Machine Intelligence for Neuroscience Experimental Control

用于神经科学实验控制的机器智能

• 批准号: BB/W019132/1
• 负责人: Thomas Mrsic-Flogel
• 金额: $ 90.23万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FW019132%2F1
• 关键词: Machine; Intelligence; Neuroscience; Experimental; Control

Understanding the brain and the behaviour it generates is a major scientific challenge of our era. To succeed, scientists must be able to explain how animal behaviour relates to neural activity across different brain regions. This requires careful design and manipulation of behavioural experiments, where experimenters either record or manipulate neural activity while the animals (e.g. non-human primates, rodents, fish, insects) engage in specific behaviours which need to be carefully observed and quantified. Experiments in behavioural and brain science laboratories require software that integrates and controls hardware from multiple recording devices (video, electrodes for neural activity measurement, sensors), and analysis tools that can interpret large and complex behavioural and neural datasets. Scientists studying brain and behaviour dedicate the majority of their time designing experiments and analysing the data, with least time spent on data acquisition itself, which may impact the quality of data. Moreover, hundreds of neuroscience research groups worldwide develop their own experimental and analytical tools, most using different programming languages, leading to inefficiencies in data sharing and analysis, and impacting reproducibility (i.e. how easy it is for someone else to repeat the same experiment). Here we propose to provide the scientific community with a software tool that will dramatically increase the efficiency of experimental control and data analysis. We will do so by developing a new set of functionalities to an existing software platform, Bonsai. Bonsai is a fully integrated software environment that emphasises performance, flexibility, and ease-of-use, allowing scientists with no previous programming experience to quickly develop their own high-performance data acquisition and experimental control systems.Thus far, Bonsai has been adopted by hundreds of scientists worldwide to provide interactive experimental control in behavioural and brain sciences. In this proposal, we aim to extend Bonsai's functionality with a toolbox of online and offline Machine Intelligence tools for analysis of behavioural and neural datasets, and to create an open-access platform for software sharing. Bonsai's enhanced functionality will enable new types of research, and speed up discovery and improve efficiency by (i) providing access to such tools to laboratories lacking expertise, (ii) reducing the need to reinvent the same tools in multiple labs and (iii) standardising the data processing streams, thus increasing reproducibility across laboratories. We believe this effort will enable and accelerate new discoveries in how the brain generates behaviour.

了解大脑及其产生的行为是我们这个时代的一个重大科学挑战。为了取得成功，科学家必须能够解释动物行为与不同大脑区域的神经活动之间的关系。这需要仔细设计和操作行为实验，实验者记录或操纵神经活动，而动物（例如非人类灵长类动物，啮齿动物，鱼类，昆虫）参与需要仔细观察和量化的特定行为。行为和脑科学实验室中的实验需要集成和控制多个记录设备（视频、神经活动测量电极、传感器）硬件的软件，以及可以解释大型复杂行为和神经数据集的分析工具。研究大脑和行为的科学家将大部分时间用于设计实验和分析数据，而花费在数据采集本身的时间最少，这可能会影响数据的质量。此外，全球数百个神经科学研究小组开发了自己的实验和分析工具，大多数使用不同的编程语言，导致数据共享和分析效率低下，并影响可重复性（即其他人重复相同实验的容易程度）。在这里，我们建议为科学界提供一个软件工具，这将大大提高实验控制和数据分析的效率。我们将通过为现有的软件平台Bonsai开发一套新的功能来实现这一目标。Bonsai是一个完全集成的软件环境，强调性能、灵活性和易用性，使没有编程经验的科学家能够快速开发自己的高性能数据采集和实验控制系统。迄今为止，Bonsai已被全球数百名科学家采用，为行为科学和脑科学提供交互式实验控制。在这项提案中，我们的目标是通过在线和离线机器智能工具工具箱来扩展Bonsai的功能，以分析行为和神经数据集，并创建一个开放访问的软件共享平台。Bonsai的增强功能将使新类型的研究成为可能，并通过以下方式加快发现和提高效率：（i）为缺乏专业知识的实验室提供这些工具，（ii）减少在多个实验室重新发明相同工具的需要，以及（iii）标准化数据处理流程，从而提高实验室的可重复性。我们相信，这项努力将促进并加速大脑如何产生行为的新发现。