Training a new generation of computational neuroscientists bridging neurobiology

培养连接神经生物学的新一代计算神经科学家

• 批准号: 9767751
• 负责人: Wei Ji Ma
• 金额: $ 20.5万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9767751
• 关键词: Academia; Algorithms; Artificial Intelligence; Behavioral; Brain; Clinical Research; Computer Simulation; Data Analyses; Data Science; Development; Education; Educational process of instructing; Ensure; Facebook; Faculty; Generations; Grant; Human; Impairment; Industry; Institutes; Learning; Link; Machine Learning; Medical; Mental Health; Mental disorders; Mentors; Mentorship; Methods; Modeling; Neurobiology; Neurosciences; New York; Occupations; Preparation; Psychiatry; Psychology; Research; Role; Science; Side; Students; Supervision; Teacher Professional Development; Techniques; Training; Training Programs; Universities; cognitive function; cognitive neuroscience; computational neuroscience; computer science; design; innovation; mathematical sciences; medical schools; neural circuit; programs; relating to nervous system; theories; undergraduate education; undergraduate student

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.

计算神经科学培训计划（TPCN）将支持综合本科生和 纽约大学计算神经科学研究生培训。该节目将由 神经科学中心（CNS），心理学系、数学系、 和计算机科学，以及医学院神经科学研究所。 TPCN 非常适合 纽约大学的独特优势和最新发展：（1）纽约大学是少数拥有足够数量的大学之一 计算神经科学家。纽约大学自 1994 年起就设立了斯隆-斯沃茨理论神经科学中心。 仅在过去三年中，纽约大学就聘请了三名计算神经科学家。 (2) CNS设立了 早在1992年就本科学习神经科学专业，因此在本科生方面有着悠久的记录 教育方面，本学年现有学生136人。 (3) 最近中枢神经系统、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 两个历史上独立的神经科学研究生项目（中枢神经系统和医学院）的 培训补助金将确保神经科学中不可或缺的计算模型能够 成为综合研究生项目的前沿和中心。 (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着密切的联系。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 数据分析技术并了解类脑人工智能算法。 拟议的培训计划将支持计算方面连贯的本科生和研究生培训 纽约大学神经科学。它将具有几个独特的特点：（1）创新的指导方法：例如， (a) 研究生学员将指导本科生学员，(b) 教师将明确讨论人为因素 学术实践； (c) 研讨会结束后将由外部发言人进行事后分析。 (2)计算 精神病学：我们提出专门为将认知联系起来而设计的新课程和研究机会 神经回路的功能和神经生物学。我们建议在新兴领域进行创新教育 计算精神病学，将理论和电路模型引入心理健康的临床研究。 (3) 广义 准备：我们的目标是让学员为学术界、医疗界和工业界的工作做好准备 研究。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来拓宽 计算神经科学培训。项目主任具有互补的优势，并且将有 计划中的互补角色。王将督导研究生学员，重点培养 计算神经科学以及计算精神病学的机械/电路级方面。马会 监督本科生学员并重点关注计算/行为方面。