena within this framework, including attention, memory, language and higher-level cognition. The class includes a lab component in which students get hands on experience with graphical neural network software (no programming experience needed), allowing deeper, more intuitive appreciation for how these systems work. Graduate-level requirements include graduate students to work by themselves for the final project, and generate a new model from scratch to answer their question. They will be responsible for more homework questions.

GRD - Regular Grades A, B, C, D, E

Graduate

CE - CL (Cross Listed), GIDP - COGS (Cognitive Science), IIA - NRSC (NRSC - Neuroscience Grad Prog)

PHIL444A