• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

V1非线性特性源自局部到全局的非线性独立成分分析。

V1 non-linear properties emerge from local-to-global non-linear ICA.

作者信息

Malo Jesús, Gutiérrez Juan

机构信息

Dept. d'Optica, Facultat de Física, Universitat de València, Spain.

出版信息

Network. 2006 Mar;17(1):85-102. doi: 10.1080/09548980500439602.

DOI:10.1080/09548980500439602
PMID:16613796
Abstract

It has been argued that the aim of non-linearities in different visual and auditory mechanisms may be to remove the relations between the coefficients of the signal after global linear ICA-like stages. Specifically, in Schwartz and Simoncelli (2001), it was shown that masking effects are reproduced by fitting the parameters of a particular non-linearity in order to remove the dependencies between the energy of wavelet coefficients. In this work, we present a different result that supports the same efficient encoding hypothesis. However, this result is more general because, instead of assuming any specific functional form for the non-linearity, we show that by using an unconstrained approach, masking-like behavior emerges directly from natural images. This result is an additional indication that Barlow's efficient encoding hypothesis may explain not only the shape of receptive fields of V1 sensors but also their non-linear behavior.

摘要

有人认为,在类似全局线性独立成分分析(ICA)的阶段之后,不同视觉和听觉机制中的非线性的目的可能是消除信号系数之间的关系。具体而言,在施瓦茨和西蒙切利(2001年)的研究中表明,通过拟合特定非线性的参数以消除小波系数能量之间的依赖性,能够再现掩蔽效应。在这项工作中,我们给出了一个不同的结果,它支持相同的高效编码假设。然而,这个结果更具普遍性,因为我们不是假设非线性具有任何特定的函数形式,而是表明通过使用一种无约束的方法,类似掩蔽的行为直接从自然图像中出现。这一结果进一步表明,巴洛的高效编码假设不仅可以解释V1传感器感受野的形状,还可以解释它们的非线性行为。

相似文献

1
V1 non-linear properties emerge from local-to-global non-linear ICA.V1非线性特性源自局部到全局的非线性独立成分分析。
Network. 2006 Mar;17(1):85-102. doi: 10.1080/09548980500439602.
2
Nonlinear and extra-classical receptive field properties and the statistics of natural scenes.非线性和超经典感受野特性与自然场景统计
Network. 2001 Aug;12(3):331-50.
3
Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds.自然图像视觉搜索中的虚拟进化产生具有抑制性周边的行为感受野。
Vis Neurosci. 2009 Jan-Feb;26(1):93-108. doi: 10.1017/S0952523809090014. Epub 2009 Mar 12.
4
Receptive field self-organization in a model of the fine structure in v1 cortical columns.V1 皮质柱精细结构模型中的感受野自组织
Neural Comput. 2009 Oct;21(10):2805-45. doi: 10.1162/neco.2009.07-07-584.
5
The maximum range and timing of excitatory contextual modulation in monkey primary visual cortex.猴子初级视觉皮层中兴奋性上下文调制的最大范围和时间
Vis Neurosci. 2006 Sep-Oct;23(5):721-8. doi: 10.1017/S0952523806230049.
6
[Linear and nonlinear properties of cat visual cortex receptive fields].[猫视觉皮层感受野的线性和非线性特性]
Fiziol Zh SSSR Im I M Sechenova. 1980 Jan;66(1):3-18.
7
Shaping up simple cell's receptive field of animal vision by ICA and its application in navigation system.基于独立成分分析塑造动物视觉简单细胞感受野及其在导航系统中的应用
Neural Netw. 2003 Jun-Jul;16(5-6):609-15. doi: 10.1016/S0893-6080(03)00133-3.
8
Natural signal statistics and sensory gain control.自然信号统计与感觉增益控制。
Nat Neurosci. 2001 Aug;4(8):819-25. doi: 10.1038/90526.
9
V4 receptive field dynamics as predicted by a systems-level model of visual attention using feedback from the frontal eye field.基于来自额叶眼区的反馈,由视觉注意系统水平模型预测的V4感受野动态。
Neural Netw. 2006 Nov;19(9):1371-82. doi: 10.1016/j.neunet.2006.08.006. Epub 2006 Oct 2.
10
Input-output statistical independence in divisive normalization models of V1 neurons.V1神经元的归一化模型中的输入-输出统计独立性
Network. 2003 Nov;14(4):733-45.

引用本文的文献

1
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly.主曲线的顺序学习:实时总结数据流
Entropy (Basel). 2021 Nov 18;23(11):1534. doi: 10.3390/e23111534.
2
Spatio-chromatic information available from different neural layers via Gaussianization.通过高斯化从不同神经层获得的空间色度信息。
J Math Neurosci. 2020 Nov 11;10(1):18. doi: 10.1186/s13408-020-00095-8.
3
In Praise of Artifice Reloaded: Caution With Natural Image Databases in Modeling Vision.赞重新构建的人工图像:在视觉建模中使用自然图像数据库时需谨慎。
Front Neurosci. 2019 Feb 18;13:8. doi: 10.3389/fnins.2019.00008. eCollection 2019.
4
Derivatives and inverse of cascaded linear+nonlinear neural models.级联线性+非线性神经网络模型的导数和反演。
PLoS One. 2018 Oct 15;13(10):e0201326. doi: 10.1371/journal.pone.0201326. eCollection 2018.
5
Topographic Independent Component Analysis reveals random scrambling of orientation in visual space.地形独立成分分析揭示了视觉空间中方向的随机混乱。
PLoS One. 2017 Jun 22;12(6):e0178345. doi: 10.1371/journal.pone.0178345. eCollection 2017.
6
Visual aftereffects and sensory nonlinearities from a single statistical framework.基于单一统计框架的视觉后效与感觉非线性
Front Hum Neurosci. 2015 Oct 13;9:557. doi: 10.3389/fnhum.2015.00557. eCollection 2015.
7
Cortical Surround Interactions and Perceptual Salience via Natural Scene Statistics.皮质环绕相互作用和基于自然场景统计的感知显著性。
PLoS Comput Biol. 2012;8(3):e1002405. doi: 10.1371/journal.pcbi.1002405. Epub 2012 Mar 1.