Computational Study on the Structure and Learning in the Integrated Neural Networks for Different Sensors

与人工神经网络不同，生物神经网络在感官信息处理中发挥着重要作用。视觉系统中的视网膜神经网络分为对称性结构和非对称性结构。不对称网络由线性路径和非线性路径组成，这种不对称网络在叉尾鱼和猫科动物等人中表现出来。这种非对称网络具有感官感知的特征，其结构的非对称性反映了神经网络的功能。因此，本研究首先分析了非对称神经网络在感觉知觉中的作用。其次，讨论了集成神经网络，以响应和学习来自不同感知网络的不同感觉信息。该综合网络由多个子网络组成，利用多个传感器正确识别目标，“前向网络”接收来自相应传感器的输入。“集成单元”集成了所有的输出。前向网络。“后向网络”接收来自集成单元的输入，并将感官信息重构为其输出。在识别阶段，积分系统通过反复修正前向网络的输入、积分单元的输出和感官信息的置信度来获得正确的输出，以最大化似然函数，该似然函数可以通过贝叶斯方法得到。说明了该系统在综合网络中的能力得到了提高。

Biological neural networks play important roles in the sensory infor- mation processing, which is different from artificial neural networks. The retinal neural networks in the visual system, are classified into symmetrical and asymmetrical structures. The asymmetrical networks, which are shown in catfish and cat et al., consist of the linear pathway and the nonlinear pathway. This asymmetrical networks have characteristic properties in the sensory perceptions.The asymmetric structures reflect the function of the neural networks. Thus, this study, first analyzes what is the functions of the asymmetric neural networks in the sensory perception. Second, the integrated neural netwoks are discussed to respond and learn different sensory informations from different perceptual networks. The integrated network consist of several sub-netwoks to recognize objects correctly by using several sensors."Forward Network" receives inputs from corresponding sensor. "Integrating Unit" integrates outputs of all. Forward Networks."Backward Network" receives inputs from Integrating Unit and recostructs the sensory information as its outputs. In the recognition phase, the integ- rated system gets a correct output by modifying the inputs of Forward Networks, the output of Integrating Unit and the confidence of the sensory information repeatedly to maximize a likelihood function , which can be derived by a Bayesian method. It was clarified the system has the improved ability in the integrated networks.