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Subject offerings of new and developing areas of knowledge in electrical and computer engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This graduate course explores the interplay between dynamical systems theory and neural networks (artificial and biological). Students analyze how stability, attractors, bifurcations, and chaos govern behavior, learning, and computation. Covers foundational discrete/continuous-time dynamical systems, applied to analyzing machine learning models (including training dynamics) and modeling complex neural circuits (rate-based and spiking). Emphasis is on hands-on computational analysis and developing theoretical understanding. The course culminates in a research project applying these interdisciplinary concepts.