CN550 - Neural and Computational Models of Recognition, Memory, and Attention
Prof. Gail A. Carpenter
TF: Ben Chandler
CN550 Spring 2009 
Course Description
CN550 develops neural network models of how internal representations of sensory events and cognitive hypotheses are learned and remembered, and of how such representations enable recognition and recall of these events. Various neural and statistical pattern recognition models, and their historical development and applications, are analyzed. Special attention is given to stable self-organization of pattern recognition and recall by Adaptive Resonance Theory (ART) models. Mathematical techniques and definitions to support fluent access to the neural network and pattern recognition literature are developed throughout the course. Experimental data and theoretical analyses from cognitive psychology, neuropsychology, and neurophysiology of normal and abnormal individuals are also discussed. Course work emphasizes skill development, including writing, mathematics, computational analysis, teamwork, and oral communication.
Datasets and contests
Data labeling by users
TechLab Consciousness readings
Editing the CN550 wiki
If you are a student registered for CN550, you can edit the wiki using the password that has been given to you.
