# Training a new generation of computational neuroscientists bridging neurobiology

> **NIH NIH R90** · NEW YORK UNIVERSITY · 2020 · $100,690

## Abstract

The Training Program in Computational Neuroscience (TPCN) will support integrated undergraduate and
graduate training in computational neuroscience at New York University. The program will be hosted by the
Center for Neural Science (CNS), with participation of faculty in the Departments of Psychology, Mathematics,
and Computer Science, and the Institute of Neuroscience at the School of Medicine. The TPCN will fit well with
NYU’s unique strengths and recent developments: (1) NYU is one of a few universities with a critical mass of
computational neuroscientists. NYU has had a Sloan-Swartz Center for Theoretical Neuroscience since 1994.
In the past three years alone, NYU has hired three computational neuroscientists. (2) CNS established an
undergraduate major in neuroscience as early as 1992, and thus has a long track record in undergraduate
education, it now has 136 students in the current academic year. (3) Recent faculty hiring in CNS, Psychology,
and the School of Medicine has greatly expanded our teaching and research capabilities in the neuroscience of
cognitive functions and their impairments associated with mental disorders. (3) As NYU is undertaking a merge
of two historically separated neuroscience graduate programs (at CNS and the School of Medicine), this
training grant will ensure that computational modeling, which has become indispensible in neuroscience, will
be front-and-center in the integrated graduate program. (4) NYU is a major center of Artificial Intelligence and
Data Science, with close links to Facebook’s AI Center and the Simons Center for Data Analysis. Our training
faculty together with these connections will give our students ample opportunities to acquire machine learning
techniques for data analysis and learn about brain-like AI algorithms.
The proposed training program will support coherent undergraduate and graduate training in computational
neuroscience at NYU. It will have several unique features: (1) Innovative mentorship methods: For example,
(a) graduate trainees will mentor undergraduate trainees, (b) faculty will explicitly discuss human factors in
academic practice; (c) there will be post-mortems after seminars by outside speakers. (2) Computational
psychiatry: We propose new courses and research opportunities that are designed specifically to link cognitive
function and the neurobiology of neural circuits. We propose innovative education in the nascent field of
Computational Psychiatry, to bring theory and circuit modeling to clinical research in mental health. (3) Broad
preparation: We aim to prepare trainees for jobs not only in academia, but also in medical and industry
research. To achieve this, we will utilize our strength in machine learning and data science to broaden
computational neuroscience training. The Program Directors have complementary strengths and will have
complementary roles in the program. Wang will supervise graduate trainees and focus on training in
mechanistic/circuit-level side of com...

## Key facts

- **NIH application ID:** 10002209
- **Project number:** 5R90DA043849-05
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Wei Ji Ma
- **Activity code:** R90 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $100,690
- **Award type:** 5
- **Project period:** 2016-09-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10002209

## Citation

> US National Institutes of Health, RePORTER application 10002209, Training a new generation of computational neuroscientists bridging neurobiology (5R90DA043849-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10002209. Licensed CC0.

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