扩展Poisson-Gaussian过程潜变模型用于无监督的神经解码
Della Daiyi Luo1, Bapun Giri2, Kamran Diba2
1Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
bioRxiv : the preprint server for biology
|March 18, 2024
概括
这项研究扩展了Poisson-Gaussian过程隐性变量模型 (P-GPLVM) 来推断新数据中的神经活动模式. 这允许检测重复的内部神经状态,有助于理解神经重复事件.
科学领域:
- 计算神经科学是一种神经科学.
- 机器学习用于神经数据分析
背景情况:
- 无监督的神经解码旨在理解内部神经状态的重复,而不需要外部变量调.
- 波桑高斯过程潜变模型 (P-GPLVM) 在高维尖列车中发现了低维结构,但缺乏对新数据的推断.
- 在新的神经数据中推断潜在轨迹对于估计内部状态重复至关重要.
结论:
- 扩展的P-GPLVM框架允许对神经活动进行无监督的分析,包括PBE.
- 这种方法有助于估计内部神经状态的重复,以识别重复事件.
- 该框架有助于回答有关神经动力学和体验编码的关键科学发现问题.
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