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。虽然系统神经科学的技术革命产生了一系列广泛的工具来观察和操纵神经回路，但行为技术却落后了。行为测量和描述的问题是复杂的，因为行为是多种多样的。为了研究行为，实验室采用复杂的行为系统，通常与定制的硬件和软件相结合，并使用这些来定义动物需要学习和执行的任务。因此，行为任务的描述与每个系统的硬件相关联，并且没有通用的抽象描述格式来桥接采用不同系统的实验室。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化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，使任务描述的广泛共享和发布成为可能，并支持跨系统实现。该项目对于提高行为神经科学的科学严谨性和可重复性具有广泛的益处。此外，该项目减少了复杂的行为神经科学实验的重大障碍，使他们在本科课程项目的范围内，并使学生接触到高度跨学科的方法，借鉴神经科学，计算机科学，心理学和语言学。该项目需要开发一种正式的计算机语言，可以以独立于平台的方式描述所有实验室行为任务。目前，行为任务主要是通过流程图和文本解释的组合来描述的，超出了用于控制行为硬件的特定软件代码。这些描述没有提供正式的说明，以确保相同的重新实现或类似描述的范式的严格比较。此外，硬件绑定的代码往往隐藏行为任务的逻辑。这个项目的目标是设计一种新的语言，有限状态机描述的扩展，既可以作为出版物的抽象说明，也可以作为控制硬件的现成程序。新的行为任务描述语言建立在虚拟有限状态机类之上，虚拟有限状态机是一种有限状态机扩展框架，旨在为实时控制系统提供软件规范。此外，新的任务描述语言引入了封装常见设计主题的方法，因此它们可以被视为原语和附加功能来定义试验结构。一致的高级描述增强了行为任务设计，将关键功能提炼成易于理解和形式上严格的结构。为了演示这种语言的使用，产生了一个交钥匙实现，包括一个图形编辑器。此外，还生成了一系列常用行为任务的模板。平台无关的行为描述语言揭示了底层的行为任务逻辑，并使其更容易描述，复制和跨实验室共享行为任务。NeuroNex创新奖是BRAIN Initiative和NSF了解大脑活动的一部分。