品牌:一个闭环实验平台,使用深度网络模型进行闭环实验
bioRxiv : the preprint server for biology
|August 23, 2023
概括
实时异步神经解码 (BRAND) 框架的后端可以在神经科学实验中实时使用人工神经网络 (ANN). 品牌提供低延迟,并支持多种编程语言,用于先进的脑电脑接口.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 人工神经网络 (ANN) 对神经解码具有强大作用,但在实时系统中面临部署挑战.
- 现有的框架难以支持ANN的高级语言以及低延迟的数据处理.
研究的目的:
- 介绍实时异步神经解码 (BRAND) 的后端,这是神经科学中实时ANN的新框架.
- 实现先进机器学习模型的无集成到闭环脑电脑界面 (BCI) 实验中.
主要方法:
- 品牌使用一个模块化架构的Linux流程 (节点) 通过数据流进行通信.
- 异步设计允许在不同的时间尺度上并行执行数据采集,控制和分析.
- 雷迪斯促进了快速的进程间通信,支持54种编程语言以实现广泛的兼容性.
主要成果:
- 对于大量的神经数据,BRAND证明了在600微秒以下的进程间延迟.
- 在BCI中实现了递归神经网络 (RNN) 解码的不到8毫秒的延迟.
- 在临床试验参与者中成功执行实时信号处理,RNN解码和任务控制.
结论:
- 品牌提供了一个快速,模块化和语言不可知论平台,用于将先进的ANN集成到闭环神经科学实验中.
- 这一框架显著降低了研究人员在实时脑电脑接口中使用尖端机器学习工具的障碍.
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