Computational Cognitive Neuroscience: CLPS1492

Mon/Wed 8:30 - 9:50, Fall 2018
Room: Vartan Gregorian Quad 116E
Lab: Metcalf 107, Mon/Thurs 4:30-6:30pm
ProfessorTeaching Assistant
Name: Michael Frank Dan Scott
Office: Metcalf 335 Metcalf 315
Phone: 863-6872
Email: anti-spam email 
addr 
img daniel_scott@brown.edu
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 (2016). Computational Cognitive Neuroscience. Wiki Book, 3rd Edition. 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 (no programming experience needed), allowing deeper, more intuitive appreciation for how these systems work.


Important Links

Professor: Michael Frank

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)

Download simulation software: Here
NOTE: Simulation exercises all require at least Emergent version 8.5

Learn to build your own networks, etc:
emergent Documentation

Emergent Tutorial (save file, open in emergent and follow directions to do yourself)

Tutorial for setting up data tables (inputs to network), and for analyzing output data (save file, open in emergent and follow directions to do yourself)

Screencast: watch how to build networks, create new programs, etc

Screencast: watch how to generate different kinds of input data, visualization etc

Youtube: Building a Standard Network

Youtube: Construct Your Own Network

Information about Programs in emergent

Tutorial including building programs


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



Lectures

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

Introduction
Units/Neurons
Extra slides on Neurons from Dan (optional, complementary to above)
Networks
Extra slides on Networks (Similarity Representations) from Dan (complementary to above)
Inhibition & Constraint Satisfaction
Extra slides on Inhibition from Dan (complementary to above)
Self-Organizing and "Hebbian") Learning
Task ("Error Driven") Learning
Combined Learning
Temporal Learning and Representation, Reinforcement Learning
Extra slides on Temporal Difference Reinforcement Learning (optional, complementary to above)
Large Scale Brain Organization / Computational Trade-offs
Perception and Attention
Basal Ganglia in reinforcement learning and action selection
Memory: Episodic, semantic, Working memory, etc
Basal Ganglia - Prefrontal Interactions in Working Memory
Executive Function
Language
Optional extra slides on the Binding Problem