Education

Graduate Certificate in Computational Neuroscience

 

Rationale and Mission

The Graduate Certificate in Computational Neuroscience is designed to meet the increasing demand for quantitative training in computational neuroscience. The goal of the GCCN is to train a new generation of scientists who will make quantitative links between properties of the brain and properties of the mind. GCCN trainees will learn to use computational methodology to interpret experimental results, build predictive models of brain function that inform the design of the experiments, and support an ever-growing list of applications from brain-machine interfaces in the clinic to cutting-edge forms of artificial intelligence.

PhD students in Neuroscience, Bioengineering, Psychology and Physics are currently eligible to apply.

The program aims to cultivate expertise in computational methods that range from theoretical models of brain activity to applications to data analysis of neuronal, genetic and behavioral data in the study of brain function and dysfunction. By integrating students and faculty from Neuroscience, Bioengineering, Psychology, and Physics graduate groups, the certificate will foster a community of scholars equipped to interpret experimental results, develop predictive models, and advance applications in clinical settings and artificial intelligence. This program will fill a gap in current offerings by formalizing computational neuroscience training across departments and supporting collaboration and innovation in research and education.

The certificate has been designed for doctoral students in the Perelman School of Medicine, School of Arts and Sciences, and School of Engineering and Applied Science. The certificate program will leverage the complementary experiences of students from different schools and engage them in mutual exchange of scientific approaches and ideas. It is designed to facilitate training across different educational backgrounds of the students in different programs.

 

Requirements

Credit Units & Courses:

 One required course

• Required course: PHYS 585: Theoretical and Computational Neuroscience

Two or three electives chosen (with the help of the Advisory Committee) to provide appropriate foundations for each trainee, selected from the many courses offered at Penn. Possible courses include:

• BE 5210: Brain-Computer Interfaces
• BE 5660: Networked Neuroscience
• CIS 5200 Machine Learning
• ESE 3050 Foundations of Data Science
• ESE 4010 Complex Networks
• ESE 4210 Control For Autonomous Robots
• ESE 4380 Machine Learning for Time-Series Data
• ESE 5030 Simulation Modeling and Analysis
• ESE 5050 Feedback Control Design and Analysis
• ESE 5120 Dynamical Systems for Engineering and Biological Applications
• ESE 5140 Graph Neural Networks
• ESE 5380 Machine Learning for Time-Series Data
• ESE 5450 Data Mining: Learning from Massive Datasets
• ESE 5460 Principles of Deep Learning
• ESE 6170 Non-Linear Control Theory
• ESE 6180 Learning for Dynamics and Control
• ESE 6740 Information Theory
• MATH 3120 Linear Algebra
• MATH 3130 Computational Linear Algebra
• MATH 3200 Computer Methods in Mathematical Science I
• MATH 4200 Ordinary Differential Equations
• MATH 4250 Partial Differential Equations
• MATH 5130 Computational Linear Algebra
• MATH 6440 Partial Differential Equations
• NGG 5730 Systems Neuroscience
• NGG 5910 Digital Signal Processing
• PHYS 5566 Machine Learning Methods in Natural Science Modeling
• PHYS 5570 Physical networks: living matter to data science
• PHYS 5580 Biological Physics
• PSYC 4281 Computational Neuroscience Lab
• PSYC 5110 Probabilistic Models of Perception
• PSYC 5490 A Neuroscience Perspective of Artificial Intelligence
• PSYC 5730 Seminar in Neuroeconomics

Research and Lab Rotations:

• Completion of required lab rotations as mandated by the home graduate group, with at least one rotation in a research group affiliated with the Computational Neuroscience Initiative.
• Participation in an original research project or dissertation within a CNI-affiliated lab or project.

Programmatic Activities:

• Attendance at biweekly CNI seminars and participation in a bi-weekly, trainee-led journal club called CNI +/-.
• Involvement in CNI-sponsored workshops (e.g., guest lectures, Neuromatch Academy, or related hands-on hackathons).

Capstone Experience:

• Completion of a scholarly product such as a research paper, conference presentation, manuscript submission, or contribution to an annual Penn Brain & Mind Symposium.

 

Application Process

Applicants must:

1. Provide a brief essay (~½ page) outlining interest in computational neuroscience and anticipated career impact.
2. Submit written permission from their graduate group chair.
3. If outside the four specified programs, describe in detail their funding sources and relevant academic requirements.
4. Applications are typically submitted before the first doctoral year, but students at later stages may be considered.

 Please email the relevant application materials to Jessica Marcus (jmarcus@upenn.edu) by May 30, 2026.

 

Advising

Each student will be assigned a faculty mentor from the advisory board. Mentoring includes an initial planning meeting, periodic review of service and elective activities, and guidance on the capstone experience.

 

Administrative Oversight

The certificate will be overseen by an Academic Advisory Board composed of standing faculty across the participating graduate groups:

Maria Geffen (Psychology/Neuroscience/Bioengineering)

Joshua Gold (Neuroscience/Psychology)

Vijay Balasubramanian (Neuroscience/Physics)

The advisory board will meet annually with each trainee to establish training plans, monitor progress, and provide mentorship, while coordinating closely with graduate group chairs and academic supervisors to ensure that certificate activities complement students' primary training.

Program Faculty (members of Computational Neuroscience Initiative, committed to training students in the program)