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 computational neuroscience as well as computational psychiatry. Ma will supervise undergraduate trainees and focus on the computational/behavioral side.
This grant will support training of a new generation of graduate and undergraduate students in computational neuroscience, which has become increasingly important to meet the challenges of making discoveries with new data analysis tools and of understanding highly nonlinear complex neural circuits. A salient component of our program is training in the nascent field of Computational Psychiatry, bridging basic neuroscience and clinical research on mental disorders. Therefore, the proposed program has the potential of making a significant impact on mental health.
| Okazawa, Gouki; Sha, Long; Purcell, Braden A et al. (2018) Psychophysical reverse correlation reflects both sensory and decision-making processes. Nat Commun 9:3479 |
| Witthoft, Nathan; Sha, Long; Winawer, Jonathan et al. (2018) Sensory and decision-making processes underlying perceptual adaptation. J Vis 18:10 |
| Li, Hsin-Hung; Rankin, James; Rinzel, John et al. (2017) Attention model of binocular rivalry. Proc Natl Acad Sci U S A 114:E6192-E6201 |
