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:

  1. Email 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.
  2. Have your research advisor send an email to, cc’ed to you, indicating their awareness and support of your application.