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Analysis of Attention in Human Perceptual Alternation Phenomena and Its Quantum Neuro Computing Model

人类知觉交替现象中的注意力分析及其量子神经计算模型

基本信息

批准号：
10680391
负责人：
MATSUI Nobuyuki
金额：
$ 1.92万
依托单位：
HIMEJI INSTITUTE OF TECHNOLOGY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

In this research, we dealt with the attention phenomena of human perceptual-alternation and its quantum neuro computing model. In order to construct a new framework for describing the cohesiveness of the distribution and synthesis inherent in a neural network for this perceptual-alternation, a neural state is described quantum dynamically and a qubitlike neural network corresponding to the quantum circuit of quantum computation is studied. As a result, we construct the feed-forward neural network with our qubit neuron model, and discuss its information processing abilities with conventional neuron models through the learning simulations of the 4bit parity check problem and the general function identification problem, and we obtain the performance figure of learning. From these figures, we find that our qubit neuron model is more excellent than the conventional one. From our main results, we conclude that our qubit neuron model is an effective method of enhancing the performance of neural networks. We also construct a neural network model for perceptual alternation of ambiguous figures by using coupled oscillatory neurons or chaos neurons that can describe the timing of neuronal firing. From our results of simulations with this model, it is found that the model is able to represent the perceptual alternation in terms of the phase activities and synchronizations of neuronal phases. It is also shown that the distribution of perceptual duration fits Gamma distribution well as it is reported in psychophysical experiments. There, however, remains a further investigation of how to connect our qubit neuron with the coupled oscillatory perceptual-alternation model.

在本研究中，我们处理了人类感知交替的注意现象及其量子神经计算模型。为了构建一个新的框架来描述神经网络中固有的分布和综合的内聚性，本文对神经网络状态进行了量子动态描述，并研究了与量子计算的量子电路相对应的类量子比特神经网络。因此，我们利用我们的量子比特神经元模型构建了前馈神经网络，并通过对4位奇偶校验问题和一般函数识别问题的学习仿真，讨论了其与传统神经元模型的信息处理能力，得到了学习的性能图。从这些图中，我们发现我们的量子比特神经元模型比传统的模型更优秀。从我们的主要结果中，我们得出结论，我们的量子比特神经元模型是提高神经网络性能的有效方法。我们还利用耦合振荡神经元或混沌神经元来描述神经元放电的时间，构建了模糊图形感知交替的神经网络模型。通过对该模型的模拟，我们发现该模型能够从相活动和神经元相同步的角度来表征感知交替。知觉持续时间的分布与Gamma分布吻合良好，与心理物理实验结果一致。然而，如何将量子比特神经元与耦合振荡感知交替模型连接起来，仍有待进一步研究。