The Neural Engineering Laboratory is engaged in collaborative explorations of the functioning of neurons and networks, using both invertebrate and vertebrate model systems.

Academic Elective Track - Computational Neuroscience

Undergraduate Neural Engineering Certificates (Profs. Nair and D. Ho)

Graduate Neural Engineering Certificate (Profs. S. Nair and D. Ho)

Undergraduate Courses

ECE/CS/BE/BioSci/Psych 2017 World of Neuroscience (1 credit; Profs. Nair and Will)

This cross-listed course will introduce undergraduates to the growing area of neuroscience from the perspectives of three disciplines: engineering, biology and psychology. Topics in the course will span multiple levels of neuroscience including genomic, genetic, molecular, cellular, systems, behavioral and clinical levels. Due to the interdisciplinary nature of the neuroscience, the classes will cover diverse topics. The topics will range from overviews of the key neurobiology topics such as developmental, sensory and genetic systems, neural engineering topics such as computational modeling and analyzing your own brain signals (EEG), and psychology topics including imaging in reward, addiction and related research areas. The overall goal is to provide a broad exposure to the fascinating world of interdisciplinary neuroscience.

ECE/CS/BE/BioSci 4590 Computational Neuroscience (4 credits)

Computational Neuroscience is an emerging interdisciplinary field that links the diverse fields of biological sciences and quantitative sciences. ECE/BioSci 4580 is an interdisciplinary course that is team-taught by faculty from biology and engineering to introduce undergraduates to this exciting and growing field. Flyer: ECE/CS/BE/BioSci 4590 Computational Neuroscience.

Additional Information: NAE Grand Challenge in Engineering – Reverse Engineer the Brain; What is computational neuroscience? + Course requirements for an interdisciplinary minor in computational neuroscience + Coming soon – Undergraduate Certificates in Neural Engineering.

ECE/CS/BE 4540 Neural Models and Machine Learning (3 credits)

This projects-based course has three components: (i) modeling neurons and networks as nonlinear electrical circuits, (ii) machine learning in neuroscience, and (iii) software automation and cyberinfrastructure to support neuroscience. Extensive modeling projects focusing on all three components. Prerequisites: Calculus and linear systems, introductory software programming, and introductory cell biology or consent of instructor.

Graduate Courses

  • ECE 8320 Nonlinear Systems with Machine Learning Tools (Instructor: Nair)

  • ECE-CS 8570 Neural Dynamics and Communication (Instructor: Nair)

  • ECE-CS 8580 Machine Learning in Neuroscience (Instructor: Nair)

  • BioSci 8440 Integrative Neuroscience I (offered by Interdisciplinary Neuroscience Program)

  • BioSci 8442 Integrative Neuroscience II (offered by Interdisciplinary Neuroscience Program)

Academic Elective Track - Control Systems

Undergraduate Courses

  • ECE/BE 4310 Feedback Control of Systems

Graduate Courses:

  • ECE 7380 Introduction to Digital Signal Processing

  • ECE 8320 Nonlinear Systems

  • MAE 7720 Modern Control

  • MAE 8740 Robust Control

  • MAE 8750 Nonlinear Control

  • MAE/ECE 8780 State Variable Methods in Automatic Control

Professional Development Courses

Annual Summer Neuroscience Workshop: Experiments and Models to Teach Undergraduate Neuroscience

(Impact: 68 biology and psychology faculty from 2- and 4-year colleges)

ECE 8110/8120 Preparing Advanced Professionals I/II (1 credit hour each)

(Impact: 271 Engineering MS & PhD students)

This two-semester seminar course sequence (1 credit hour each; do not depend on each other, i.e., can be taken independently) covers several topics relevant to advanced graduate students, such as findings from cognitive science on ‘how people learn’, professional skills, personal skills, proposal writing, and the science of teaching and learning. Includes seminars by experts.

  • ECE 8110. The topics in Part I include the following:

    • Pedagogy – latest from cognitive science and learning theory, based on material from ‘How People Learn’ (The National Academy Press, 1999 – first book reading);

    • The importance of professional skills (2nd book reading – ‘The 7 Habits of Highly Effective People’ by Stephen Covey).

    • The book readings are interspersed with seminars by outside speakers on topics such as how to be an effective teacher, how does a university function, how do leading industries perform research; life in academic vs. industry, etc.

    • The requirements for the courses are attendance, reading assigned materials, participation in class discussions, and submission of materials developed for student presentations.

  • ECE 8120. The topics in Part II include the following:

    • The second course in the two-semester sequence (both independent of each other) continues the format in Part I with a combination of group discussions using book readings and seminars by experts.

    • A major focus in Part II of the course is on ‘How to write an effective proposal’, spanning about 4 weeks.

    • As part of that segment, students review model proposals, model reviews, and the segment culminates with a ‘hands-on’ proposal writing session.

    • This is followed by a focus on the findings from psychology/cognitive science on ‘learning’ using the book ‘Make it Stick – the Science of Successful Learning” by Brown, Roediger III, and McDaniel.

    • The requirements for the courses are attendance, reading assigned materials, participation in class discussions, and submission of materials developed for student presentations.

Additional resources: https://cyneuro.org