• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

如何分析脉冲耦合神经网络的神经动力学特性?交叉皮质模型的理论分析与案例研究

How to Analyze the Neurodynamic Characteristics of Pulse-Coupled Neural Networks? A Theoretical Analysis and Case Study of Intersecting Cortical Model.

作者信息

Jin Xin, Zhou Dongming, Jiang Qian, Chu Xing, Yao Shaowen, Li Keqin, Zhou Wei

出版信息

IEEE Trans Cybern. 2022 Jul;52(7):6354-6368. doi: 10.1109/TCYB.2020.3043233. Epub 2022 Jul 4.

DOI:10.1109/TCYB.2020.3043233
PMID:33449895
Abstract

The intersecting cortical model (ICM), initially designed for image processing, is a special case of the biologically inspired pulse-coupled neural-network (PCNN) models. Although the ICM has been widely used, few studies concern the internal activities and firing conditions of the neuron, which may lead to an invalid model in the application. Furthermore, the lack of theoretical analysis has led to inappropriate parameter settings and consequent limitations on ICM applications. To address this deficiency, we first study the continuous firing condition of ICM neurons to determine the restrictions that exist between network parameters and the input signal. Second, we investigate the neuron pulse period to understand the neural firing mechanism. Third, we derive the relationship between the continuous firing condition and the neural pulse period, and the relationship can prove the validity of the continuous firing condition and the neural pulse period as well. A solid understanding of the neural firing mechanism is helpful in setting appropriate parameters and in providing a theoretical basis for widespread applications to use the ICM model effectively. Extensive experiments of numerical tests with a common image reveal the rationality of our theoretical results.

摘要

交叉皮层模型(ICM)最初是为图像处理而设计的,是受生物启发的脉冲耦合神经网络(PCNN)模型的一个特例。尽管ICM已被广泛使用,但很少有研究关注神经元的内部活动和放电条件,这可能导致该模型在应用中无效。此外,缺乏理论分析导致参数设置不当,从而限制了ICM的应用。为了解决这一缺陷,我们首先研究ICM神经元的持续放电条件,以确定网络参数与输入信号之间存在的限制。其次,我们研究神经元脉冲周期以理解神经放电机制。第三,我们推导持续放电条件与神经脉冲周期之间的关系,并且该关系也能证明持续放电条件和神经脉冲周期的有效性。深入理解神经放电机制有助于设置合适的参数,并为有效使用ICM模型进行广泛应用提供理论基础。对一幅普通图像进行的大量数值测试实验揭示了我们理论结果的合理性。

相似文献

1
How to Analyze the Neurodynamic Characteristics of Pulse-Coupled Neural Networks? A Theoretical Analysis and Case Study of Intersecting Cortical Model.如何分析脉冲耦合神经网络的神经动力学特性?交叉皮质模型的理论分析与案例研究
IEEE Trans Cybern. 2022 Jul;52(7):6354-6368. doi: 10.1109/TCYB.2020.3043233. Epub 2022 Jul 4.
2
PCNN Mechanism and its Parameter Settings.PCNN 机制及其参数设置。
IEEE Trans Neural Netw Learn Syst. 2020 Feb;31(2):488-501. doi: 10.1109/TNNLS.2019.2905113. Epub 2019 Apr 11.
3
Medical image fusion using enhanced cross-visual cortex model based on artificial selection and impulse-coupled neural network.基于人工选择和脉冲耦合神经网络的增强型跨视觉皮层模型的医学图像融合
Comput Methods Programs Biomed. 2023 Feb;229:107304. doi: 10.1016/j.cmpb.2022.107304. Epub 2022 Dec 9.
4
Quantum pulse coupled neural network.量子脉冲耦合神经网络。
Neural Netw. 2022 Aug;152:105-117. doi: 10.1016/j.neunet.2022.04.007. Epub 2022 Apr 18.
5
Implementation of a pulse coupled neural network in FPGA.在现场可编程门阵列中实现脉冲耦合神经网络。
Int J Neural Syst. 2000 Jun;10(3):171-7. doi: 10.1142/S0129065700000156.
6
An Improved Pulse-Coupled Neural Network Model for Pansharpening.用于多光谱锐化的改进型脉冲耦合神经网络模型。
Sensors (Basel). 2020 May 12;20(10):2764. doi: 10.3390/s20102764.
7
Monostable multivibrators as novel artificial neurons.单稳态多谐振荡器作为新型人工神经元。
Neural Netw. 2018 Dec;108:224-239. doi: 10.1016/j.neunet.2018.08.014. Epub 2018 Aug 23.
8
An optimized pulse coupled neural network image de-noising method for a field-programmable gate array based polarization camera.基于现场可编程门阵列的偏振相机的优化脉冲耦合神经网络图像去噪方法。
Rev Sci Instrum. 2021 Nov 1;92(11):113703. doi: 10.1063/5.0056983.
9
Stability of two cluster solutions in pulse coupled networks of neural oscillators.神经振荡器脉冲耦合网络中两种聚类解的稳定性
J Comput Neurosci. 2011 Apr;30(2):427-45. doi: 10.1007/s10827-010-0268-x. Epub 2010 Aug 20.
10
NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency.基于 NSCT 的多模态医学图像融合方法,使用脉冲耦合神经网络和改进的空间频率。
Med Biol Eng Comput. 2012 Oct;50(10):1105-14. doi: 10.1007/s11517-012-0943-3. Epub 2012 Jul 24.

引用本文的文献

1
Architectural planning robot driven by unsupervised learning for space optimization.基于无监督学习驱动的用于空间优化的建筑规划机器人。
Front Neurorobot. 2025 Jan 3;18:1517960. doi: 10.3389/fnbot.2024.1517960. eCollection 2024.
2
Cross-attention swin-transformer for detailed segmentation of ancient architectural color patterns.用于古建筑色彩图案精细分割的交叉注意力窗口变压器
Front Neurorobot. 2024 Dec 13;18:1513488. doi: 10.3389/fnbot.2024.1513488. eCollection 2024.
3
Multi-Exposure Image Fusion Algorithm Based on Improved Weight Function.
基于改进权重函数的多曝光图像融合算法
Front Neurorobot. 2022 Mar 8;16:846580. doi: 10.3389/fnbot.2022.846580. eCollection 2022.
4
Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image.人工智能 脉冲耦合神经网络算法在超声影像下 严重脓毒症合并急性肾损伤的诊治中的应用
J Healthc Eng. 2021 Jul 20;2021:6761364. doi: 10.1155/2021/6761364. eCollection 2021.