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Courses - Spring 2017
Computer Science Department Site
Neural Modeling
Credits: 3
Grad Meth: Reg, Aud
Prerequisite: CMSC421; or students who have taken courses with comparable content may contact the department; or permission of instructor.
Fundamental methods of neural modeling. Surveys historical development and recent research results from both the computational and dynamical systems perspective. Logical neurons, perceptrons, linear adaptive networks, attractor neural networks, competitive activation methods, error back-propagation, self-organizing maps, and related topics. Applications in artificial intelligence, cognitive science, and neuroscience.