• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computational Neuroscience >Improved dimensionalh-reduced visual cortical network using stochastic noise modeling
【24h】

Improved dimensionalh-reduced visual cortical network using stochastic noise modeling

机译：使用随机噪声建模改进降维视觉皮层网络

获取原文
获取原文并翻译 | 示例
       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we extend our framework for constructing low-dimensional dynamical system models of large-scale neuronal networks of mammalian primary visual cortex. Our dimensional reduction procedure consists of performing a suitable linear change of variables and then systematically truncating the new set of equations. The extended framework includes modeling the effect of neglected modes as a stochastic process. By parametrizing and including stochasticity in one of two ways we show that we can improve the systems-level characterization of our dimensionally reduced neuronal network model. We examined orientation selectivity maps calculated from the firing rate distribution of large-scale simulations and stochastic dimensionally reduced models and found that by using stochastic processes to model the neglected modes, we were able to better reproduce the mean and variance of firing rates in the original large-scale simu lations while still accurately predicting the orientation preference distribution.
机译：在本文中，我们扩展了我们的框架，以构建哺乳动物初级视觉皮层的大规模神经元网络的低维动力学系统模型。我们的降维程序包括执行变量的适当线性变化，然后系统地截断新的方程组。扩展框架包括将被忽略的模式的影响建模为随机过程。通过以两种方式之一进行参数化和包括随机性，我们表明我们可以改善尺寸缩小的神经元网络模型的系统级特征。我们检查了由大型模拟的发射速率分布和随机尺寸缩减模型计算出的定向选择性图，发现通过使用随机过程对被忽略的模式进行建模，我们能够更好地重现原始发射速率的均值和方差大规模仿真，同时仍能准确预测方向偏好分布。

著录项

  • 来源
    《Journal of Computational Neuroscience》 |2012年第2期|p.367-376|共10页
  • 作者

    Louis Tao; Jeremy Praissman; Andrew T. Sornborger;

  • 作者单位

    Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetics Engineering, College of Life Sciences, Peking University,Number 5 Summer Palace Road,Beijing 100871, People's Republic of China,Center for Applied Mathematics and Statistics,New Jersey Institute of Technology,323 Martin Luther King, Jr. Blvd.,Newark, NJ 07102, USA;

    Department of Biochemistry and Molecular Biology,University of Georgia,B122 Life Sciences Building, Green Street,Athens, GA 30602, USA;

    Department of Mathematics and Faculty of Engineering,University of Georgia,500 D.W. Brooks Drive,Athens, GA 30602, USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    primary visual cortex; low dimensional characterization; stochastic process; autoregressive process;

    机译：初级视觉皮层低尺寸特征;随机过程自回归过程;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号