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Showing 1–5 of 5 results
Advanced filters: Author: Thomas Naselaris Clear advanced filters

  • Whether or not deep neural networks require hierarchical representations to predict brain activity is not known. Here, the authors show that a multi-branch deep neural network can predict neural activity independently in visual areas in the absence of hierarchical representations.

    • Ghislain St-Yves
    • Emily J. Allen
    • Thomas Naselaris
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-16

  • Prediction of high-level visual representations in the human brain may benefit from multimodal sources in network training and the incorporation of complex datasets. Wang and colleagues show that language pretraining and a large, diverse dataset together build better models of higher-level visual cortex compared to earlier models.

    • Aria Y. Wang
    • Kendrick Kay
    • Leila Wehbe
    Research
    Nature Machine Intelligence
    Volume: 5, P: 1415-1426

  • How hippocampal area CA1 and the entorhinal cortex preserve temporal memories over long timescales is not known. Here, the authors show using 7T fMRI, that temporal context memory for scene images is predicted by the re-expression of CA1 and entorhinal cortex activity patterns during subsequent encounters over a period of months.

    • Futing Zou
    • Guo Wanjia
    • Sarah DuBrow
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-12

  • Recent functional magnetic resonance imaging (fMRI) studies have shown that it is possible to deduce simple features in the visual scene or to which category it belongs. A decoding method based on quantitative receptive field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas has now been developed. These models make it possible to identify, out of a large set of completely novel complex images, which specific image was seen by an observer.

    • Kendrick N. Kay
    • Thomas Naselaris
    • Jack L. Gallant
    Research
    Nature
    Volume: 452, P: 352-355

  • The authors measured high-resolution fMRI activity from eight individuals who saw and memorized thousands of annotated natural images over 1 year. This massive dataset enables new paths of inquiry in cognitive neuroscience and artificial intelligence.

    • Emily J. Allen
    • Ghislain St-Yves
    • Kendrick Kay
    Research
    Nature Neuroscience
    Volume: 25, P: 116-126