Understanding of Human Perceptual-Alternation Phenomena and its Neural Network Model

人类知觉交替现象及其神经网络模型的理解

• 批准号: 07680410
• 负责人: MATSUI Nobuyuki
• 金额: $ 1.41万
• 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: 1995
• 资助国家: 日本
• 起止时间: 1995 至 1996
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680410/
• 关键词: ambiguous figure; visual perception; perceptual-alternation; neural network information processing; Gamma distribution; Chaos; neuron firing-rate; perceptual duration time

In this research, we dealt with the phenomena of human perceptual-alternation at the time one observes the ambiguous figures such as the "Rubin's face and vase". Using a primary model based on the multilayr neural network proposed by M.Riani et al. to represent the information processing in the higher level of brain for this phenomenon, we examined the input pattern-dependence of the firing-rate for the perceptual-alternation. In our approach, this alternation is interpreted as that of the rate of firing neurons for the pattern perceived. Experimentally, we could obtain the Gamma distribution of the perceptual duration of the alternating interpretations. We then regards this Gamma distribution as the universal character of the perceptual alternation. From our result of computer simulation we could show the efficiency of the model. In particular, we have shown the chaotic behavior of visual inputs corresponding to attention eye movement makes it possible to obtain clearer the Gamma distributions than those of the random and/or 1/f visual behavior. We also find some other efficiencies of the chaotic inputs, for example, such as the input pattern-dependence of the rate of firing neurons and discuss an other model, for example, the chaotic neural network.

在这项研究中，我们处理了人类在观察诸如“鲁宾的脸和花瓶”等模糊图形时的感知变化现象。使用M.Riani等人提出的基于多层神经网络的初级模型来表示大脑高层对这一现象的信息处理，我们检查了感知交替中触发率的输入模式依赖性。在我们的方法中，这种交替被解释为神经元对所感知的模式的放电速率。实验上，我们可以得到交替解释的感知持续时间的伽玛分布。然后我们把这个伽玛分布看作是感知交替的普遍特征。计算机仿真结果表明了该模型的有效性。特别是，我们已经表明，与注意力眼球运动相对应的视觉输入的混沌行为使得获得比随机和/或1/f视觉行为更清晰的Gamma分布成为可能。我们还发现了混沌输入的其他一些效率，例如，神经元放电速率的输入模式依赖，并讨论了另一个模型，例如，混沌神经网络。

项目成果

Nobuyuki Matsui: "The Efficiency of the Chaotic Visual Behaviour in Modeling the Human Perceptual-Alternation by Artificial Neural Network" Proceedings of 1995 IEEE International Conference on Neural Networks. Vol.4. 1991-1994 (1995)

Nobuyuki Matsui：“通过人工神经网络模拟人类感知交替的混沌视觉行为的效率”1995 年 IEEE 国际神经网络会议论文集。

長尾夏樹,西村治彦,松井伸之: "カオス・ニューラルネットワークによる認知ゆらぎの検討" 情報処理学会第53回全国大会講演論文集. 分冊2. 207-208 (1996)

Natsuki Nagao、Haruhiko Nishimura、Nobuyuki Matsui：“利用混沌神经网络研究认知波动”第 53 届日本信息处理学会全国会议论文集第 2 卷 207-208（1996 年）。

Nobuyuki Matsui, Takayuki Mori: "The Efficiency of the Chaotic Visual Behavior in Modeling the Human Perceptional-Alternation by Artificial Neural Network" Proceedings of the IEEE International Conference on Neural Networks. Vol.4. 1991-1994 (1995)

Nobuyuki Matsui、Takayuki Mori：“通过人工神经网络模拟人类感知交替的混沌视觉行为的效率”IEEE 国际神经网络会议论文集。

Natsuki Nagao, Haruhiko Nishimura, Nobuyuki Matsui: "A perception model of ambiguous patterns based on the chaotic neural networks" Proceedings of the 53th Annual Conference of Infomation processing of Japan. Vol.2. 207-208 (1996)

Natsuki Nagao、Haruhiko Nishimura、Nobuyuki Matsui：“基于混沌神经网络的模糊模式感知模型”第 53 届日本信息处理年会论文集。

松井伸之,森 栄之: "交代想起のANN表現におけるカオス挙動入力の効果" 1997年電子情報通信学会総合大会講演論文集. 情報・システム1. D-2-4-23 (1997)

Nobuyuki Matsui、Hideyuki Mori：“交替回忆的 ANN 表示中混沌行为输入的影响”1997 年 IEICE 信息与系统大会记录 1. D-2-4-23 (1997)。