Training in Theoretical and Computational Neuroscience

理论和计算神经科学培训

• 批准号: 7622154
• 负责人: PETER SAGGAU
• 金额: $ 13.39万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7622154
• 关键词: Training; computational neuroscience

DESCRIPTION (provided by applicant): This interdisciplinary and multi-institutional training program seeks to support the education of future researchers who can apply the tools of Mathematics, Physics and Engineering to problems of Brain Research. Traditional Neuroscience uses reductionism to formulate hypotheses and tests them experimentally, while Theroretical and Computational Neuroscience builds on information Theory, Dynamical Systems Theory, and Computer Science to create theoretical models to be tested numerically. Collaborations of Neuroscientists, individually trained in experimental and computational approaches, are not unusual on the basis of experimental data. In extension of this, we advocate a synergistic use of both approaches to control the experiment itself, and propose to train pre- and postdoctoral students accordingly. Commensurate with our escalating knowledge of neural function, the complexity of experiments to analyze both healthy and diseased brain function is ever-increasing. In this situation, it is necessary to utilize the analytic and predictive nature of Theroretical and Computational Neuroscience not only between but rather during experiments. To meet this challenge, we will recruit both pre- post-doctoral students with previous training in Mathmatics, Physics and Engineering, and associate them with dual mentors of expertise in both theoretical and experimental Neuroscience. In addition to using theoretical tools, these students will be trained in state-of-the-art experimental methods, specifically those for complex multidimensional data acquisition, processing and visualization, as these are most prominent in Advanced Imaging Techniques. We have devised a curriculum to best educate these interdisciplinary students. For predoctoral students, training will be a well-balanced combination of classroom instruction and hands-on labs. For both pre- and postdoctoral students, there will be active journal clubs, mentor-guided research with an internship in the lab of the co-mentor, and conference presentations. Our training faculty of 23 is drawn from six institutions in and around the Texas Medical Center in Houston. All faculty members are also members of the Gulf Coast Consortium for Theoretical and Computational Neuroscience, which is part of the Gulf Coast Consortia for Interdisciplinary Bioscience Research and Training. Both our spectrum of represented disciplines and existing facilities makes this an ideal site for the proposed training program.

描述（由申请人提供）： 这个跨学科和多机构的培训计划旨在支持未来研究人员的教育，他们可以将数学，物理和工程的工具应用于大脑研究的问题。传统神经科学使用还原论来制定假设并进行实验测试，而理论和计算神经科学则建立在信息理论，动力系统理论和计算机科学的基础上，以创建理论模型进行数值测试。在实验数据的基础上，神经科学家的合作并不罕见，他们分别接受了实验和计算方法的训练。在此基础上，我们主张协同使用这两种方法来控制实验本身，并建议相应地培养博士前和博士后学生。随着我们对神经功能的认识不断提高，分析健康和患病大脑功能的实验的复杂性也在不断增加。在这种情况下，有必要利用理论和计算神经科学的分析和预测性质，不仅在实验之间，而且在实验过程中。为了迎接这一挑战，我们将招募在数学，物理学和工程学方面接受过培训的博士后学生，并将他们与理论和实验神经科学方面的专业知识的双重导师联系起来。除了使用理论工具外，这些学生还将接受最先进的实验方法的培训，特别是那些用于复杂多维数据采集，处理和可视化的方法，因为这些方法在高级成像技术中最为突出。我们设计了一个课程，以最好地教育这些跨学科的学生。对于博士生，培训将是课堂教学和动手实验室的平衡组合。对于博士前和博士后学生，将有活跃的期刊俱乐部，导师指导的研究，在共同导师的实验室实习，以及会议演示。我们的23名培训教师来自休斯顿德克萨斯医疗中心及其周围的六个机构。所有教师也是墨西哥湾沿岸理论和计算神经科学联盟的成员，该联盟是跨学科生物科学研究和培训的墨西哥湾沿岸联盟的一部分。我们的代表学科和现有设施的频谱使这成为拟议的培训计划的理想场所。