Retinotopic Sparse Representation of Natural Images
Independent component analysis and sparse coding have provided a functional explanations of simple cells in primary visual cortex (VI). The learned components (corresponding to the responses of neurons) of these models are randomly scattered and have no particular order. In practice, however, the arrangement of neurons in VI are ordered in a very specific manner. In this paper, we propose a sparse coding of natural images under a retinotopic map constraint. We investigate the spatial specifically connections between retinal input and vl neurons. Some simulations on natural images demonstrate that the proposed model can learn a retinotopic sparse representation efficiently.
Libo Ma
State Key Laboratory of Neurobiology, Chinese Academy of Sciences, Institute of Neuroscience,Shanghai Institutes of Biological Sciences, Shanghai 200031, China
国际会议
The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)
杭州
英文
435-440
2009-11-15（万方平台首次上网日期，不代表论文的发表时间）