CMSC 727
Neural Modeling
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 .
Sources
- Course catalogue ↗2026-2027