Training Program for Interactionist Cognitive Neuroscience (ICoN)

我们的互动认知神经科学（ICoN）培训计划旨在提供以学生为中心， 计算认知神经科学的跨学科培训，整合来自多个尺度的数据， 分析水平。理解人类大脑和心理健康的变革性成果需要 跨多个层次的分析。遗传学和细胞生物学的最新历史进展是 为理解神经功能的基本原理铺平了道路。在光谱的另一端， 非侵入性成像和刺激人类大脑的技术， 支持感知、动机和认知的宏观组织。现在，一个主要的努力， 这两个尺度之间的“系统”水平开始揭示活动、连接和计算 神经回路这种系统级进展的出现，为连接核心电路提供了希望 计算到人类的紧急行为，并导致详细的，精神疾病的transdiagnosis模型。 然而，正如我们最近所争论的那样（Badre，Frank和摩尔，2015年神经元），实现这一承诺需要 在电路级计算和人类系统的紧急功能之间建立直接联系。我们 我认为，以这种方式整合系统和人类神经科学需要一种系统的方法， 两个关键战略。首先，必须使用正式的计算模型来提供 第二，必须应用互补的方法，在理想情况下， 协调进行的人类和非人类研究。实现这些目标需要新一代的 科学家可以充分利用多种技术和数据源，并且精通 计算理论传统的神经科学培训依赖于学徒模式， 一个实验室和调查级别因此，需要专门的培训计划， 神经科学家对这种“互动主义”的态度。ICoN将提供这种培训，强调两个原则： I.计算是在级别之间转换的关键。学生必须严格量化 受过正规理论训练。一个密切的推论是，他们必须流利的先进的分析方法 跨级集成所必需的（例如，机器学习）。 二.下一代学者必须具备多层次的专业知识。学生必须接受培训， 并集成多种方法和数据源。此外，他们必须有能力（和勇气）去追求 想法到他们的下一个最合乎逻辑的步骤，是问题驱动，而不是技术限制。学生将接受培训 进行跨领域的综合研究项目，如人类认知神经科学，系统 神经科学和计算神经科学。

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.