应用于 V1 中神经元反应的卷积神经网络模型揭示了有限的非线性处理
Hui-Yuan Miao1,2, Frank Tong1,3,4
1Department of Psychology, Vanderbilt University, Nashville, TN, USA.
Journal of vision
|June 3, 2024
概括
计算模型表明,初级视觉皮层 (V1) 神经元使用很少的非线性阶段. 比较VGG-19和AlexNet模型显示,受体场的大小,而不是复杂性,影响V1响应预测.
科学领域:
- 计算神经科学是一种计算神经科学.
- 计算机视觉 计算机视觉 计算机视觉
- 神经科学是一个神经科学.
背景情况:
- 早期模型提出主要视觉皮层 (V1) 神经元与简单的非线性类似于加博尔过器.
- 最近的卷积神经网络 (CNN) 研究表明V1涉及比以前认为的更复杂的非线性计算.
研究的目的:
- 为了调查受体场的大小,而不是固有的复杂性,解释了下层VGG-19层在预测V1反应中的表现.
- 为了比较VGG-19和AlexNet模型对 V1神经活动的预测能力.
主要方法:
- 对比VGG-19和AlexNet CNNs来预测 V1对自然和合成图像的反应.
- 分析了受感场大小和图像大小对模型性能的影响.
- 利用Gabor金字塔模型来评估像规范化和对比度和等非线性贡献.
主要成果:
- 亚历克斯网的早期层,具有更大的受体场,比VGG-19的下层更好地预测V1反应.
- 经过修改的AlexNet与VGG-19的性能相匹配,使用更少的非线性计算.
- 减少输入图像大小改变了VGG-19的最佳层,支持受体场大小假设.
结论:
- V1前反应可以通过有限数量的非线性处理阶段来解释.
- 受感场的大小是CNN模型预测V1活动的一个关键因素.
- 这些发现挑战了早期视觉处理中广泛的非线性计算的概念.
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