Cross Disciplinary Neural Engineering Training Program

Supported by a T32 grant from the National Institute of Neurological Disorders and Stroke, an institute within the U.S. National Institutes of Health

Understanding how the brain works and treating brain disorders are challenging tasks that need future leaders in neuroscience research. At the Penn State Center for Neural Engineering, we invite bright and ambitious graduate students to join the Cross Disciplinary Neural Engineering (CDNE) training program funded by the National Institutes of Health (NIH). Second-year graduate students associated with the center should apply to the program to participate in their third and fourth years. Through the program, graduate students will learn to work across the disciplinary boundaries of engineering, sciences, mathematics, and human brain health, with the ability to communicate and understand deeply the needs of their collaborators. Ultimately, students who participate in the program will have the opportunity to produce lasting advances in both basic neurosciences and human brain health.

Trainees can acquire:

• Excellent cross-disciplinary scientific literacy and ability to communicate across boundaries.
• Ability to lead and work within scientifically diverse groups.
• Proficiency in advanced data analytic and statistical techniques, as well as experimental design, that span engineering and device development, theoretical modeling, as well as neurophysiology and human brain health applications.
• Competence in identifying and addressing ethical concerns that might emerge in their research.

Current graduate programs contributing to the CDNE training program include:

• Anthropology
• Biomedical Engineering
• Electrical Engineering
• Engineering Science and Mechanics
• Mathematics
• Mechanical Engineering
• Physics
• Neuroscience

Additional programs may be added under the advisement of the CDNE faculty (and their service on graduate programs) to the program administration.

Prospective engineering graduate applicants should state that they are interested in following the Cross Disciplinary Neural Engineering Training Program.  Once matriculated, students should enroll anytime, and ideally within their first two years of graduate school.

Training Curriculum

The CDNE program elements encompass students’ full graduate career.  Training elements include a three core courses to provide common grounding in neuroscience, neural engineering, and quantitative / statistical data analysis; cross-disciplinary research with collaboration between at least two mentors’ research groups; regular participation and leadership in journal clubs;  self-paced completion of cross-disciplinary training modules; participation in the CDNE all-group meeting; and guided submission of external fellowship applications.

Required Courses

The required courses serve the objectives of preparing the trainees to work at the boundary between engineering, mathematics and neuroscience.  Each is three credits, can serve as either required or technical electives in the students’ home graduate programs.

Two of the required courses are foundational courses that present two different views of topics in neuroscience; they provide a core set of disciplinary language for students working at the boundary between neuroscience and engineering.

• ESC 525: Neural Engineering: Fundamentals of Interfacing with Brain (3 Credits)
  The course explores theoretically and quantitatively the biophysical basis of neural function, measurable signals, and neural stimulation, and the electrochemical nature of the electrode tissue interface.
• NEURO 520: Cellular and Molecular Neuroscience (3 Credits)
  An introduction to neurons, glia, and the molecular basis of brain function.

To prepare the students to work in a quantitative and statistically sound approaches, they must complete at least one course on modeling of data. Acceptable courses include:

• ESC 555: Neuroscience Data Analysis (3 Credits)
  This course covers the biophysical origin and measurement of brain signals, the theoretical background of modern analysis methods and their practical implementation to brain signals, and introduces a toolbox of mathematical and computational techniques to analyze electrophysiological, optical and anatomical data. Topics covered include spectral methods, neural encoding and decoding, information theory and image analysis.
• A 500 level Stats Course: A 500 level Stats course (including Stats 500-502; 513-4; 557). Of particular interest are:
  • STAT 500: Applied Statistics (3 Credits). Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and 2-way ANOVA, Chi-square tests, diagnostics.
  • STAT 501: Regression Methods (3 Credits). Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.
  • STAT 502: Analysis of Variance and Design of Experiments (3 Credits).  Analysis of variance and design concepts; factorial, nested, and unbalanced data; ANCOVA; blocked, Latin square, split-plot, repeated measures designs.
  • STAT 513/514: Theory of Statistics I (3 Credits) / II (3 Credits).  (I) Probability models, random variables, expectation, generating functions, distribution theory, limit theorems, parametric families, exponential families, sampling distributions. (II) Sufficiency, completeness, likelihood, estimation, testing, decision theory, Bayesian inference, sequential procedures, multivariate distributions and inference, nonparametric inference.
  • STAT 557: Data Mining (3 Credits): This course introduces data mining and machine learning methods, major software packages, and their applications. Topics covered in this course include linear classification/regression, logistic regression, regularization and dimension reduction, decision trees, and mixture models.
• Alternate courses may be accepted for statistics through consultation and petition with the program directors and program statistician.

CNDE Trainees are expected to completed both ESC 525, Neuro 526, and a stats course by the end of year 2 of their departmental programs.

Course/Activity	Topic	Years
Foundational Courses (both required)	ESC 525: Fundamentals of Interfacing with Brain	1, 2
	NEURO 520: Cellular and Molecular Neurosciences
Course in Statistics or Neural Data Analysis (one required)	ESC 555: Analysis of Neural Data	1, 2
	A 500 level Stats course (including Stats 500-502; 512-3; 515): Alternate statistics/data analysis focused courses may be accepted under petition to program directors/statistician
CDNE Required Activities	All Group Meeting: Weekly meeting of trainees, trainers, and statistician. Once per month is a programmatic lecture. Other weeks involve presentations of ongoing research, discussions of experimental design and statistical design, and special topics relating to rigor, professional development, etc.	2-5
	CNE Seminar Weekly: Weekly seminar with local and invited faculty presentations.	All
	Journal Clubs (JC): Cross-group meetings to discuss and provide critical cross-disciplinary interpretation of current scientific literature. JCs are organized around themes such as NeuroTechnology, Computational Neuroscience, Neurodegeneration, etc.	All
	CNE Retreat: Faculty lectures; student presentations (oral and/or poster).	All
Professional Development (both required)	Grant Writing: Advanced training and submission of a grant proposal. Informal course.	2
	Responsible Conduct of Research (RCR): Penn State offers a Scholarship and Research Integrity Program (SARI) on such topics as publication practices, conflicts of interest, mentoring, research misconduct.	1, 5

Trainee Application Process

Interested students should review the CDNE Training Program Guide for Students.  

To apply, Penn State graduate students must complete the online application.

Those in year two of their graduate studies can be nominated for fellowship support. The deadline for funding is March 31 annually.  

Contact Us

Email us with any questions regarding the CDNE training program.