A neural model of the binding in the human brain and its application to pattern recognition

人类大脑中的信息绑定对于解释和理解人类如何能够如此容易地识别外部世界变得越来越重要。此外，我们期望将绑定机制应用到识别器中，可以获得更好的识别性能。在这项研究中，我们开发了神经网络，可以很好地解释人类大脑中的信息绑定。基于以往的神经网络模型很难描述锋电位的同步化或同步化，本文提出了两种相量神经网络模型。一种是相位速率振荡神经元模型，其中相位表示正弦相位。另一种是协方差神经元模型，其中两个神经元之间的相位差的余弦表示协方差系数。在这两个模型中，我们实现了Hebb学习和Boltzmann学习规则，以获得有效的学习。从计算机模拟结果，我们表明，所提出的学习规则的工作效率比有序模型。应用这些模型所获得的知识，我们提出了脑电波识别器，表现出比以前更高的性能。我们还发展了学习中的泛化理论。

The information binding in human brain is getting important to explain and to understand how human being can recognize the outer world so easily. Besides this, we expect that applying the binding mechanism to recognizer, we can get much better recognition performance. In this research we developed the neural networks that can well explain the information binding in the human brain. Based on the fact that the previous neural models are difficult to represent the spike synchronization or asynchronization, we developed two phasor neural models. One is the phase-rate oscillating neural model, in which the phase represents the sinusoidal phase. The other is covariance neural model, in which the cosine of the phase difference between two neurons represents the covariance coefficient. In both model we implemented the Hebb learning and Boltzmann learning rules to get the efficient learning. From the computer simulation results, we showed that the proposed learning rules work much more efficient than ordinal models. Applying the knowledge obtained from these models, we proposed the brain wave recognizer which shows much higher performance than previous. We also developed the theory of generalization in learning.