NeuroNex Innovation Award: Behavioral Technologies

NeuroNex 创新奖：行为技术

• 批准号: 1707394
• 负责人: Adam Kepecs
• 金额: $ 80万
• 依托单位: Cold Spring Harbor Laboratory
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707394&HistoricalAwards=false
• 关键词: NeuroNex; Innovation; Award; Behavioral; Technologies

Producing behavior is the brain's principal function. While a technological revolution in systems neuroscience yielded a broad array of tools to observe and manipulate neural circuits, behavioral technologies have lagged behind. The problem of behavioral measurement and description is as complex as behaviors are diverse. To study behavior, laboratories employ complex behavioral systems, often in combination with custom-made hardware and software, and use these to define the tasks animals are required to learn and perform. As a consequence, the descriptions of behavioral tasks are tied to the hardware of each system, and there is no general, abstract description format to bridge across laboratories employing different systems. Building on insights from computer science, computational linguistics, and psychology, the goal of this project is to develop a formal language to describe behavioral tasks. This new behavioral task description language enhances accurate task design, improves reproducibility of existing tasks, enables widespread sharing and publication of task descriptions, and supports cross-system implementation. The project has broad benefits for improving scientific rigor and reproducibility in behavioral neuroscience. Moreover, the project reduces a significant barrier to sophisticated behavioral neuroscience experiments, putting them within reach of undergraduate class projects, and exposing students to a highly interdisciplinary approach, drawing on neuroscience, computer science, psychology, and linguistics. The project entails the development of a formal computer language that can describe all laboratory behavioral tasks in a platform-independent manner. Currently, behavioral tasks are described largely with a combination of flowcharts and textual explanation, beyond the specific software codes used to control behavioral hardware. These descriptions do not provide formal accounts that ensure identical re-implementation or the rigorous comparison of similarly described paradigms. In addition, the hardware-bound codes tend hide the logic of behavioral tasks. The objective of this project is to design a new language, an extension to finite state machine descriptions, that can serve both as abstract illustrations for publications and also ready-to-run programs to control hardware. The new behavioral task description language builds on the class virtual finite state machines, a finite state machine extension framework that was developed to provide software specifications for real-time control systems. Additionally, the new task description language introduces ways to encapsulate common design motifs so they can be treated as primitives and additional features to define trial structures. The consistent high-level description enhances behavioral task design, distilling critical features into an easy-to-understand and formally rigorous structure. To demonstrate the use of this language, a turn-key implementation, including a graphical editor, is produced. In addition, templates for an array of commonly used behavioral tasks are produced. The platform-independent behavior description language exposes the underlying behavioral task logic and makes it easier to describe, reproduce, and share behavioral tasks across laboratories. This NeuroNex Innovation Award is part of the BRAIN Initiative and NSF's Understanding the Brain activities.

产生行为是大脑的主要功能。尽管神经科学的技术革命产生了一系列观察和操纵神经回路的广泛工具，但行为技术却落后了。行为衡量和描述的问题与行为一样复杂。为了研究行为，实验室通常使用复杂的行为系统，通常与定制的硬件和软件结合使用，并使用这些系统来定义动物需要学习和执行的任务。结果，行为任务的描述与每个系统的硬件相关联，并且没有一般的抽象描述格式可以跨越采用不同系统的实验室桥接。基于计算机科学，计算语言学和心理学的见解，该项目的目标是开发一种形式的语言来描述行为任务。这种新的行为任务描述语言增强了准确的任务设计，提高了现有任务的可重复性，实现了任务描述的广泛共享和发布，并支持跨系统实现。该项目在改善行为神经科学的科学严谨性和可重复性方面具有广泛的好处。此外，该项目减少了复杂的行为神经科学实验的重大障碍，使其在本科班级项目的范围内，并使学生采用高度跨学科的方法，利用神经科学，计算机科学，心理学和语言学。该项目需要开发正式的计算机语言，该语言可以以独立于平台的方式来描述所有实验室行为任务。当前，除了用于控制行为硬件的特定软件代码之外，还通过流程图和文本说明的结合来描述行为任务。这些描述不提供正式的帐户，以确保重新实现相同的重新实现或类似描述的范例的严格比较。此外，限制硬件的代码倾向于隐藏行为任务的逻辑。该项目的目的是设计一种新语言，即有限状态机器描述的扩展，既可以用作出版物的抽象插图，也可以用作控制硬件的即将运行的程序。新的行为任务描述语言基于类虚拟有限状态机，这是一种有限状态机器扩展框架，旨在为实时控制系统提供软件规格。此外，新任务说明语言引入了封装通用设计图案的方法，因此可以将其视为原始图和其他功能以定义试验结构。一致的高级描述增强了行为任务设计，将关键特征提炼成易于理解和正式严格的结构。为了证明这种语言的使用，生产了包括图形编辑器在内的旋转实现。另外，生成了一系列常用行为任务的模板。独立于平台的行为描述语言揭示了基本的行为任务逻辑，并使在实验室之间更容易描述，复制和共享行为任务。该神经元创新奖是大脑计划的一部分，而NSF理解了大脑活动。