概念的几何变化在人类视觉皮层中的多样化
bioRxiv : the preprint server for biology
|December 9, 2024
概括
大脑活动和深度神经网络使用不同的几何策略来对视觉概念进行分类. 虽然人类视觉皮层依赖于多重中心距离 (信号),但深度神经网络利用维度.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 大脑中视觉概念的准确线性分类取决于概念多元体的几何.
- 了解这些几何性质如何在大脑区域和深度神经网络 (DNN) 之间存在差异至关重要.
研究的目的:
- 研究人类视觉皮层和DNN中的概念多元体的几何性质.
- 为了确定这些属性如何影响几次射击线性分类的准确性.
- 为了比较大脑和DNN所使用的几何策略.
主要方法:
- 利用大型fMRI数据集来估计人类视觉皮层中概念多元体的几何性质.
- 在深度神经网络 (DNN) 中分析概念多样性的几何性质.
- 应用了一种最近的理论,将多元体几何学与少数镜头线性分类准确性联系起来.
主要成果:
- 人类视觉皮层的分类精度变化是由多元中心 (几何信号) 之间的距离驱动的.
- 在DNN层中分类准确度的变化主要是由多重维度 (维度) 的有效数量驱动的.
- 在大脑区域和DNN层之间存在强烈的负相关性信号和维度.
结论:
- 人类视觉皮层和DNN使用不同的几何策略来对视觉概念进行线性分类.
- 尽管有不同的策略,但两种系统都在一个共同的约束下运行,将信号和维度联系起来.
- 这项研究提供了对生物和人工系统中视觉处理背后的计算原理的见解.
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