Training a new generation of computational neuroscientists bridging neurobiology

在计算神经科学（TPCN）的培训计划将支持综合本科和 在纽约大学接受计算神经科学的研究生培训。该计划将由 神经科学中心（CNS），参与教师在心理学，数学， 和计算机科学，以及医学院的神经科学研究所。TPCN将与 纽约大学的独特优势和最近的发展：（1）纽约大学是为数不多的大学， 计算机神经科学家自1994年以来，纽约大学一直有斯隆-斯沃茨理论神经科学中心。 仅在过去的三年里，纽约大学就聘请了三位计算神经科学家。(2)CNS建立了一个 早在1992年，我就在神经科学本科专业学习，因此在本科生中有很长的记录。 教育，它现在有136名学生在本学年。(3)最近在CNS，心理学， 医学院极大地扩展了我们在神经科学方面的教学和研究能力， 认知功能及其与精神障碍相关的损害。(3)由于纽约大学正在进行合并 两个历史上分开的神经科学研究生课程（在CNS和医学院），这 培训补助金将确保在神经科学中已成为不可或缺的计算建模， 成为综合研究生课程的前沿和中心。(4)纽约大学是人工智能的主要中心， 数据科学，与Facebook的人工智能中心和西蒙斯数据分析中心有密切联系。我们的培训 教师与这些联系将为我们的学生提供充足的机会来获得机器学习 数据分析技术和学习类脑AI算法。 拟议的培训计划将支持连贯的本科生和研究生培训计算 神经科学博士它将有几个独特的特点：（1）创新的指导方法：例如， (a)研究生学员将指导本科学员，（B）教师将明确讨论人的因素， 学术实践;（c）在由外界人士主讲的研讨会后，会进行事后检讨。(2)计算 精神病学：我们提出新的课程和研究机会，专门设计，以联系认知 功能和神经回路的神经生物学。我们建议在新兴领域的创新教育， 计算精神病学，将理论和电路建模应用于心理健康的临床研究。(3)广泛 准备：我们的目标是准备学员的工作不仅在学术界，而且在医疗和工业 research.为了实现这一目标，我们将利用我们在机器学习和数据科学方面的优势， 计算机神经科学训练项目主管具有互补优势， 在节目中扮演互补角色。王将监督研究生学员，并专注于 计算神经科学和计算精神病学的机械/电路层面。马将 指导本科学员，并专注于计算/行为方面。

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.