适应性建模和推断神经元组合中的更高阶协调:一个动态贪的估计方法
Shoutik Mukherjee1,2, Behtash Babadi1,2
1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA.
bioRxiv : the preprint server for biology
|October 31, 2023
概括
这项研究引入了一种新的算法来分析复杂的神经协调,超越了简单的双向活动. 该方法揭示了对人类和老鼠皮质组合中的大脑状态过渡的新见解.
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.
- 系统神经科学 系统神经科学
背景情况:
- 对神经功能如刺激表达和记忆而言,协调的集体尖活动至关重要.
- 现有的分析神经同步的方法往往忽略了更高阶的相互作用,或者需要大量的数据.
- 当前基于模型的分析可能会对相互作用相关性施加限制性假设,并需要重复试验.
研究的目的:
- 开发一种新的统计框架,用于识别集体尖端活动中的更高层次协调.
- 克服无模型同步措施和现有的基于模型的方法的局限性.
- 提供一种不需要重复试验的方法,并且对相互作用相关性的假设较少.
主要方法:
- 提出了一个基于离散标记点过程模型的自适应贪过算法.
- 开发了一个统计推断框架,用于识别重要的更高层次协调.
- 在精确的统计测试中,对贪地估计的参数构建的置信区间.
主要成果:
- 在模拟的神经元组件上证明了拟议方法的实用性.
- 将这些方法应用于来自人类和老鼠皮质组件的多电极记录.
- 确定了关于大脑状态转换期间局部人口活动动态的新见解.
结论:
- 开发的自适应性贪过算法有效地识别神经元组合中的更高阶协调.
- 统计推理框架为分析复杂的神经动态提供了强大的工具.
- 这些发现提供了关于大脑状态转换背后的神经机制的新视角.
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