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 Diba3
1Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, U.S.A. dl67@rice.edu.
Neural computation
|July 19, 2024
概括
这项研究扩展了Poisson-Gaussian过程隐性变量模型 (P-GPLVM),以推断新数据中的神经活动模式. 改进后的模型允许无人监督的解码和分析神经的重新激活,包括在尖的波浪浪.
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
- 系统神经科学 系统神经科学
背景情况:
- 无监督的神经解码需要将内部神经模式的重新激活与外部变量调整分离.
- 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,但缺乏用于新数据的潜轨道推理.
- 估计神经反应受限于无法推断先前学习的潜空间中的潜轨迹.
研究的目的:
- 扩展P-GPLVM用于新型神经数据中的潜变量推理,使无监督的神经解码和重新激活分析成为可能.
- 开发用于在时间压缩的神经活动中进行受约束的潜变量推理的原则方法,例如人口爆发事件.
- 引入用于评估神经模式重新激活有效性和推断编码体验的指标.
主要方法:
- 扩展了P-GPLVM以使用学习的平滑性和映射信息推断新神经数据的潜在轨迹.
- 开发了对暂时压缩的神经活动 (例如,海马的尖波) 的受约束的潜变量推断.
- 在迷宫探索和跑步期间将扩展框架应用于海马体合奏记录,分析神经模式的重新激活.
主要成果:
- 复制了P-GPLVM从海马录音中学习隐藏空间编码动物位置的能力.
- 证明已学习的潜空间可以区分迷宫背景.
- 在跑步期间观察到神经模式的重新激活,与训练数据的体验相似性相关.
- 在人口爆发事件期间成功估计了神经模式的重新激活,识别了重播事件.
结论:
- 扩展的P-GPLVM框架提供了一个强大的工具,用于对神经活动和解码进行无监督分析.
- 该方法能够对神经活性进行强有力的推断,即使对于像尖波浪等压缩活动模式.
- 这种方法有助于回答神经科学中关于神经表征和记忆重复的关键问题.
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