Course Schedule and Catalog
Course Schedule - Fall Semester 2016
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).Additional information available here.
ELEC 548 001 (CRN: 11403)
NEURAL SIGNAL PROCESSING
Long Title: MACHINE LEARNING AND SIGNAL PROCESSING FOR NEURO ENGINEERING
Department: Electrical & Computer Eng.
Instructor: Kemere, Caleb
Meeting: 9:25AM - 10:40AM TR (22-AUG-2016 - 2-DEC-2016)
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 35
Section Enrolled: 17
Total Cross-list Max Enrollment: 35
Total Cross-list Enrolled: 22
Enrollment data as of: 19-APR-2024 2:45PM
Final Exam: GR Course-Dept Schedules Exam
Description: The activity of a complex network of billions of interconnected neurons underlies our ability to sense, represent and store the details of experienced life, and enables us to interact with our environment and other organisms. Modern neuroscience techniques enable us to access this activity, and thus to begin to understand the processes whereby individual neurons enable complex behaviors. In order to increase this understanding and to design biomedical systems which might therapeutically interact with neural circuits, advanced statistical signal processing and machine learning approaches are required. This class will cover a range of techniques and their application to basic neuroscience and neural interfaces. Topics include latent variable models, point processes, Bayesian inference, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, decoding, characterization of neural systems, and field potential analysis. Cross-list: BIOE 548.