Training in Theoretical and Computational Neuroscience

理论和计算神经科学培训

• 批准号: 7886513
• 负责人: PETER SAGGAU
• 金额: $ 12.9万
• 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/7886513
• 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)： 这一跨学科和多机构的培训计划旨在支持未来的研究人员的教育，他们可以将数学、物理和工程的工具应用于脑研究的问题。传统神经科学使用还原论来表述假设并通过实验进行测试，而理论神经科学和计算神经科学则建立在信息论、动力系统理论和计算机科学的基础上，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少见，他们分别接受了实验和计算方法的培训。作为延伸，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后和博士后学生。随着我们对神经功能的了解不断升级，分析健康和疾病大脑功能的实验的复杂性也在不断增加。在这种情况下，不仅在实验之间，而且在实验期间，有必要利用理论神经科学和计算神经科学的分析和预测性质。为了迎接这一挑战，我们将招收之前接受过数学、物理和工程学培训的博士后研究生，并将他们与理论和实验神经科学专业知识的双重导师联系在一起。除了使用理论工具，这些学生还将接受最先进的实验方法的培训，特别是那些复杂的多维数据采集、处理和可视化方法，因为这些方法在高级成像技术中最突出。我们设计了一套课程来最好地教育这些跨学科的学生。对于博士前学生来说，培训将是课堂教学和动手实验的良好平衡的结合。对于博士后和博士后，将有活跃的期刊俱乐部，导师指导的研究和在共同导师的实验室实习的研究，以及会议报告。我们的23名培训人员来自休斯敦德克萨斯医疗中心及其周围的六家机构。所有教员也是墨西哥湾沿岸理论和计算神经科学联盟的成员，该联盟是墨西哥湾沿岸跨学科生物科学研究和培训联盟的一部分。我们的代表性学科和现有设施都使这里成为拟议培训计划的理想地点。