Emergence of simple-cell receptive field properties by learning a sparse code for natural images

@article{Olshausen1996EmergenceOS,
  title={Emergence of simple-cell receptive field properties by learning a sparse code for natural images},
  author={B. Olshausen and D. Field},
  journal={Nature},
  year={1996},
  volume={381},
  pages={607-609}
}
  • B. Olshausen, D. Field
  • Published 1996
  • Computer Science, Medicine
  • Nature
  • THE receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented1–4 and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms5,6. [...] Key Result The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.Expand Abstract
    Hierarchical Learning from Natural Images
    Localized Receptive Fields May Mediate Transformation-Invariant Recognition in the Visual Cortex
    6
    A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images
    284
    Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2
    43