Training Program for Interactionist Cognitive Neuroscience (ICoN)

互动认知神经科学培训计划 (ICoN)

• 批准号: 10404975
• 负责人: David Badre
• 金额: $ 24.68万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10404975
• 关键词: Training Programs; cognitive neuroscience

Our training program for Interactionist Cognitive Neuroscience (ICoN) seeks to provide student-focused, interdisciplinary training in computational cognitive neuroscience that integrates data from multiple scales and levels of analysis. Transformative gains in understanding the human brain and mental health require integration across multiple levels of analysis. Recent historic advances in genetics and cellular biology are paving the way for understanding fundamentals of neural function. At the other end of the spectrum, methods for imaging and stimulating human brains non-invasively have led to revolutionary advances in discovering the macro-scale organization supporting perception, motivation, and cognition. Now, a major effort at the `systems' level between these two scales is beginning to uncover the activity, connectivity, and computations of neural circuits. The advent of this systems-level progress holds the promise of linking core circuit computations to emergent human behavior and leading to detailed, transdiagnostic models of mental illness. However, as we recently argued (Badre, Frank and Moore, 2015 Neuron), fulfilling this promise requires making direct links between circuit-level computation and the emergent function of the human system. We believe that integrating systems- and human neuroscience in this way demands a systematic approach built on two key strategies. First, formal computational models must be used to provide principled links between levels of analysis; and, second, complementary methods must be applied, and in the ideal case parallel human and non-human studies conducted in coordination. Achieving these aims requires a new generation of scientists that can take full advantage of multiple techniques and data sources, and who are deeply versed in computational theory. Traditional neuroscience training relies on an apprenticeship model that limits students to a single lab and level of inquiry. Thus, a specialized training program is required to specifically equip neuroscientists for this `Interactionist' approach. ICoN will provide this training emphasizing the two tenets: I. Computation is key to translating between levels. Students must be rigorously quantitatively trained in formal theory. A close corollary is that they must be fluent in the advanced analysis methods necessary for cross-level integration (e.g., machine learning). II. Next-generation scholars must have expertise at multiple levels. Students must be trained to use and integrate multiple methods and data sources. Further, they must have the skills (and courage) to pursue ideas to their next most logical step, to be question driven and not technique limited. Students will be trained to conduct integrative research projects across domains such as human cognitive neuroscience, systems neuroscience, and computational neuroscience.

我们的互动主义认知神经科学培训计划（ICON）旨在提供以学生为中心的， 计算认知神经科学中的跨学科培训，该培训整合了来自多个量表的数据和 分析水平。理解人脑和心理健康的变革性增长需要 跨多个分析的集成。遗传学和细胞生物学的最新历史进步是 为理解神经功能的基础铺平道路。在频谱的另一端，方法 用于成像和刺激人的大脑无创，导致了革命性的进步 宏观组织支持感知，动机和认知。现在，在 这两个量表之间的“系统”级别开始揭示活动，连通性和计算 神经回路。此系统级进度的出现具有连接核心电路的希望 对新兴人类行为进行计算，并导致精神疾病的详细，经诊断的模型。 但是，正如我们最近所说的 在电路级计算与人类系统的新兴功能之间建立直接联系。我们 相信以这种方式整合系统和人类神经科学需要系统的方法 关于两个关键策略。首先，必须使用正式的计算模型来提供主要的联系 分析水平；第二，必须应用互补方法，在理想情况下平行 在协调方面进行的人类和非人类研究。实现这些目标需要新一代 可以充分利用多种技术和数据源的科学家，并且深深地精通 计算理论。传统的神经科学培训依赖于限制学生的学徒模型 到一个实验室和询问级别。因此，需要专门配备专门的培训计划 这种“互动主义”方法的神经科学家。图标将提供此培训，强调这两个宗旨： I.计算是在级别之间翻译的关键。学生必须严格定量 接受了正式理论的培训。一个紧密的推论是，它们必须流利的高级分析方法 跨级集成所需的（例如机器学习）。 ii。下一代学者必须具有多个级别的专业知识。必须对学生进行培训才能使用 并整合了多种方法和数据源。此外，他们必须具有追求技能（和勇气） 想法是他们下一个最合乎逻辑的步骤，要受到质疑而不是技术有限的想法。学生将接受培训 为了跨人类认知神经科学等领域进行综合研究项目，系统 神经科学和计算神经科学。