Training Program for Interactionist Cognitive Neuroscience (ICoN)

我们的互动主义认知神经科学(ICON)培训计划旨在提供以学生为中心的、 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和 分析水平。在了解人脑和心理健康方面取得的变革性成果需要 跨多个分析级别的集成。遗传学和细胞生物学的最新历史性进展是 为理解神经功能的基本原理铺平了道路。在光谱的另一端，方法 因为非侵入性地成像和刺激人脑已经导致了发现 支持感知、动机和认知的宏观组织。现在，一项重大的努力在 这两个天平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带来了将核心电路连接起来的希望 对紧急人类行为的计算，并导致精神疾病的详细、跨诊断模型。 然而，正如我们最近所说(Badre，Frank and Moore，2015 Neuron)，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建立一个系统的方法 关于两个关键战略。首先，必须使用正式的计算模型来提供原则上的联系 分析水平；第二，必须应用互补的方法，在理想情况下是并行的 人类研究和非人类研究协调进行。实现这些目标需要新一代 能够充分利用多种技术和数据源，并精通 计算理论。传统的神经科学培训依赖于学徒制模式，这种模式限制了学生 单一的实验室和不同的调查水平。因此，需要专门的培训计划来专门装备 神经学家支持这种“互动主义”的方法。ICON将提供此培训，强调以下两个原则： 计算是在不同级别之间进行转换的关键。学生必须严格量化 受过正规理论训练的。一个密切的推论是，他们必须熟练掌握先进的分析方法 对于跨级集成(例如，机器学习)来说是必要的。 下一代学者必须具备多层次的专业知识。必须训练学生使用 并集成多种方法和数据源。此外，他们必须有追求的技能(和勇气) 将想法转化为下一个最合乎逻辑的步骤，以问题为导向，而不是技术限制。学生们将接受培训 进行跨人类认知神经科学、系统等领域的综合研究项目 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.