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CMSC 727

Neural Modeling

3 Credits

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.

Prerequisites & course information

Additional Information: Students must have prior coursework similar to the content offered in undergraduate course CMSC421 .

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