从Poisson观测中对静态和切换动态系统模型进行无监督学习
Christian Y Song1, Maryam M Shanechi1,2,3,4
1Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America.
Journal of neural engineering
|December 12, 2023
概括
这项研究介绍了Poisson Cubature Filter (PCF),用于对神经群体动态进行无监督学习. PCF准确地识别了尖端数据中的行为模式,超过了现有方法,特别是有限的数据.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 对神经群体动态的准确建模对于理解行为至关重要.
- 切换动态系统提供可解释性,但需要对未标记的数据进行无监督学习.
- 现有的方法使用拉普拉斯近似Poisson观测可以导致不准确的学习.
研究的目的:
- 开发一种新的,准确的无监督学习方法,用于神经激增活动中的Poisson观测.
- 改进神经群体动态中潜在状态和模式的识别.
- 在现有的学习框架中克服拉普拉斯近似的局限性.
主要方法:
- 衍生了Poisson立方体波器 (PCF),一种基于对Poisson观测的确定性抽样的新推断方法.
- 在无监督学习框架内嵌入PCF,使用最小平均平方误差方法.
- 用确定性抽样和立方体规则对Poisson观测进行近似分析挑战术语.
主要成果:
- 在静止和开关动态系统中,PCF实现了准确的无监督学习.
- 在模拟和真实神经数据中,PCF显著超过了之前基于拉普拉斯近似的方法.
- PCF显示出更高的数据效率和更可靠的系统识别,特别是在较小的数据集.
- 在运动皮层数据中,PCF发现了可解释的,与行为相关的模式.
结论:
- 开发的基于PCF的无监督学习方法准确地识别出潜伏的制度和人口升活动中的状态.
- 与现有的方法相比,PCF为模拟神经动态提供了一种更强大,更有效的数据方法.
- 这一进步对基础神经科学研究和神经技术应用有重大影响.
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