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: 9316750
• 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/9316750
• 关键词: Academia; Algorithms; Artificial Intelligence; Behavioral; Brain; Clinical Research; Computer Simulation; Data Analyses; Data Science; Development; Education; Educational process of instructing; Ensure; Faculty; Generations; Grant; Human; Impairment; Industry; Institutes; Learning; Link; Machine Learning; Mathematics; Medical; Mental Health; Mental disorders; Mentors; Mentorship; Methods; Modeling; Neurobiology; Neurosciences; New York; Occupations; Preparation; Psychiatry; Psychology; Research; Role; Science; Side; Students; Teacher Professional Development; Techniques; Training; Training Programs; Universities; cognitive function; computational neuroscience; computer science; design; innovation; medical schools; neural circuit; programs; relating to nervous system; theories; undergraduate education

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.

在计算神经科学（TPCN）的培训计划将支持集成 在纽约大学接受计算神经科学的本科生和研究生培训。节目将由 由神经科学中心（CNS），与教师的参与部门， 心理学，数学和计算机科学，以及神经科学研究所在学校 药TPCN将非常适合纽约大学的独特优势和最近的发展：（1）纽约大学是 是为数不多的拥有大量计算神经科学家的大学之一。纽约大学有一个 斯隆-斯沃茨理论神经科学中心（Sloan-Swartz Center for Theoretical Neuroscience），1994年至今。仅在过去的三年里，纽约大学 雇了三个计算神经科学家(2)CNS设立了一个本科专业， 神经科学早在1992年，因此在本科教育中有很长的记录，它现在 本学年有136名学生。(3)最近在CNS，心理学和 医学院大大扩展了我们的教学和研究能力， 认知功能及其与精神障碍相关的损害的神经科学。(3)作为 纽约大学正在进行两个历史上分离的神经科学研究生课程的合并（在CNS 和医学院），这项培训补助金将确保计算建模， 它已经成为神经科学中不可或缺的一部分，将成为综合研究生院的前沿和中心。 程序. (4)纽约大学是人工智能和数据科学的主要中心，与 Facebook的人工智能中心和西蒙斯数据分析中心。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技术 进行数据分析并学习类脑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， 计算神经科学它将有几个独特的特点：（1）创新的导师制 方法：例如， (a)研究生学员将指导本科学员，（B）教师将明确讨论人类 （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：我们提出专门设计的新课程和研究机会 特别是将认知功能和神经回路的神经生物学联系起来。我们提出 在计算精神病学的新生领域的创新教育，使理论和电路 心理健康的临床研究建模。(3)广泛的准备：我们的目标是培养学员 不仅在学术界，而且在医学和工业研究中。因此我们 我们将利用我们在机器学习和数据科学方面的优势， 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。 Ma将监督本科学员，并专注于计算/行为方面。