自动运动的紧的多感官表示足以计算外部世界变量.
Christina E May1, Benjamin Cellini2, Floris van Breugel2
1Department of Neuroscience, NYU Grossman School of Medicine, New York, NY 10016, USA.
bioRxiv : the preprint server for biology
|July 14, 2025
概括
可以通过在其导航中心 (PFNs) 中的多感官集成来推断风向. 这种活跃的感知使它们能够估计通过单一感官直接测量不到的外部力量.
科学领域:
- 神经科学是一个神经科学.
- 动物行为 动物行为
- 感官整合 感官整合
背景情况:
- 动物使用外部力量导航,但直接测量是不可能的.
- 大脑整合多模态线索来估计力量的过程还不太清楚.
研究的目的:
- 调查飞行PFN中的多式自动运动提示表示.
- 确定神经回路如何编码感官信息以推断外力.
主要方法:
- 记录了神经对光流和空气流的反应.
- 开发了感官编码的计算模型.
- 应用于神经数据的非线性可观察性分析.
主要成果:
- 一种类型的PFN集成了光流和空气流,具有不同的动态.
- 另一个单独的PFN类型编码空气速度.
- 机动期间的PFN活动允许对外部风向的解码.
结论:
- 活跃的感觉和多感官编码使得无法测量的外部属性的推断成为可能.
- 紧的神经系统可以通过集成的感官处理来推断环境力量.
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