通用适配模型从产前视网膜波中学习边缘表示
Lalit Pandey1, Samantha M W Wood2, Benjamin Cappell3
1Informatics Department, Indiana University Bloomington, United States of America.
Cognition
|January 30, 2026
概括
定向选择性,即感知定向边缘的能力,源于产前视觉系统的适应. 在视网膜波上训练的通用模型自发地发展了这一关键的视觉处理能力.
科学领域:
- 计算神经科学是一种神经科学.
- 发展生物学 发展生物学
- 视觉科学 视觉科学 视觉科学
背景情况:
- 定向选择性是生物视觉在各种物种的基本特征.
- 导向选择性的发育起源,无论是遗传还是经验依赖的,仍然在很大程度上是未知的.
研究的目的:
- 调查导向选择性是否出现在产前发育期间的经验依赖的适应过程中.
- 测试通用计算模型是否可以在没有先天的先验的情况下自发地发展导向选择性.
主要方法:
- 使用了通用图像可计算的配件模型 (变压器),没有先前存在的方向选择性.
- 训练模型使用无监督时间学习与生物可信的产前数据 (视网膜波).
- 在各种模型架构,训练条件和特定物种的视网膜波中验证了结果.
主要成果:
- 模型自发地从头开始发展出强大的定向选择性.
- 导向选择性的出现取决于适应产前视觉输入 (视网膜波).
- 在不同的模型大小和训练参数中,结果一致.
结论:
- 导向选择性来自于适应产前经验的域-通用合适机制,而不是遗传预定.
- 支持视觉系统中学习和神经发育的经验依赖理论.
- 建议一种统一的机制来开发跨物种的边缘表示.
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