V1 中神经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, Frank Tong1,2
1Department of Psychology, Vanderbilt University, Nashville, TN, 37240, USA.
bioRxiv : the preprint server for biology
|September 11, 2023
概括
计算模型表明,主要视觉皮层 (V1) 神经元使用简单的非线性. 这项研究发现,V1反应可以通过一些非线性阶段更好地解释,这挑战了以前的深度学习模型假设.
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:
- 传统模型提出,初级视觉皮层 (V1) 神经元的功能就像Gabor过器一样,具有简单的非线性.
- 最近的卷积神经网络 (CNN) 模型表明V1采用比以前理解的更复杂的非线性计算.
研究的目的:
- 调查接收场的大小,而不是复杂性,是否解释了VGG-19 CNN模型中较低层单元的性能.
- 为了比较VGG-19和AlexNet模型对 V1反应的预测能力.
- 确定解释V1前反应所需的最小数量的非线性阶段.
主要方法:
- 与VGG-19和AlexNet CNN模型进行比较,用于预测 V1对自然和合成图像的神经反应.
- 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正常化和对比度和等非线性贡献.
主要成果:
- 亚历克斯网络的第一个卷积层,具有更大的受体场,比早期VGG-19层更好地预测V1反应.
- VGG-19的最佳表现是在七个非线性步骤后实现的,而AlexNet的最佳表现是在第一层.
- 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.
- 减少输入图像大小将VGG-19的最佳层转移到早期阶段,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加工阶段来充分解释.
- 受感场的大小是CNN模型预测V1活动的性能的一个关键因素.
- 在早期视觉处理中计算的复杂性可能低于一些深度学习模型所建议的.
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