Graduate Certificate in Neural Computation and Engineering
The Graduate Certificate Program in Neural Computation and Engineering provides interdisciplinary training for students engaged in quantitative, mathematical, engineering and computational approaches to problems in neuroscience. The Certificate Program allows enrolled students to receive formal recognition for their work, and facilitates connections within the neural computation and engineering community.
The Certificate program is a non-degree granting program; participation requires that a student be already admitted to the University of Washington, working in the biological, physical, computational, mathematical, chemical, engineering or quantitative social sciences.
Please join our mailing list for quarterly updates on course offerings.
Required Courses and Activities
The following are all required to receive the Certificate. Successful completion of the Graduate Certificate Program will require a minimum cumulative GPA of 3.0 for courses required for the Certificate and a cumulative GPA of 3.2 or higher.
1. At least two quarters of AMATH 500, a theoretical neuroscience journal club. In at least one quarter you will present a paper.
2. At least two of the following core courses (graded):
| Course | Title |
|---|---|
| NEURO 545 (W) | Quantitative Methods in Neuroscience |
| CSE/NEURO 528 | Computational Neuroscience |
| AMATH 534 (W, every other year) | Dynamics of Neurons and Networks |
| BIOE 560 (A) | Neuroengineering |
3. At least two of the following elective courses, totaling at least 7 additional graded credits:
| Course | Title | Credits |
|---|---|---|
| NEURO 502 | Sensory and motor systems | 5 |
| NEURO 503 | Cognitive and integrative neuroscience | 4 |
| NEURO 511a | Artiphysiology | 3 |
| EE 596b | Practical Introduction to Neural Networks | 4 |
| AMATH 582 | Computational methods for data analysis | 5 |
| AMATH 522 | Computational modeling of biological systems | 5 |
| CSE 546/STAT 535 | Machine learning | 4 |
| EE 505 | Probability and random processes | 4 |
| STAT 535 | Statistical learning | 3 |
| AMATH 533/ CSE 529 | Neural control of movement | 3 |
| AMATH/CSE 579 | Intelligent control through optimization and learning | 3 |
| EE 518 | Digital signal processing | 4 |
| EE 546 | Applied neural control | 3 |
| ENTRE 579 | Health Innovation Practicum | 2 |
| BIOE 561 | Neural Engineering Tech Studio | 4 |
4. Capstone project: As a capstone experience, all students will present a 10-15 minute talk, with additional time for questions, at an annual research symposium or equivalent event which will demonstrate mastery of a computational or mathematical approach applied to a problem in neuroscience. This work may align with the student’s core thesis work or may be a side project inspired by coursework, course projects or participation in external summer courses. Students will generally present their capstone presentation between the 3rd and 5th years of graduate school. All enrolled students will be expected to attend this yearly event.
Admission requirements and application instructions
Admission is open to students at any stage in their graduate education who will be able to satisfy the requirements by the time of graduation. You should be enrolled in a relevant degree program and have selected a mentor and project within the broad framework of the program. To apply, do the following:
- Email cncadmin@uw.edu with the following information: name, student number, graduate program, year started in program, expected date of graduation, and thesis mentor’s name. Please include a copy of your CV, your unofficial graduate transcript, and a short statement of your research interests.
- Have your research advisor send an email to cncadmin@uw.edu, cc’ed to you, indicating their awareness and support of your application.