一种用于实时估计皮质状态的机器学习方法
David A Weiss1,2, Adriano Mf Borsa1,3, Aurélie Pala4
1Program in Bioengineering, Georgia Institute of Technology, Atlanta, GA, United States of America.
Journal of neural engineering
|January 17, 2024
概括
研究人员开发了快速的数据驱动算法,用于实时估计皮质状态,这是大脑功能的一个关键因素. 这种新方法使用隐藏的半马科夫模型来准确跟踪大脑状态,改善我们对神经动态的理解.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 信号处理 信号处理
背景情况:
- 皮质功能是由内部变量称为"皮质状态"动态调节的.
- 目前用于估计皮质状态的方法通常不精确,不适合实时应用.
- 准确,实时解码皮质状态对于理解大脑功能和开发先进的神经技术至关重要.
研究的目的:
- 开发和实施强大的,数据驱动的算法,以快速,在线地皮状态估计.
- 为了改进推断,模拟皮质状态转换的时间动态.
- 提供一个实时软件工具,用于不断解码皮质状态.
主要方法:
- 利用无监督的高斯混合模型来识别局部场势 (LFP) 信号中的新兴集群.
- 扩展了方法,使用了一个带有Gaussian观测的临时信息的隐藏半马尔科夫模型 (HSMM).
- 在实时系统中实现HSMM算法,并通过模拟实验评估性能.
主要成果:
- 无监督的聚类揭示了电生理学数据中出现的类似状态结构,与兴奋状态相关联.
- 通过建模状态交换动态,HSMM能够实时推断皮质状态.
- 基于HSMM的状态估计显示了对杂的,顺序的电生理学数据的稳定性.
结论:
- 本文介绍了第一个实时软件,用于高时分辨率 (40毫秒) 的连续皮层状态解码.
- 开发的算法和软件有助于理解皮层状态如何动态调节神经功能.
- 该工具为健康和疾病环境中的状态意识大脑机器接口提供了基础.
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