整合了自身感知以及视觉上下文和效应副本,以进行可靠的预测
Océane Dauzere-Peres1, Antoine Wystrach2
1Centre de Recherches sur la Cognition Animale, CBI,CNRS, Université Paul Sabatier, Toulouse, France. oceane.dauzere-peres@univ-tlse3.fr.
Nature communications
|December 1, 2024
概括
在中的前进模型使用效能副本,自身感受和视觉输入来预测光流. 这种多式联运集成通过调整预测错误来完善机动控制.
科学领域:
- 神经科学是一个神经科学.
- 动物行为 动物行为
- 机器人技术 机器人技术 机器人技术
背景情况:
- 前模型预测行动的感官后果.
- 效应副本是取消预测感官输入的运动信号.
- 昆虫在飞行控制中使用效应副本来检测光流.
研究的目的:
- 在行走的的光流检测中研究先进的模型.
- 确定光学流量检测如何与机动控制集成.
- 探索昆虫导航中预测的基础机制.
主要方法:
- 使用一个虚拟现实设置与Cataglyphis velox.
- 操纵了运动和光流之间的关系.
- 记录和分析的运动和光流感知.
主要成果:
- 根据它们的运动来预测光流.
- 预测包括效应副本,自感反和视觉全景.
- 预测错误是按眼睛计算的,并用于移动调整而集成.
结论:
- 昆虫前进模型是复杂的,集成多式联运信息.
- 自身感知和视觉上下文完善了光流的预测.
- 来自多式预测的错误信号调节了昆虫的运动.
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