Neural Computations by Asymmetric Networks with Nonlinearities
Neural Computations by Asymmetric Networks with Nonlinearities
复制标题
DOI：
10.1007/978-3-540-71629-7_5
复制
发表时间：
2007-04
期刊：
影响因子：
--
通讯作者：
N. Ishii;Toshinori Deguchi;M. Kawaguchi
中科院分区：
文献类型：
--
作者：
N. Ishii;Toshinori Deguchi;M. Kawaguchi
非线性是生物视觉神经网络中的一个重要因素。在视觉网络的突出特征中，运动检测是在视觉皮层中进行的。用于运动检测的视觉皮层由初级视觉皮层（V1）和中颞叶区（MT）两层网络组成，其中非线性功能在视觉系统中起着重要作用。这些网络将被分解成具有非线性的非对称子网络。本文讨论了具有非线性的非对称神经网络的基本特征，用于这些神经网络中变化刺激的检测或运动检测。通过对非对称网络的优化，推导出运动检测方程。阐明了奇偶非线性组合非对称网络在刺激变化检测和刺激运动方向检测方面具有能力，而对称网络则需要时间记忆才能具有相同的能力。这些事实应用于两个分层网络，V1和MT。
Nonlinearity is an important factor in the biological visual neural networks. Among prominent features of the visual networks, movement detections are carried out in the visual cortex. The visual cortex for the movement detection, consist of two layered networks, called the primary visual cortex (V1),followed by the middle temporal area (MT), in which nonlinear functions will play important roles in the visual systems. These networks will be decomposed to asymmetric sub-networks with nonlinearities. In this paper, the fundamental characteristics in asymmetric neural networks with nonlinearities, are discussed for the detection of the changing stimulus or the movement detection in these neural networks. By the optimization of the asymmetric networks, movement detection equations are derived. Then, it was clarified that the even-odd nonlinearity combined asymmetric networks, has the ability in the stimulus change detection and the direction of movement or stimulus, while symmetric networks need the time memory to have the same ability. These facts are applied to two layered networks, V1 and MT.