This course will expose students to computational models of cognitive processes and compare these models to recent findings in neuroscience. The course will incorporate projects such as implementations and evaluations of simple neural networks (e.g. models of memory and perceptual learning), reinforcement learning models (e.g. models of learning), and Bayesian models (e.g. optimal cognitive processes). We will read and discuss primary and secondary sources to understand how well these models fit the empirical results and whether the models offer plausible neural explanations at different scales. We will also read and discuss review articles that look at larger-scale interactions among brain regions as a means of explaining cognitive processes. Prerequisite: PSY 100, CSC 121, PSY 300 or 301.
|Prerequisite: PSY 100, CSC 121, PSY 300 or 301.||1 course|