Computational Cognitive Neuroscience: CLPS1492

Tue/Thur 10:30AM - 11:50, Fall 2022
Lab: on Zoom, Mon/Thurs 4-6pm

ProfessorTeaching Assistants
Name: Michael Frank Krishn Bera & Zhenyu Zhu
Office: Metcalf 335 Metcalf 315
Phone: 863-6872
Email: anti-spam email 
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.

Important Links

Professor: Michael Frank

UPDATED Full Syllabus: PDF

Canvas site: assignments, recorded lectures

Piazza: technical questions etc

Homework Projects: Here (will be updated during semester)

Simulation software:

Install Go/Python version here
Download just the simulation assignments here (doesn't require compilation)

Learn to build your own networks, etc:

Basal ganglia and reinforcement projects available here (follow directions to simulate various effects of dopamine or other circuit manipulations on learning and choice)

Lecture slides

Note: I reserve the right to update these up to the night before lecture.

Inhibition & Constraint Satisfaction
Self-Organizing and "Hebbian") Learning
Task ("Error Driven") Learning
Combined Learning
Temporal Learning and Representation, Reinforcement Learning
Extra Slides on Temporal Difference Reinforcement Learning (Alana Guest Lecture: pptx) (PDF; no animations)
Large Scale Brain Organization / Computational Trade-offs
Perception and Attention
Basal Ganglia in reinforcement learning and action selection
Basal Ganglia slides 2021
Memory: Episodic, semantic, Working memory, etc
Basal Ganglia - Prefrontal Interactions in Working Memory
Executive Function
Optional extra slides on the Binding Problem