# INTERDISCIPLINARY TRAINING IN COMPUTATIONAL NEUROSCIENCE

> **NIH NIH T90** · CARNEGIE-MELLON UNIVERSITY · 2020 · $138,566

## Abstract

Project Summary
To understand the many disorders of the brain it is necessary to grapple with its complexity. Increasingly large
and complicated data sets are being collected, but the tools for analyzing and modeling the data are not yet
available. More researchers trained in computational neuroscience are desperately needed. This project
supports graduate and undergraduate training programs in computational neuroscience (TPCN) at both
Carnegie Mellon University (CMU) and the University of Pittsburgh (Pitt), and a summer school in
computational neuroscience for undergraduates, which are available to students coming from colleges and
universities throughout the United States.
The CMU-Pitt TPCN has 16 training faculty in computational neuroscience, 22 training faculty whose
laboratories are primarily experimental, and 20 training faculty whose laboratories are both computational and
experimental. At the graduate level the TPCN offers a PhD program in Neural Computation (PNC) and joint
PhD programs with CMU’s Department of Statistics (PNC-Stat) and its Machine Learning Department (PNC-
MLD), all set within a highly collegial, cross-disciplinary environment of our Center for the Neural Basis of
Cognition (CNBC), which is operated jointly by CMU and Pitt. The CNBC was established in 1994 to foster
interdisciplinary research on the neural mechanisms of brain function, and now comprises 145 faculty having
appointments in 22 departments. At the undergraduate level a substantial pool of local students is
supplemented during the summer by a cohort of students from across the country. During this renewal funding
period the project is strengthening the role of statistics and machine learning throughout the training programs;
(2) revising the summer undergraduate program by creating a didactic two-week “boot camp” at the beginning,
which includes a 20-lecture overview of computational neuroscience; (3) creating online materials, in
conjunction with the boot camp, that will serve not only our own students but also the greater world of training
in computational neuroscience; and (4) enhancing our minority recruitment by (a) taking advantage of the boot
camp and online materials, as well as making promotional visits to targeted campuses, and (b) creating and
running a one-year “bridge” program to better prepare under-represented minorities for PhD programs.
TPCN trainees work in vertically integrated, cross-disciplinary research teams. Graduate students take a year-
long course in computational neuroscience that bridges modeling and modern statistical machine learning
approaches to neuroscience. To ensure their competency in core neuroscience principles they also take
courses in cognitive neuroscience, neurophysiology, and systems neuroscience. They then pursue depth in a
relevant quantitative discipline, such as computer science, engineering, mathematics, or statistics. Graduate
students have extended experience in at least one experimental laboratory, and they...

## Key facts

- **NIH application ID:** 10002197
- **Project number:** 5T90DA022762-15
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** ROBERT E KASS
- **Activity code:** T90 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $138,566
- **Award type:** 5
- **Project period:** 2006-09-30 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002197

## Citation

> US National Institutes of Health, RePORTER application 10002197, INTERDISCIPLINARY TRAINING IN COMPUTATIONAL NEUROSCIENCE (5T90DA022762-15). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10002197. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*