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Creative information processing systems based on non-standard computation toward brain potentiality

基于非标准脑潜能计算的创意信息处理系统

基本信息

批准号：
16500135
负责人：
MATSUI Nobuyuki
金额：
$ 2.3万
依托单位：
University of Hyogo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

In this research, we tried to find the methods of creative thinking based on non-standard computation toward brain potentiality such as perceptual alternation phenomena and investigated the emergence of this creative effect through system applications to these methods. In our study of the creative information processing systems, we have obtained the following main results:1. We found the multiplet as the characteristics of association in discrete states and discrete time Hopfield neural network described by quaternion.2. We confirmed the efficiency of the qubit neural network in the color night vision and data-mining applications.3. We developed the block learning SOM system with high adaptability under non-stationary environments.4. We cleared the efficiency of the multi-stable perception model by multi-layered bi-directional associative memory.5. We derived flocking behaviors of agents from a scheme of reinforcement learning, and also clarified the effect of reinforcement learning using chaotic exploration in the maze world problem.6. We constructed the algorithms on defect-tolerance in the asynchronous cellular automaton and showed that defective cells are detected and isolated by configurations of random flies that move around in cellular space.As a result, we clarified that our creative information processing systems have better performances than conventional ones in processing various problems. We have presented a lot of our fruitful results so that we consider our research aims have been achieved. However, many subjects remain to be explored, for examples, Clifford algebra approach, Bloch representation on qubit neuron state and its application to cellular neural network, etc. These are our future works.

在本研究中，我们试图找到创造性思维的方法，基于非标准计算的大脑潜力，如知觉交替现象，并通过系统应用这些方法，调查这种创造性效应的出现。在创造性信息加工系统的研究中，我们获得了以下主要结果：1。我们发现多重态是离散状态和四元数描述的离散时间Hopfield神经网络的联想特征.验证了量子比特神经网络在彩色夜视和数据挖掘应用中的有效性.开发了在非平稳环境下具有高适应性的分块学习SOM系统.通过多层双向联想记忆，验证了多稳态感知模型的有效性.从强化学习的方案中推导出智能体的群集行为，并阐明了混沌探索强化学习在迷宫世界问题中的作用.我们构造了异步元胞自动机中的缺陷容忍算法，并通过在元胞空间中随机移动的苍蝇结构来检测和隔离缺陷元胞，从而阐明了我们的创新信息处理系统在处理各种问题时具有比传统系统更好的性能.我们展示了大量的成果，我们认为我们的研究目标已经实现。然而，还有许多问题有待于进一步研究，如Clifford代数方法、量子比特神经元态的Bloch表示及其在细胞神经网络中的应用等，这些都是我们未来的工作。