|Name:||Michael Frank||Alana Jaskir|
|Office:||Metcalf 335||Metcalf 315|
|Office Hours:||9-10am Tues or by apt||Labtime or by apt|
Text: O'Reilly, R. C., Munakata, Y., Frank, M. J., Hazy, T. E.,
and Contributors (2012). Computational Cognitive Neuroscience.
Wiki Book, 4th Edition (2020). The url of the updated book is here.
Goals: How does the brain secrete the mind? This course introduces you to the field of computational cognitive neuroscience, which considers how neural mechanisms inform the workings of the mind, and reciprocally, how cognitive and computational constraints afford a richer understanding of the problems these mechanisms evolved to solve. 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. We then discuss learning (plasticity) mechanisms that allow networks of neurons to be adaptive and which are required to perform any reasonably complex task. We consider how different brain systems (visual cortex, hippocampus, parietal cortex, frontal cortex, basal ganglia) interact to solve difficult computational tradeoffs. We examine a range of cognitive phenomena within this framework, including visual object recognition, attention, various forms of learning and memory, language and cognitive control. We will see how damage to different aspects of biological networks can lead to cognitive deficits akin to those observed in neurological conditions. The class includes a lab component in which students get hands on experience with graphical neural network software, allowing deeper, more intuitive appreciation for how these systems work.
UPDATED Full Syllabus: PDF
Calendar of due dates: Google Calendar Link
Download lecture slides: Overall course download site (Also see below)
Homework Projects: Here (will be updated during semester)
Basal ganglia and reinforcement projects available here (follow directions to simulate various effects of dopamine or other circuit manipulations on learning and choice)