Neural Network Approaches to Visual Motion Perception

作     者：郭爱克 杨先一 

作者机构：Laboratory of Visual Information Processing Institute of Biophysics Academia Sinica Beijing 100101 PRC) 

出 版 物：《Science China Chemistry》 (中国科学（化学英文版）)

年 卷 期：1994年第37卷第2期

页      面：177-189页

核心收录：

学科分类：1002[医学-临床医学] 100204[医学-神经病学] 10[医学] 

基　　金：Project supported by the National Natural Science Foundation of China 

主　　题：Reichardt’s algorithm figure-ground discrimination Kohonen’s algorithm and self organization. 

摘      要：This paper concerns certain difficult problems in image processing and perception: neuro-computation of visual motion information. The first part of this paper deals with the spatial physiological integration by the figure-ground discrimination neural network in the visual system of the fly. We have outlined the fundamental organization and algorithms of this neural network, and mainly concentrated on the results of computer simulations of spatial physiological integration. It has been shown that the gain control mechanism , the nonlinearity of synaptic transmission characteristic , the interaction between the two eyes , and the directional selectivity of the pool cells play decisive roles in the spatial physiological integration. In the second part, we have presented a self-organizing neural network for the perception of visual motion by using a retinotopic array of Reichardt s motion detectors and Kohonen s self-organizing maps. It .has been demonstrated by computer simulations that the network is able to learn to solve the ambiguities given by local motion detection mechanism. The resultant self-organized configuration in the output layer is resembling direction selective columns which first appear in area MT of the primate visual system. It has been explored that the spatio-temporal coherences, mapping, cooperation, competition, and Hebb rule are the basic neural principles for visual motion perception.