用上下文学习和AI增强的张量方法革新行为神经科学发现
Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods
AI总结：
本文提出了一种结合上下文学习和AI增强的张量方法，用于加速行为神经科学领域中从实验数据中获得见解的流程。
摘要：
科学发现流程通常涉及复杂、僵化且耗时的过程，从数据准备到分析和解释发现。最近的AI进展有可能以一种方式改变此类流程，使领域专家能够专注于解释和理解发现，而不是调试僵化的流程或手动标注数据。作为数据科学/AI研究人员与行为神经科学家之间积极合作的一部分，我们展示了一个AI增强的流程示例，专门设计用来改变并加速团队中领域专家从实验数据中获得见解的方式。本应用领域是行为神经科学，研究小鼠的恐惧泛化，这是一个重要的问题，其进展可以加深我们对临床重要且常导致严重后果的条件如创伤后应激障碍（PTSD）的理解。我们识别出“上下文学习”（ICL）作为领域专家自动化流程中部分步骤的合适接口，而无需或熟悉AI模型训练和微调。我们展示了其在数据准备和模式解释中的显著有效性。此外，我们引入了新的AI增强的张量分解模型，使能够更流畅地从异构数据中发现模式。我们彻底评估了所提出流程的实验表现，展示了其优于该领域标准做法以及合理ML基线（不落入ICL范式）的优越性能，以确保我们在追求无缝且易于使用的接口时不牺牲性能。最后，我们展示了有效的发现，结果由团队中的领域专家验证。
英文摘要：
Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting findings. Recent advances in AI have the potential to transform such pipelines in a way that domain experts can focus on interpreting and understanding findings, rather than debugging rigid pipelines or manually annotating data. As part of an active collaboration between data science/AI researchers and behavioral neuroscientists, we showcase an example AI-enhanced pipeline, specifically designed to transform and accelerate the way that the domain experts in the team are able to gain insights out of experimental data. The application at hand is in the domain of behavioral neuroscience, studying fear generalization in mice, an important problem whose progress can advance our understanding of clinically significant and often debilitating conditions such as PTSD (Post-Traumatic Stress Disorder). We identify the emerging paradigm of "In-Context Learning" (ICL) as a suitable interface for domain experts to automate parts of their pipeline without the need for or familiarity with AI model training and fine-tuning, and showcase its remarkable efficacy in data preparation and pattern interpretation. Also, we introduce novel AI-enhancements to tensor decomposition model, which allows for more seamless pattern discovery from the heterogeneous data in our application. We thoroughly evaluate our proposed pipeline experimentally, showcasing its superior performance compared to what is standard practice in the domain, as well as against reasonable ML baselines that do not fall under the ICL paradigm, to ensure that we are not compromising performance in our quest for a seamless and easy-to-use interface for domain experts. Finally, we demonstrate effective discovery, with results validated by the domain experts in the team.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
