Application of Asymmetric Networks to Movement Detection and Generating Independent Subspaces
Application of Asymmetric Networks to Movement Detection and Generating Independent Subspaces
复制标题
非对称网络在运动检测和生成独立子空间中的应用
DOI：
10.1007/978-3-319-65172-9_23
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Masashi Kawaguchi ,Hiroshi Sasaki
中科院分区：
文献类型：
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作者：
Naohiro Ishii;Toshinori Deguchi;Masashi Kawaguchi ,Hiroshi Sasaki
其突出特点是在视网膜和视觉皮层网络中观察到的平方和校正函数等非线性特征。皮层运动处理的传统模型使用带有 Gabor 滤波器的对称二次函数。本文提出了一种新的非对称网络运动处理模型。首先，使用维纳核分析非对称网络。结果表明，与传统的二次模型相比，具有非线性的非对称网络对于产生定向运动是有效且通用的。其次，数据的独立性最大化是计算神经网络中的一个重要问题。为了明确Gabor函数非对称网络的特性，计算了正交性，在二次模型中不最大化独立性优化的情况下显示了非对称网络的独立特性。将非对称网络独立性的正交分析应用于 V1 和 MT 神经网络，通过使用选择性 Gabor 函数生成独立子空间。
The prominent feature is the nonlinear characteristics as the squaring and rectification functions, which are observed in the retinal and visual cortex networks. Conventional model for motion processing in cortex, uses a symmetric quadratic functions with Gabor filters. This paper proposes a new motion processing model in the asymmetric networks. First, the asymmetric network is analyzed using Wiener kernels. It is shown that the asymmetric network with nonlinearities is effective and general for generating the directional movement compared with the conventional quadratic model. Second, independence maximization of data is an important issue in computational neural networks. To make clear the characteristics of the asymmetric network with Gabor functions, orthogonality is computed, which shows independent characteristics of the asymmetric network without maximizing optimization of independence in the quadratic model. The orthogonal analyses for the independence of the asymmetric networks are applied to the V1 and MT neural networks to generate independent subspaces by using selective Gabor functions.