中文摘要：

在稀少的编码的 L0 标准限制有的优点生产象生理学数据的接受的地形状的一样的差异，但是对分析困难。理解怎么仍然是一个挑战性的问题 V1 简单房间的多样的形状接受的地出现在视觉外皮。这份报纸论述一个生物学上嘴巧的学习算法，命名基于 Hebbian 的吝啬的移动，为这个问题。L0 标准限制优化基础功能而非他们的系数的数字。我们报导优化过程是实质上，基础的选择的 01 编程工作。由假设基础功能独立地从一个基础集合被选择，我们发现包含特殊基础功能的输入样品的空间分发在这个基础让星形状和山峰工作。因此，为与 L0 标准的稀少的编码学习基础函数能作为基础函数是内核密度估计的模式的模式察觉被解释。我们采用吝啬的移动检测模式并且证明吝啬的移动的更新的规则是 Hebbian。模拟结果在生产象 Gabor 一样和像斑点的基础功能表明建议算法的坚韧性。

英文摘要：

The L0-norm constraint in sparse coding has the advantage of producing the same diversity of receptive field shapes as physiology data, but is difficult for analysis. It remains a challenging issue to understand how the diverse shapes of V1 simple cell receptive fields emerge in visual cortex. This paper presents a biologically plausible learning algorithm, named Hebbian-based mean shift, for this prob- lem. The L0-norm constraint optimizes the number of basis functions rather than their coefficients. We report that the optimization procedure is essentially a 0-1 programming of the selection of basis functions. By assuming that the basis functions are independently selected from a basis set, we find the spatial distribution of input samples containing a special basis function has a star shape and peaks at this basis func- tion. Thus, learning the basis functions for sparse coding with the L0-norm can be interpreted as mode detection where the basis functions are the modes of the kernel density estimate. We employ mean shift to detect modes and prove that the updating rule for the mean shift is Hebbian. The simulation results demonstrate the robustness of the proposed algorithm in producing both Gabor-like and blob-like basis functions.