首页> 外文会议>Computational Neuroscience Meeting (CNS'01) Jul, 2001 Monterey, California >Formation of pinwheels of preferred orientation by learning sparse neural representations of natural images
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Formation of pinwheels of preferred orientation by learning sparse neural representations of natural images

机译:通过学习自然图像的稀疏神经表示形成首选方向的风车

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We devise self-organizing model of the striate cortex that learns orientation maps by sparse coding of natural images. The model assumes the existence of oriented receptive fields and of retinotopic mapping. We demonstrate that learning sparse representations of natural images leads to the formation of spatially periodic orientation maps. If and only if the sparseness of the representation is sufficiently high, these orientation maps reproduce different critical parameters of experimentally measured maps in the striate cortex. We conclude the functional topology of the visual cortex that may be tailored to optimize the encoding of natural stimuli with minimal redundancy of the underlying representation.
机译:我们设计了条纹皮质的自组织模型,该模型通过自然图像的稀疏编码来学习方向图。该模型假设存在定向的感受野和视网膜定位。我们证明学习自然图像的稀疏表示会导致形成空间周期性的方向图。当且仅当表示的稀疏度足够高时,这些定向图才能在条纹皮质中重现实验测量的图的不同关键参数。我们得出结论,可视皮层的功能拓扑可以进行调整,以优化自然刺激的编码,并减少基础表示的冗余。

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