准确识别多个相互作用的神经群体之间的通信
Belle Liu1, Jacob Sacks2, Matthew D Golub2
1Graduate Program in Neuroscience, University of Washington.
ArXiv
|September 2, 2025
概括
我们通过动态系统 (MR-LFADS) 开发了多区域潜伏因子分析, 这种新方法提高了对神经群体动态和大脑信息处理的理解.
科学领域:
- 神经科学
- 计算神经科学
- 系统神经科学
背景情况:
- 通过先进的神经记录技术, 能够同时监测多个大脑区域的活动.
- 现有的数据驱动模型往往无法准确区分影响神经群体的来源,从而阻碍了区域间通信的研究.
研究的目的:
- 通过动态系统 (MR-LFADS) 引入一个新的计算框架,以解开神经通信模式.
- 通过分离区域间通信,外部输入和本地动态来提高整个大脑信息处理的准确性.
主要方法:
- 开发了一种序列变量自编码模型MR-LFADS.
- 使用动态系统原理来建模神经群体活动.
- 通过对任务训练的多区域神经网络进行广泛的模拟来验证模型.
主要成果:
- 与现有方法相比,MR-LFADS在模拟神经网络中识别通信方面表现出卓越的性能.
- 该模型成功地预测了电路扰动对未在训练期间使用的大规模电生理学数据的大脑影响.
结论:
- 提供了分析复杂的神经群体动态和区域间通信的强大方法.
- 该模型作为一个有价值的工具来揭示使用真实和合成神经数据的整个大脑信息处理的基本原则.
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