卷积架构是 de novo 的皮层对齐
Atlas Kazemian1, Eric Elmoznino1,2, Michael F Bonner1
1Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.
bioRxiv : the preprint server for biology
|August 6, 2025
概括
卷积神经网络 (CNN) 中的建筑诱导偏差是开发皮层对齐的视觉表示的关键,即使训练最小. 这些偏见使得在广泛的经验形成突触连接之前,表征能够出现.
科学领域:
- 计算神经科学是一种神经科学.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 对于视觉的深度神经网络 (DNN) 中皮层对齐表示的出现尚未完全理解.
- 之前的研究强调了建筑的限制,但在预培训后,各种建筑的成功使人们质疑了它们的主要重要性.
- 本研究探讨了DNN中建筑诱导偏差的作用,特别是卷积架构,用于视觉表示开发.
研究的目的:
- 确定驱动DNN中皮质对齐表示的发展的潜在因素.
- 评估建筑诱导偏差与预训练在实现视觉表示对齐方面的意义.
- 识别特定的建筑操纵,促进皮质对齐的表示.
主要方法:
- 检查了各种架构的DNN,但没有预先培训.
- 量化了这些网络在灵长类动物和人类视觉皮层中预测图像表示的能力.
- 研究了空间域压缩 (聚合) 和特征域扩展 (通道增加) 对表示对齐的影响.
主要成果:
- 具有聚合和特征扩展的卷积架构成功地在没有预先训练的情况下生成了皮质对齐的表示.
- 这些维度操纵在非卷积架构和具有特定架构修改的卷积模型中效果较差.
- 卷积网络固有的架构诱导偏差对于功能扩展的性能增长至关重要.
结论:
- 卷积神经网络 (CNN) 的诱导偏差在开发皮层对齐的视觉表示中发挥着突出作用.
- 这些架构约束与生物视觉原则相一致,足以允许在经验通过突触调之前出现表示.
- 研究结果表明,CNN架构是模仿生物视觉处理的重要因素,独立于广泛的训练数据.
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