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Showing 1–3 of 3 results
Advanced filters: Author: Daniel Yamins Clear advanced filters

  • This study shows that the amount of linearly decodable information for categorical-orthogonal object tasks (for example, position, scale, pose, perimeter and aspect ratio) increases up the ventral visual hierarchy, ultimately matching human levels in inferior temporal cortex. It also provides a computational model that explains how this pattern of information arises.

    • Ha Hong
    • Daniel L K Yamins
    • James J DiCarlo
    Research
    Nature Neuroscience
    Volume: 19, P: 613-622

  • Recent computational neuroscience developments have used deep neural networks to model neural responses in higher visual areas. This Perspective describes key algorithmic underpinnings in computer vision and artificial intelligence that have contributed to this progress and outlines how deep networks could drive future improvements in understanding sensory cortical processing.

    • Daniel L K Yamins
    • James J DiCarlo
    Reviews
    Nature Neuroscience
    Volume: 19, P: 356-365

  • A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience.

    • Blake A. Richards
    • Timothy P. Lillicrap
    • Konrad P. Kording
    Reviews
    Nature Neuroscience
    Volume: 22, P: 1761-1770