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Decoding Natural Behavior from Neuroethological Embedding
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利用跨试验和行为会议的相关性来改善神经解码.

Yizi Zhang1, Hanrui Lyu2, Cole Hurwitz3

  • 1Department of Statistics, Columbia University, New York, NY, USA.

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|November 27, 2025
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概括

新的多会话模型捕获交叉试验和交叉会话的神经依赖,改善行为解码. 这些可解释的模型提供了对神经表征和大脑范围的时间尺度的洞察.

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神经像素的记录记录.在决策过程中做出决定.电力生理学 电力生理学可解释的模型可以解释模型.多会话建模的多会话建模.神经解码的神经解码降级回归的降级回归方法状态空间模型的状态空间模型

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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.
  • 机器学习在生物学中的应用

背景情况:

  • 传统的神经解码器分析单个试验,忽视关键的交叉会议和交叉试验的神经活动模式.
  • 动物的行为受到先前经验的影响,在重复任务中表现出一致的神经模式.

研究的目的:

  • 开发新的计算模型,将神经活动整合到多个会话和试验中.
  • 通过计算时间依赖,改进从神经数据解码行为.

主要方法:

  • 引入了两个互补的多会话模型:降级回归和状态空间模型.
  • 将模型应用于来自国际大脑实验室 (IBL) 鼠标神经像素数据集的大数据集 (433 次会议,270 个大脑区域).

主要成果:

  • 提出的解码器在解码四种不同的行为方面显著超过了传统方法.
  • 在不同的数据集,物种和实验任务中概括模型性能.
  • 实现了高效和可解释的神经表征,识别了与任务相关的单个神经元贡献和整个大脑激活时间表.

结论:

  • 多会话建模方法优于单会话方法,用于理解行为的神经相关性.
  • 这些模型提供可解释的,低维的神经表征,对神经科学研究有价值.
  • 开发的方法为神经解码提供了对深度学习的强大,高效的替代方案.